
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

						__a  			=					'0.12'  # assumed parallelism: 8
@require_flax
@is_staging_test
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@classmethod
	def 						_lowercase  (       cls  :			Any       )       ->     Optional[Any]:
					lowercase_       =					TOKEN
					HfFolder.save_token(SCREAMING_SNAKE_CASE_       )
	@classmethod
	def 						_lowercase  (       cls  :			int       )       ->     str:
					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 						_lowercase  (       self  :			List[Any]       )       ->     str:
					lowercase_       =					BertConfig(
					    vocab_size=9_9       ,							hidden_size=3_2       ,							num_hidden_layers=5       ,							num_attention_heads=4       ,							intermediate_size=3_7       )
					lowercase_       =					FlaxBertModel(SCREAMING_SNAKE_CASE_       )
					model.push_to_hub('''test-model-flax'''       ,							use_auth_token=self._token       )

					lowercase_       =					FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax'''       )

					lowercase_       =					flatten_dict(unfreeze(model.params       )       )
					lowercase_       =					flatten_dict(unfreeze(new_model.params       )       )

					for key in base_params.keys():
									lowercase_       =					(base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(SCREAMING_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(SCREAMING_SNAKE_CASE_       ,							repo_id='''test-model-flax'''       ,							push_to_hub=SCREAMING_SNAKE_CASE_       ,							use_auth_token=self._token       )

					lowercase_       =					FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax'''       )

					lowercase_       =					flatten_dict(unfreeze(model.params       )       )
					lowercase_       =					flatten_dict(unfreeze(new_model.params       )       )

					for key in base_params.keys():
									lowercase_       =					(base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(SCREAMING_SNAKE_CASE_       ,							1e-3       ,							msg=f'''{key} not identical'''       )




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[Any]:
					lowercase_       =					BertConfig(
					    vocab_size=9_9       ,							hidden_size=3_2       ,							num_hidden_layers=5       ,							num_attention_heads=4       ,							intermediate_size=3_7       )
					lowercase_       =					FlaxBertModel(SCREAMING_SNAKE_CASE_       )
					model.push_to_hub('''valid_org/test-model-flax-org'''       ,							use_auth_token=self._token       )

					lowercase_       =					FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''       )

					lowercase_       =					flatten_dict(unfreeze(model.params       )       )
					lowercase_       =					flatten_dict(unfreeze(new_model.params       )       )

					for key in base_params.keys():
									lowercase_       =					(base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(SCREAMING_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(
									    SCREAMING_SNAKE_CASE_       ,							repo_id='''valid_org/test-model-flax-org'''       ,							push_to_hub=SCREAMING_SNAKE_CASE_       ,							use_auth_token=self._token       )

					lowercase_       =					FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''       )

					lowercase_       =					flatten_dict(unfreeze(model.params       )       )
					lowercase_       =					flatten_dict(unfreeze(new_model.params       )       )

					for key in base_params.keys():
									lowercase_       =					(base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(SCREAMING_SNAKE_CASE_       ,							1e-3       ,							msg=f'''{key} not identical'''       )




def 				a						(							snake_case__:					Any						,   snake_case__:					List[str]					):


				'''simple docstring'''
				lowercase_       =					True
				lowercase_       =					flatten_dict(modela.params					)
				lowercase_       =					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:
												lowercase_       =					False

				return models_are_equal


@require_flax
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			int       )       ->     Union[str, Any]:
					lowercase_       =					BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''       )
					lowercase_       =					FlaxBertModel(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''bert'''
					with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )       )

									with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       )

									lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,							subfolder=SCREAMING_SNAKE_CASE_       )

					self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )       )
	def 						_lowercase  (       self  :			List[str]       )       ->     Union[str, Any]:
					lowercase_       =					BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''       )
					lowercase_       =					FlaxBertModel(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''bert'''
					with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )       ,							max_shard_size='''10KB'''       )

									with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       )

									lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,							subfolder=SCREAMING_SNAKE_CASE_       )

					self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					lowercase_       =					'''bert'''
					lowercase_       =					'''hf-internal-testing/tiny-random-bert-subfolder'''

					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,							subfolder=SCREAMING_SNAKE_CASE_       )

					self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Any       )       ->     str:
					lowercase_       =					'''bert'''
					lowercase_       =					'''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,							subfolder=SCREAMING_SNAKE_CASE_       )

					self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






from typing import TYPE_CHECKING

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


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




