


"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


UpperCamelCase						:				Tuple             =    "docs/source/en/_toctree.yml"







def  __snake_case       (   UpperCamelCase__	)      ->		List[str]:







		"""simple docstring"""





		A					       = defaultdict(UpperCamelCase__	)
		A					       = []
		A					       = []
		for doc in doc_list:
				if "local" in doc:
						counts[doc["local"]] += 1

				if doc["title"].lower() == "overview":
						overview_doc.append({'local': doc['local'], 'title': doc['title']}	)
				else:
						new_doc_list.append(UpperCamelCase__	)

		A					       = new_doc_list
		A					       = [key for key, value in counts.items() if value > 1]

		A					       = []
		for duplicate_key in duplicates:
				A					       = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}	)
				if len(UpperCamelCase__	) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]	)
		A					       = sorted(UpperCamelCase__ ,			key=lambda UpperCamelCase__	: s["title"].lower()	)

		# "overview" gets special treatment and is always first
		if len(UpperCamelCase__	) > 1:
				raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'	)

		overview_doc.extend(UpperCamelCase__	)

		# Sort
		return overview_doc







def  __snake_case       (   UpperCamelCase__=False	)      ->		Any:







		"""simple docstring"""





		with open(UpperCamelCase__ ,			encoding='utf-8'	) as f:
				A					       = yaml.safe_load(f.read()	)

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

		# Then to the model doc
		A					       = 0
		while api_doc[scheduler_idx]["title"] != "Schedulers":
				scheduler_idx += 1

		A					       = api_doc[scheduler_idx]['sections']
		A					       = clean_doc_toc(UpperCamelCase__	)

		A					       = False
		if new_scheduler_doc != scheduler_doc:
				A					       = True
				if overwrite:
						A					       = new_scheduler_doc

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







def  __snake_case       (   UpperCamelCase__=False	)      ->		List[str]:







		"""simple docstring"""





		with open(UpperCamelCase__ ,			encoding='utf-8'	) as f:
				A					       = yaml.safe_load(f.read()	)

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

		# Then to the model doc
		A					       = 0
		while api_doc[pipeline_idx]["title"] != "Pipelines":
				pipeline_idx += 1

		A					       = False
		A					       = api_doc[pipeline_idx]['sections']
		A					       = []

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						A					       = pipeline_doc['section']
						A					       = clean_doc_toc(UpperCamelCase__	)
						if overwrite:
								A					       = new_sub_pipeline_doc
				new_pipeline_docs.append(UpperCamelCase__	)

		# sort overall pipeline doc
		A					       = clean_doc_toc(UpperCamelCase__	)

		if new_pipeline_docs != pipeline_docs:
				A					       = True
				if overwrite:
						A					       = new_pipeline_docs

		if diff:
				if overwrite:
						A					       = api_doc
						with open(UpperCamelCase__ ,			'w' ,			encoding='utf-8'	) as f:
								f.write(yaml.dump(UpperCamelCase__ ,			allow_unicode=UpperCamelCase__	)	)
				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__":
	UpperCamelCase						:				List[str]             =    argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	UpperCamelCase						:				Tuple             =    parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)









"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


UpperCamelCase						:				Optional[Any]             =      logging.get_logger(__name__)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







						def __init__(							self   :    str			,			**_lowercase   :    Union[str, Any]		):
								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												A					       = deprecated_arg[3:]
												A					       = not kwargs.pop(_lowercase		)
												logger.warning(
												    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
												    f' {positive_arg}={kwargs[positive_arg]}'		)
								A					       = kwargs.pop('tpu_name'			,			self.tpu_name		)
								A					       = kwargs.pop('device_idx'			,			self.device_idx		)
								A					       = kwargs.pop('eager_mode'			,			self.eager_mode		)
								A					       = kwargs.pop('use_xla'			,			self.use_xla		)
								super().__init__(**_lowercase		)

						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Name of TPU"""}							,    )
						lowerCAmelCase       =     field(
						    default=0							,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}							,    )
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    metadata={"""help""": """Benchmark models in eager model."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    }							,    )







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								requires_backends(self			,			['tf']		)
								if self.is_tpu:
										tf.config.experimental_connect_to_cluster(self._setup_tpu		)
										tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

										A					       = tf.distribute.TPUStrategy(self._setup_tpu		)
								else:
										# currently no multi gpu is allowed
										if self.is_gpu:
												# TODO: Currently only single GPU is supported
												tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,			'GPU'		)
												A					       = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'		)
										else:
												tf.config.set_visible_devices([]			,			'GPU'		)  # disable GPU
												A					       = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'		)

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







						@property
						def __a						(							self   :    Dict		):
								return self.n_gpu > 0





