



'''simple docstring'''





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  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Dict=2				,		UpperCamelCase__      :     Tuple=56				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     List[Any]=99				,		UpperCamelCase__      :     Union[str, Any]=32				,		UpperCamelCase__      :     Optional[Any]=2				,		UpperCamelCase__      :     int=2				,		UpperCamelCase__      :     str=7				,		UpperCamelCase__      :     Tuple="gelu_new"				,		UpperCamelCase__      :     Optional[int]=0.1				,		UpperCamelCase__      :     Optional[int]=0.1				,		UpperCamelCase__      :     Dict=5_12				,		UpperCamelCase__      :     Tuple=16				,		UpperCamelCase__      :     Any=2				,		UpperCamelCase__      :     Optional[Any]=0.02				,		UpperCamelCase__      :     Union[str, Any]=4				,		UpperCamelCase__      :     List[Any]="block_sparse"				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Optional[int]=2				,		UpperCamelCase__      :     int=3				,		)				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     seq_length
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_attention_mask
								UpperCAmelCase         =     use_token_type_ids
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     type_vocab_size
								UpperCAmelCase         =     type_sequence_label_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     num_choices

								UpperCAmelCase         =     rescale_embeddings
								UpperCAmelCase         =     attention_type
								UpperCAmelCase         =     use_bias
								UpperCAmelCase         =     block_size
								UpperCAmelCase         =     num_random_blocks






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size      )

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

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

								UpperCAmelCase         =     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=UpperCamelCase__				,		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 SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
								UpperCAmelCase         =     {
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "attention_mask": attention_mask,
								}
								return config, inputs_dict





@require_flax
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Dict          =(
		    (
		        FlaxBigBirdForCausalLM,
		        FlaxBigBirdModel,
		        FlaxBigBirdForPreTraining,
		        FlaxBigBirdForMaskedLM,
		        FlaxBigBirdForMultipleChoice,
		        FlaxBigBirdForQuestionAnswering,
		        FlaxBigBirdForSequenceClassification,
		        FlaxBigBirdForTokenClassification,
		    )
		    if is_flax_available()
		    else ()
		)

		lowercase      :       Any          =False
		lowercase      :       Tuple          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxBigBirdModelTester(self      )






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								super().test_from_pretrained_save_pretrained()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								super().test_from_pretrained_with_no_automatic_init()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								super().test_no_automatic_init()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								super().test_hidden_states_output()






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								for model_class_name in self.all_model_classes:
														UpperCAmelCase         =     model_class_name.from_pretrained("google/bigbird-roberta-base"      )
														self.assertIsNotNone(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																				UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )
																				UpperCAmelCase         =     model_class(UpperCamelCase__      )

																				@jax.jit
																				def model_jitted(UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple=None				,		**UpperCamelCase__      :     List[str]      ):
																										return model(input_ids=UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		**UpperCamelCase__      )

																				with self.subTest("JIT Enabled"      ):
																										UpperCAmelCase         =     model_jitted(**UpperCamelCase__      ).to_tuple()

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

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







		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]=1e-5				,		UpperCamelCase__      :     Tuple="outputs"				,		UpperCamelCase__      :     Optional[Any]=None      )				->					Optional[int]:
								'''simple docstring'''







								if name.startswith("outputs.attentions"      ):
														return
								else:
														super().check_pt_flax_outputs(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )








import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCamelCase     :							str  							=				os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCamelCase     :							Optional[Any]  							=				"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     self.transformer_dir
								shutil.copy(
								    os.path.join(UpperCamelCase__				,		"src/transformers/models/bert/modeling_bert.py"      )				,		os.path.join(self.transformer_dir				,		"models/bert/modeling_bert.py"      )				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     os.path.join(self.transformer_dir				,		"new_code.py"      )
								with open(UpperCamelCase__				,		"w"				,		newline="\n"      ) as f:
														f.write(UpperCamelCase__      )
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__      )      ) == 0      )
								else:
														check_copies.is_copy_consistent(f.name				,		overwrite=UpperCamelCase__      )
														with open(UpperCamelCase__				,		"r"      ) as f:
																				self.assertTrue(f.read()				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		REFERENCE_CODE + "\n"				,		)

								# With no empty line at the end
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		UpperCamelCase__				,		)

								# Copy consistency with rename
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)

								# Copy consistency with a really long name
								UpperCAmelCase         =     "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
								self.check_copy_consistency(
								    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'				,		F'{long_class_name}LMPredictionHead'				,		re.sub("Bert"				,		UpperCamelCase__				,		UpperCamelCase__      )				,		)

								# Copy consistency with overwrite
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		UpperCamelCase__				,		overwrite_result=re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)







		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
								    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
								    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
								    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
								    " Luong, Quoc V. Le, Christopher D. Manning."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
								    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
								    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
								    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
								    " Christopher D. Manning 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check whether the number of models is equal to README.md after conversion.
								self.assertTrue(UpperCamelCase__      )

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
								    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check if the model link is synchronized.
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



