




import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax

		from transformers.models.big_bird.modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		)




class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     str=56					,      __lowerCamelCase				:     int=True					,      __lowerCamelCase				:     Any=True					,      __lowerCamelCase				:     Dict=True					,      __lowerCamelCase				:     List[str]=True					,      __lowerCamelCase				:     Optional[int]=99					,      __lowerCamelCase				:     List[str]=32					,      __lowerCamelCase				:     Any=2					,      __lowerCamelCase				:     Union[str, Any]=2					,      __lowerCamelCase				:     List[Any]=7					,      __lowerCamelCase				:     Optional[int]="gelu_new"					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Union[str, Any]=5_12					,      __lowerCamelCase				:     Optional[int]=16					,      __lowerCamelCase				:     Union[str, Any]=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     str=4					,      __lowerCamelCase				:     List[Any]="block_sparse"					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     List[Any]=False					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     Any=3					,      )					->     List[str]:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_attention_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_choices

							a    =			rescale_embeddings
							a    =			attention_type
							a    =			use_bias
							a    =			block_size
							a    =			num_random_blocks





				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[Any]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

							a    =			None
							if self.use_attention_mask:
										a    =			random_attention_mask([self.batch_size, self.seq_length]							)

							a    =			None
							if self.use_token_type_ids:
										a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size							)

							a    =			BigBirdConfig(
							    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=__lowerCamelCase					,      initializer_range=self.initializer_range					,      attention_type=self.attention_type					,      block_size=self.block_size					,      num_random_blocks=self.num_random_blocks					,      use_bias=self.use_bias					,      rescale_embeddings=self.rescale_embeddings					,      )

							return config, input_ids, token_type_ids, attention_mask





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							a    =			self.prepare_config_and_inputs()
							a    ,		a    ,		a    ,		a    =			config_and_inputs
							a    =			{
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "attention_mask": attention_mask,
							}
							return config, inputs_dict




@require_flax
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							a    =			FlaxBigBirdModelTester(self							)





				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[Any]:
							super().test_from_pretrained_save_pretrained()





				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     str:
							super().test_from_pretrained_with_no_automatic_init()





				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union[str, Any]:
							super().test_no_automatic_init()





				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     str:
							super().test_hidden_states_output()





				@slow
				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							for model_class_name in self.all_model_classes:
										a    =			model_class_name.from_pretrained("google/bigbird-roberta-base"							)
										self.assertIsNotNone(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							if self.test_attn_probs:
										super().test_attention_outputs()





				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__							):
													a    =			self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase							)
													a    =			model_class(__lowerCamelCase							)

													@jax.jit
													def model_jitted(__lowerCamelCase				:     int					,      __lowerCamelCase				:     Optional[Any]=None					,      **__lowerCamelCase				:     Tuple							):
																return model(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      **__lowerCamelCase							)

													with self.subTest("JIT Enabled"							):
																a    =			model_jitted(**__lowerCamelCase							).to_tuple()

													with self.subTest("JIT Disabled"							):
																with jax.disable_jit():
																			a    =			model_jitted(**__lowerCamelCase							).to_tuple()

													self.assertEqual(len(__lowerCamelCase							)					,      len(__lowerCamelCase							)							)
													for jitted_output, output in zip(__lowerCamelCase					,      __lowerCamelCase							):
																self.assertEqual(jitted_output.shape					,      output.shape							)





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Union[str, Any]=1e-5					,      __lowerCamelCase				:     List[str]="outputs"					,      __lowerCamelCase				:     Dict=None							)					->     Any:
							# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
							# an effort was done to return `attention_probs` (yet to be verified).
							if name.startswith("outputs.attentions"							):
										return
							else:
										super().check_pt_flax_outputs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)











'''simple docstring'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

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




