fix typos in the code comments and error messages (#36993)

* chore: enhance code comments

* chore: enhance code comments

* chore: enhance code comments

* chore: enhance code comments

* chore: enhance code comments

* chore: enhance code comments

* chore: enhance code comments

src/transformers/models/altclip/modeling_altclip.py

@@ -798,7 +798,7 @@ class AltCLIPAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/aria/modeling_aria.py

@@ -267,7 +267,7 @@ def sequential_experts_gemm(token_states, expert_weights, tokens_per_expert):
     output = torch.zeros(num_tokens, out_features, dtype=token_states.dtype, device=token_states.device)
 
     cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
-    # Insert zero at the begining for offset index's convenience
+    # Insert zero at the beginning for offset index's convenience
     zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
     cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))
 

src/transformers/models/aria/modular_aria.py

@@ -86,7 +86,7 @@ def sequential_experts_gemm(token_states, expert_weights, tokens_per_expert):
     output = torch.zeros(num_tokens, out_features, dtype=token_states.dtype, device=token_states.device)
 
     cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
-    # Insert zero at the begining for offset index's convenience
+    # Insert zero at the beginning for offset index's convenience
     zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
     cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))
 

src/transformers/models/clip/modeling_clip.py

@@ -373,7 +373,7 @@ class CLIPAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/clipseg/modeling_clipseg.py

@@ -341,7 +341,7 @@ class CLIPSegAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/esm/modeling_esmfold.py

@@ -1016,7 +1016,7 @@ class EsmFoldSelfAttention(nn.Module):
         use mask.
 
         Inputs:
-            x: batch of input sequneces (.. x L x C) mask: batch of boolean masks where 1=valid, 0=padding position (..
+            x: batch of input sequences (.. x L x C) mask: batch of boolean masks where 1=valid, 0=padding position (..
             x L_k) bias: batch of scalar pairwise attention biases (.. x Lq x Lk x num_heads)
 
         Outputs:

src/transformers/models/esm/openfold_utils/rigid_utils.py

@@ -989,10 +989,10 @@ class Rigid:
 
     def to_tensor_4x4(self) -> torch.Tensor:
         """
-        Converts a transformation to a homogenous transformation tensor.
+        Converts a transformation to a homogeneous transformation tensor.
 
         Returns:
-            A [*, 4, 4] homogenous transformation tensor
+            A [*, 4, 4] homogeneous transformation tensor
         """
         tensor = self._trans.new_zeros((*self.shape, 4, 4))
         tensor[..., :3, :3] = self._rots.get_rot_mats()
@@ -1003,10 +1003,10 @@ class Rigid:
     @staticmethod
     def from_tensor_4x4(t: torch.Tensor) -> Rigid:
         """
-        Constructs a transformation from a homogenous transformation tensor.
+        Constructs a transformation from a homogeneous transformation tensor.
 
         Args:
-            t: [*, 4, 4] homogenous transformation tensor
+            t: [*, 4, 4] homogeneous transformation tensor
         Returns:
             T object with shape [*]
         """

src/transformers/models/falcon_mamba/configuration_falcon_mamba.py

@@ -80,7 +80,7 @@ class FalconMambaConfig(PretrainedConfig):
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the cache should be used.
         use_mambapy (`bool`, *optional*, defaults to `False`):
-            Determines the fallback strategy during training if the CUDA-based official implementation of FalconMamba is not avaiable. If `True`, the falcon_mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.
+            Determines the fallback strategy during training if the CUDA-based official implementation of FalconMamba is not available. If `True`, the falcon_mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.
         mixer_rms_eps (`float`, *optional*, defaults to 1e-06):
             The RMS norm epsilon value that is used in the Mixer RMS norm for B, C and dt states.
     Example:

src/transformers/models/falcon_mamba/modeling_falcon_mamba.py

@@ -119,7 +119,7 @@ class FalconMambaMixer(nn.Module):
 
         # projection of the input hidden states
         self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=config.use_bias)
-        # selective projection used to make dt, B and C input dependant
+        # selective projection used to make dt, B and C input dependent
         self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
         # time step projection (discretization)
         self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
@@ -768,7 +768,7 @@ class FalconMambaForCausalLM(FalconMambaPreTrainedModel, GenerationMixin):
         attention_mask: Optional[torch.LongTensor] = None,
         **kwargs,
     ):
-        # Overwitten -- uses `cache_params` as opposed to `past_key_values`
+        # Overwritten -- uses `cache_params` as opposed to `past_key_values`
 
         if use_cache:
             # `cache_position` should have been initialized in `generate`

src/transformers/models/git/modeling_git.py

@@ -791,7 +791,7 @@ class GitVisionAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/idefics/vision.py

@@ -237,7 +237,7 @@ class IdeficsVisionAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/kosmos2/modeling_kosmos2.py

@@ -543,7 +543,7 @@ class Kosmos2VisionAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following

src/transformers/models/longt5/modeling_longt5.py

@@ -235,7 +235,7 @@ class LongT5LayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # LongT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/mamba/modeling_mamba.py

@@ -98,7 +98,7 @@ class MambaMixer(nn.Module):
 
         # projection of the input hidden states
         self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=config.use_bias)
-        # selective projection used to make dt, B and C input dependant
+        # selective projection used to make dt, B and C input dependent
         self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
         # time step projection (discretization)
         self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
@@ -708,7 +708,7 @@ class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
         attention_mask: Optional[torch.LongTensor] = None,
         **kwargs,
     ):
-        # Overwitten -- uses `cache_params` as opposed to `past_key_values`
+        # Overwritten -- uses `cache_params` as opposed to `past_key_values`
 
         if use_cache:
             # `cache_position` should have been initialized in `generate`

src/transformers/models/mt5/modeling_mt5.py

@@ -135,7 +135,7 @@ class MT5LayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # MT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/pix2struct/modeling_pix2struct.py

@@ -72,7 +72,7 @@ class Pix2StructLayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/pop2piano/modeling_pop2piano.py

@@ -164,7 +164,7 @@ class Pop2PianoLayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # Pop2Piano uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py

@@ -119,7 +119,7 @@ GIN_TO_CONFIG_MAPPING = {
 
 
 def convert_gin_to_config(gin_file, num_experts):
-    # Convert a google style config to the hugging face fromat
+    # Convert a google style config to the hugging face format
     import regex as re
 
     with open(gin_file, "r") as f:

src/transformers/models/switch_transformers/modeling_switch_transformers.py

@@ -230,7 +230,7 @@ class SwitchTransformersLayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # SwitchTransformers uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 
@@ -297,12 +297,12 @@ class SwitchTransformersSparseMLP(nn.Module):
         expert the corresponding hidden states.
 
         """
-        # Step 1: Get the router_mask from the router as wel as the probabilities
+        # Step 1: Get the router_mask from the router as well as the probabilities
         router_mask, router_probs, router_logits = self.router(hidden_states)
         expert_index = torch.argmax(router_mask, dim=-1)
 
         # The routers introduced might not always map all the tokens, to a router, which means that some hidden states
-        # can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones.
+        # can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the selected ones.
 
         next_states = hidden_states.clone()
 

src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py

@@ -218,7 +218,7 @@ def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_f
         flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"]
 
     flax_model.save_pretrained(flax_dump_folder_path)
-    print("T5X Model was sucessfully converted!")
+    print("T5X Model was successfully converted!")
 
 
 if __name__ == "__main__":

src/transformers/models/t5/modeling_t5.py

@@ -249,7 +249,7 @@ class T5LayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/udop/modeling_udop.py

@@ -524,7 +524,7 @@ class UdopLayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # Udop uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/umt5/modeling_umt5.py

@@ -73,7 +73,7 @@ class UMT5LayerNorm(nn.Module):
 
     def forward(self, hidden_states):
         # UMT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32
 

src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py

@@ -73,7 +73,7 @@ def transform_attention(current: np.ndarray):
         return transform_attention_kernel(current)
 
     else:
-        raise Exception(f"Invalid number of dimesions: {np.ndim(current)}")
+        raise Exception(f"Invalid number of dimensions: {np.ndim(current)}")
 
 
 def transform_attention_bias(current: np.ndarray):

src/transformers/models/x_clip/modeling_x_clip.py

@@ -300,7 +300,7 @@ class XCLIPAttention(nn.Module):
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if output_attentions:
-            # this operation is a bit akward, but it's required to
+            # this operation is a bit awkward, but it's required to
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following