




"""simple docstring"""
import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
SCREAMING_SNAKE_CASE__   =					"src/transformers"
SCREAMING_SNAKE_CASE__   =					"docs/source/en/tasks"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Tuple   )   ->			Dict:





						"""simple docstring"""






						with open(_lowerCamelCase	,     'r'	,     encoding='utf-8'	,     newline='\n'   ) as f:
												snake_case        =     f.readlines()
						# Find the start prompt.
						snake_case        =     0
						while not lines[start_index].startswith(_lowerCamelCase   ):
												start_index += 1
						start_index += 1

						snake_case        =     start_index
						while not lines[end_index].startswith(_lowerCamelCase   ):
												end_index += 1
						end_index -= 1

						while len(lines[start_index]   ) <= 1:
												start_index += 1
						while len(lines[end_index]   ) <= 1:
												end_index -= 1
						end_index += 1
						return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__   =					direct_transformers_import(TRANSFORMERS_PATH)

SCREAMING_SNAKE_CASE__   =					{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
SCREAMING_SNAKE_CASE__   =					{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     TASK_GUIDE_TO_MODELS[task_guide]
						snake_case        =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCamelCase	,     set()   )
						snake_case        =     {
						    code: name
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if (code in model_maping_names or code in special_model_types)
						}
						return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]   ) + "\n"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Union[str, Any]=False   )   ->			Any:





						"""simple docstring"""






						snake_case        =     _find_text_in_file(
						    filename=os.path.join(_lowerCamelCase	,     _lowerCamelCase   )	,     start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'	,     end_prompt='<!--End of the generated tip-->'	,     )

						snake_case        =     get_model_list_for_task(_lowerCamelCase   )

						if current_list != new_list:
												if overwrite:
																		with open(os.path.join(_lowerCamelCase	,     _lowerCamelCase   )	,     'w'	,     encoding='utf-8'	,     newline='\n'   ) as f:
																								f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
												else:
																		raise ValueError(
																		    f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
																		    ' to fix this.'   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					for task_guide in TASK_GUIDE_TO_MODELS.keys():
										check_model_list_for_task(task_guide, args.fix_and_overwrite)



"""simple docstring"""
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


SCREAMING_SNAKE_CASE__   =					"true"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Dict=8_2	,     _UpperCamelCase    :			int=1_6   )   ->			str:





						"""simple docstring"""






						set_seed(4_2   )
						snake_case        =     RegressionModel()
						snake_case        =     deepcopy(_UpperCamelCase   )
						snake_case        =     RegressionDataset(length=_UpperCamelCase   )
						snake_case        =     DataLoader(_UpperCamelCase	,     batch_size=_UpperCamelCase   )
						model.to(accelerator.device   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return model, ddp_model, dataloader





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'   )
						snake_case        =     load_dataset('glue'	,     'mrpc'	,     split='validation'   )

						def tokenize_function(_UpperCamelCase    :			Optional[Any]   ):
												snake_case        =     tokenizer(examples['sentence1']	,     examples['sentence2']	,     truncation=_UpperCamelCase	,     max_length=_UpperCamelCase   )
												return outputs

						with accelerator.main_process_first():
												snake_case        =     dataset.map(
												    _UpperCamelCase	,     batched=_UpperCamelCase	,     remove_columns=['idx', 'sentence1', 'sentence2']	,     )

						snake_case        =     tokenized_datasets.rename_column('label'	,     'labels'   )

						def collate_fn(_UpperCamelCase    :			Optional[Any]   ):
												if use_longest:
																		return tokenizer.pad(_UpperCamelCase	,     padding='longest'	,     return_tensors='pt'   )
												return tokenizer.pad(_UpperCamelCase	,     padding='max_length'	,     max_length=1_2_8	,     return_tensors='pt'   )

						return DataLoader(_UpperCamelCase	,     shuffle=_UpperCamelCase	,     collate_fn=_UpperCamelCase	,     batch_size=1_6   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     Accelerator(dispatch_batches=_UpperCamelCase	,     split_batches=_UpperCamelCase   )
						snake_case        =     get_dataloader(_UpperCamelCase	,     not dispatch_batches   )
						snake_case        =     AutoModelForSequenceClassification.from_pretrained(
						    'hf-internal-testing/mrpc-bert-base-cased'	,     return_dict=_UpperCamelCase   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     []
						for batch in dataloader:
												snake_case   ,snake_case        =     batch.values()
												with torch.no_grad():
																		snake_case        =     model(_UpperCamelCase   )
																		snake_case   ,snake_case        =     accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						snake_case   ,snake_case        =     [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCamelCase   )
												targs.append(_UpperCamelCase   )
						snake_case   ,snake_case        =     torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
						return logits, targs





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Tuple=8_2	,     _UpperCamelCase    :			Optional[Any]=False	,     _UpperCamelCase    :			int=False	,     _UpperCamelCase    :			List[str]=1_6   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     get_basic_setup(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case   ,snake_case        =     generate_predictions(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						assert (
						    len(_UpperCamelCase   ) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase   )}"""





def 				lowerCAmelCase__      (     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     evaluate.load('glue'	,     'mrpc'   )
						snake_case   ,snake_case        =     get_mrpc_setup(_UpperCamelCase	,     _UpperCamelCase   )
						# First do baseline
						snake_case   ,snake_case   ,snake_case        =     setup['no']
						model.to(_UpperCamelCase   )
						model.eval()
						for batch in dataloader:
												batch.to(_UpperCamelCase   )
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=batch['labels']   )
						snake_case        =     metric.compute()

						# Then do distributed
						snake_case   ,snake_case   ,snake_case        =     setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												snake_case        =     batch['labels']
												snake_case   ,snake_case        =     accelerator.gather_for_metrics((preds, references)   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=_UpperCamelCase   )
						snake_case        =     metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]	,     distributed[key]   ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""





def 				lowerCAmelCase__      (     )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('**Testing gather_for_metrics**'   )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""   )
																								test_mrpc(_UpperCamelCase	,     _UpperCamelCase   )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test torch metrics**'   )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
																		test_torch_metrics(_UpperCamelCase	,     9_9   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'   )
						snake_case        =     Accelerator()
						test_torch_metrics(_UpperCamelCase	,     5_1_2   )
						accelerator.state._reset_state()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()


