Fixed Majority of the Typos in transformers[en] Documentation (#33350)

* Fixed typo: insted to instead

* Fixed typo: relase to release

* Fixed typo: nighlty to nightly

* Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks

* Fixed typo in comment: quantizd to quantized

* Fixed typo: architecutre to architecture

* Fixed typo: contibution to contribution

* Fixed typo: Presequities to Prerequisites

* Fixed typo: faste to faster

* Fixed typo: extendeding to extending

* Fixed typo: segmetantion_maps to segmentation_maps

* Fixed typo: Alternativelly to Alternatively

* Fixed incorrectly defined variable: output to output_disabled

* Fixed typo in library name: tranformers.onnx to transformers.onnx

* Fixed missing import: import tensorflow as tf

* Fixed incorrectly defined variable: token_tensor to tokens_tensor

* Fixed missing import: import torch

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed typo in function args: numpy.ndarry to numpy.ndarray

* Fixed Inconsistent Library Name: Torchscript to TorchScript

* Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor

* Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments

* Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR

* Fixed Inconsistent Model Name Typo: EART to BERT

* Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow

* Fixed Broken Link for Speech Emotion Classification with Wav2Vec2

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed Punctuation: Two commas

* Fixed Punctuation: No Space between XLM-R and is

* Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method

* Added backticks to display model.fit() in codeblock

* Added backticks to display openai-community/gpt2 in codeblock

* Fixed Minor Typo: will to with

* Fixed Minor Typo: is to are

* Fixed Minor Typo: in to on

* Fixed Minor Typo: inhibits to exhibits

* Fixed Minor Typo: they need to it needs

* Fixed Minor Typo: cast the load the checkpoints To load the checkpoints

* Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM

* Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state

* Added missing verbosity level: fatal

* Fixed Minor Typo: take To takes

* Fixed Minor Typo: heuristic To heuristics

* Fixed Minor Typo: setting To settings

* Fixed Minor Typo: Content To Contents

* Fixed Minor Typo: millions To million

* Fixed Minor Typo: difference To differences

* Fixed Minor Typo: while extract To which extracts

* Fixed Minor Typo: Hereby To Here

* Fixed Minor Typo: addition To additional

* Fixed Minor Typo: supports To supported

* Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark

* Fixed Minor Typo: a To an

* Fixed Minor Typo: a To an

* Fixed Minor Typo: Chain-of-though To Chain-of-thought

docs/source/en/tasks/asr.md

@@ -196,7 +196,7 @@ Now instantiate your `DataCollatorForCTCWithPadding`:
 
 ## Evaluate
 
-Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [word error rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
+Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [word error rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
 
 ```py
 >>> import evaluate

docs/source/en/tasks/audio_classification.md

@@ -164,7 +164,7 @@ To apply the preprocessing function over the entire dataset, use 🤗 Datasets [
 
 ## Evaluate
 
-Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
+Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
 
 ```py
 >>> import evaluate

docs/source/en/tasks/image_text_to_text.md

@@ -204,7 +204,7 @@ for value in generator:
 
 ## Fit models in smaller hardware
 
-VLMs are often large and need to be optimized to fit in smaller hardware. Transformers supports many model quantization libraries, and here we will only show int8 quantization with [Quanto](./quantization/quanto#quanto). int8 quantization offers memory improvements up to 75 percent (if all weights are quantized). However it is no free lunch, since 8-bit is not a CUDA-native precision, the weights are quantized back and forth on the fly, which adds up to latency. 
+VLMs are often large and need to be optimized to fit on smaller hardware. Transformers supports many model quantization libraries, and here we will only show int8 quantization with [Quanto](./quantization/quanto#quanto). int8 quantization offers memory improvements up to 75 percent (if all weights are quantized). However it is no free lunch, since 8-bit is not a CUDA-native precision, the weights are quantized back and forth on the fly, which adds up to latency. 
 
 First, install dependencies.
 

docs/source/en/tasks/image_to_image.md

@@ -36,6 +36,7 @@ We can now initialize the pipeline with a [Swin2SR model](https://huggingface.co
 
 ```python
 from transformers import pipeline
+import torch
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 pipe = pipeline(task="image-to-image", model="caidas/swin2SR-lightweight-x2-64", device=device)

docs/source/en/tasks/monocular_depth_estimation.md

@@ -159,7 +159,7 @@ def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invali
     """Converts a depth map to a color image.
 
     Args:
-        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
+        value (torch.Tensor, numpy.ndarray): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
         vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
         vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
         cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.

docs/source/en/tasks/prompting.md

@@ -290,7 +290,7 @@ Result: Modern tools often used to make gazpacho include
 #### Reasoning
 
 Reasoning is one of the most difficult tasks for LLMs, and achieving good results often requires applying advanced prompting techniques, like 
-[Chain-of-though](#chain-of-thought).
+[Chain-of-thought](#chain-of-thought).
 
 Let's try if we can make a model reason about a simple arithmetics task with a basic prompt: 
 

docs/source/en/tasks/text-to-speech.md

@@ -580,7 +580,7 @@ Load the model from the 🤗 Hub:
 >>> model = SpeechT5ForTextToSpeech.from_pretrained("YOUR_ACCOUNT/speecht5_finetuned_voxpopuli_nl")
 ```
 
-Pick an example from the test dataset obtain a speaker embedding. 
+Pick an example from the test dataset to obtain a speaker embedding. 
 
 ```py 
 >>> example = dataset["test"][304]

docs/source/en/tasks/video_classification.md

@@ -191,7 +191,7 @@ You should probably TRAIN this model on a down-stream task to be able to use it
 
 The warning is telling us we are throwing away some weights (e.g. the weights and bias of the `classifier` layer) and randomly initializing some others (the weights and bias of a new `classifier` layer). This is expected in this case, because we are adding a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do.
 
-**Note** that [this checkpoint](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) leads to better performance on this task as the checkpoint was obtained fine-tuning on a similar downstream task having considerable domain overlap. You can check out [this checkpoint](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) which was obtained by fine-tuning `MCG-NJU/videomae-base-finetuned-kinetics`.  
+**Note** that [this checkpoint](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) leads to better performance on this task as the checkpoint was obtained by fine-tuning on a similar downstream task having considerable domain overlap. You can check out [this checkpoint](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) which was obtained by fine-tuning `MCG-NJU/videomae-base-finetuned-kinetics`.  
 
 ## Prepare the datasets for training