
"""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


_lowercase							      = logging.get_logger(__name__)







@dataclass
class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Optional[int]					=			[
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]


						def __init__(       self   :					int     ,**A_   :					Any					)   ->      Any:
							for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
									A        =      deprecated_arg[3:]
									A        =      not kwargs.pop(A_					)
									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__(**A_					)

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


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      bool:
							requires_backends(self     ,['tf']					)
							return self._setup_tpu is not None


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      "tf.distribute.Strategy":
							requires_backends(self     ,['tf']					)
							return self._setup_strategy


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      str:
							requires_backends(self     ,['tf']					)
							return tf.config.list_physical_devices('GPU'					)


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      int:
							requires_backends(self     ,['tf']					)
							if self.cuda:
								return len(self.gpu_list					)
							return 0




						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      bool:
							return self.n_gpu > 0




'''simple docstring'''






import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
						import os

						from flax.core.frozen_dict import unfreeze
						from flax.traverse_util import flatten_dict

						from transformers import FlaxBertModel

						UpperCamelCase__:   Tuple								=			"0.12"  # assumed parallelism: 8

@require_flax
@is_staging_test
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@classmethod
	def      A	(						cls			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		TOKEN
								HfFolder.save_token(__snake_case		)







	@classmethod
	def      A	(						cls			:     List[str]		)			->		Tuple:
								try:
															delete_repo(token=cls._token		,			repo_id='''test-model-flax'''		)
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,			repo_id='''valid_org/test-model-flax-org'''		)
								except HTTPError:
															pass







	def      A	(						self			:     int		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''test-model-flax'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Tuple 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Union[str, Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

								# Reset repo
								delete_repo(token=self._token		,			repo_id='''test-model-flax'''		)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(__snake_case		,			repo_id='''test-model-flax'''		,			push_to_hub=__snake_case		,			use_auth_token=self._token		)

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       str 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Optional[Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)







	def      A	(						self			:     Optional[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Dict 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       int 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Any 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

								# Reset repo
								delete_repo(token=self._token		,			repo_id='''valid_org/test-model-flax-org'''		)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(
															    __snake_case		,			repo_id='''valid_org/test-model-flax-org'''		,			push_to_hub=__snake_case		,			use_auth_token=self._token		)

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Optional[Any] 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       int 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Any  )      ->   Union[str, Any]:
							UpperCAmelCase :       str 							=		True
							UpperCAmelCase :       int 							=		flatten_dict(modela.params  )
							UpperCAmelCase :       Dict 							=		flatten_dict(modela.params  )
							for key in flat_params_a.keys():
														if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
																					UpperCAmelCase :       Dict 							=		False

							return models_are_equal




@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       int 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Dict 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		,			max_shard_size='''10KB'''		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Any 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     Optional[int]		)			->		str:
								UpperCAmelCase :       Dict 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)







	def      A	(						self			:     Dict		)			->		List[Any]:
								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Dict 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)





