Fix doc errors and typos across the board (#8139)

* Fix doc errors and typos across the board

* Fix a typo

* Fix the CI

* Fix more typos

* Fix CI

* More fixes

* Fix CI

* More fixes

* More fixes

src/transformers/generation_tf_utils.py

@@ -25,14 +25,14 @@ logger = logging.get_logger(__name__)
 
 class TFGenerationMixin:
     """
-    A class contraining all of the functions supporting generation, to be used as a mixin in
-    :class:`~transfomers.TFPreTrainedModel`.
+    A class containing all of the functions supporting generation, to be used as a mixin in
+    :class:`~transformers.TFPreTrainedModel`.
     """
 
     def prepare_inputs_for_generation(self, inputs, **kwargs):
         """
-        Implement in subclasses of :class:`~transfomers.TFPreTrainedModel` for custom behavior to prepare inputs in the
-        generate method.
+        Implement in subclasses of :class:`~transformers.TFPreTrainedModel` for custom behavior to prepare inputs in
+        the generate method.
         """
         return {"inputs": inputs}
 
@@ -216,17 +216,17 @@ class TFGenerationMixin:
         )
 
         if input_ids is not None:
-            batch_size = shape_list(input_ids)[0]  # overriden by the input batch_size
+            batch_size = shape_list(input_ids)[0]  # overridden by the input batch_size
         else:
             batch_size = 1
 
-        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictely positive integer."
+        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
         assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
         assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
         assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
         assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
         assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
-        assert temperature > 0, "`temperature` should be strictely positive."
+        assert temperature > 0, "`temperature` should be strictly positive."
         assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
         assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
         assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
@@ -239,10 +239,10 @@ class TFGenerationMixin:
         assert (eos_token_id is None) or (
             isinstance(eos_token_id, int) and (eos_token_id >= 0)
         ), "`eos_token_id` should be a positive integer."
-        assert length_penalty > 0, "`length_penalty` should be strictely positive."
+        assert length_penalty > 0, "`length_penalty` should be strictly positive."
         assert (
             isinstance(num_return_sequences, int) and num_return_sequences > 0
-        ), "`num_return_sequences` should be a strictely positive integer."
+        ), "`num_return_sequences` should be a strictly positive integer."
         assert (
             bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
         ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
@@ -722,7 +722,7 @@ class TFGenerationMixin:
                     beam_scores[:, None], (batch_size * num_beams, vocab_size)
                 )  # (batch_size * num_beams, vocab_size)
 
-                # re-organize to group the beam together (we are keeping top hypothesis accross beams)
+                # re-organize to group the beam together (we are keeping top hypothesis across beams)
                 next_scores = tf.reshape(
                     next_scores, (batch_size, num_beams * vocab_size)
                 )  # (batch_size, num_beams * vocab_size)
@@ -897,7 +897,7 @@ class TFGenerationMixin:
 
     def adjust_logits_during_generation(self, logits, **kwargs):
         """
-        Implement in subclasses of :class:`~transfomers.PreTrainedModel` for custom behavior to adjust the logits in
+        Implement in subclasses of :class:`~transformers.PreTrainedModel` for custom behavior to adjust the logits in
         the generate method.
         """
         return logits
@@ -978,7 +978,7 @@ def calc_banned_bad_words_ids(prev_input_ids, bad_words_ids):
 
 def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
     """
-    Filter a distribution of logits using top-k and/or nucleus (top-p) filterin
+    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
 
     Args:
         logits: logits distribution shape (batch size, vocabulary size)
@@ -1047,7 +1047,7 @@ def set_tensor_by_indices_to_value(tensor, indices, value):
 
 def sample_without_replacement(logits, num_samples):
     """
-    categorical sampling witouth replacement is currently not implemented the gumbel-max trick will do for now see
+    categorical sampling without replacement is currently not implemented the gumbel-max trick will do for now see
     https://github.com/tensorflow/tensorflow/issues/9260 for more info
     """
     z = -tf.math.log(tf.random.uniform(shape_list(logits), 0, 1))