



import argparse
from collections import defaultdict

import yaml


__A       =						'''docs/source/en/_toctree.yml'''



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       defaultdict(lowerCAmelCase_    )
						for doc in model_doc:
												counts[doc["local"]] += 1
						UpperCAmelCase_=       [key for key, value in counts.items() if value > 1]

						UpperCAmelCase_=       []
						for duplicate_key in duplicates:
												UpperCAmelCase_=       list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}    )
												if len(lowerCAmelCase_    ) > 1:
																		raise ValueError(
																		    F"""{duplicate_key} is present several times in the documentation table of content at """
																		    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
																		    """others."""    )
												# Only add this once
												new_doc.append({"""local""": duplicate_key, """title""": titles[0]}    )

						# Add none duplicate-keys
						new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1]    )

						# Sort
						return sorted(lowerCAmelCase_      ,key=lambda lowerCAmelCase_    : s["title"].lower()    )



def 	__a					(   lowerCAmelCase_       : Optional[int]=False    )				->			Optional[Any]:






						'''simple docstring'''



						with open(lowerCAmelCase_      ,encoding="""utf-8"""    ) as f:
												UpperCAmelCase_=       yaml.safe_load(f.read()    )

						# Get to the API doc
						UpperCAmelCase_=       0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						UpperCAmelCase_=       content[api_idx]["""sections"""]

						# Then to the model doc
						UpperCAmelCase_=       0
						while api_doc[model_idx]["title"] != "Models":
												model_idx += 1

						UpperCAmelCase_=       api_doc[model_idx]["""sections"""]

						UpperCAmelCase_=       [(idx, section) for idx, section in enumerate(lowerCAmelCase_    ) if """sections""" in section]
						UpperCAmelCase_=       False
						for idx, modality_doc in modalities_docs:
												UpperCAmelCase_=       modality_doc["""sections"""]
												UpperCAmelCase_=       clean_model_doc_toc(lowerCAmelCase_    )

												if old_modality_doc != new_modality_doc:
																		UpperCAmelCase_=       True
																		if overwrite:
																								UpperCAmelCase_=       new_modality_doc

						if diff:
												if overwrite:
																		UpperCAmelCase_=       model_doc
																		UpperCAmelCase_=       api_doc
																		with open(lowerCAmelCase_      ,"""w"""      ,encoding="""utf-8"""    ) as f:
																								f.write(yaml.dump(lowerCAmelCase_      ,allow_unicode=lowerCAmelCase_    )    )
												else:
																		raise ValueError(
																		    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""    )


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

	check_model_doc(args.fix_and_overwrite)









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							UpperCAmelCase_=       self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Evaluation"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics
							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)

							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
													# Only the main node write the results by default
													UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

													# Prefix all keys with metric_key_prefix + '_'
													for key in list(metrics.keys()					):
																			if not key.startswith(F"""{metric_key_prefix}_"""					):
																									UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)

													metrics.update(output.metrics					)
							else:
													UpperCAmelCase_=       output.metrics

							if self.args.should_log:
													# Only the main node log the results by default
													self.log(__UpperCAmelCase					)

							if self.args.tpu_metrics_debug or self.args.debug:
													# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
													xm.master_print(met.metrics_report()					)

							UpperCAmelCase_=       self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Prediction"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics

							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)
							if self.post_process_function is None or self.compute_metrics is None:
													return output

							UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  """predict"""					)
							UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()					):
													if not key.startswith(F"""{metric_key_prefix}_"""					):
																			UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)
							metrics.update(output.metrics					)
							return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=__UpperCAmelCase					)




