



'''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__:   int								=			"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			:     Union[str, Any]		)			->		Dict:


								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			:     List[str]		)			->		Optional[Any]:
								UpperCAmelCase :       List[Any] 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       List[str] 							=		FlaxBertModel(_snake_case		)
								model.push_to_hub('''test-model-flax'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Optional[int] 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

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

								for key in base_params.keys():
															UpperCAmelCase :       Dict 							=		(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 :       int 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       Tuple 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Dict 							=		flatten_dict(unfreeze(new_model.params		)		)

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







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

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

								UpperCAmelCase :       Union[str, Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Union[str, Any] 							=		flatten_dict(unfreeze(new_model.params		)		)

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


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict  )      ->   int:
							UpperCAmelCase :       Any 							=		True
							UpperCAmelCase :       Tuple 							=		flatten_dict(modela.params  )
							UpperCAmelCase :       Optional[Any] 							=		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 :       Tuple 							=		False

							return models_are_equal




@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






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

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

															with self.assertRaises(_snake_case		):
																						UpperCAmelCase :       List[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			:     int		)			->		str:
								UpperCAmelCase :       List[str] 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       int 							=		FlaxBertModel(_snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		'''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 :       Optional[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			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(_snake_case		):
															UpperCAmelCase :       Optional[int] 							=		FlaxBertModel.from_pretrained(_snake_case		)

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

								self.assertIsNotNone(_snake_case		)







	def      A	(						self			:     Optional[int]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		'''bert'''
								UpperCAmelCase :       List[str] 							=		'''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		)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






