


'''simple docstring'''



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

import black


_lowercase      :      List[Any]            =					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.
_lowercase      :      int            =					"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class 	UpperCamelCase__(       unittest.TestCase	):




    def 					a__(		self      :			Any				)->					Dict:

          """simple docstring"""




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




    def 					a__(		self      :			Tuple				)->					Optional[Any]:

          """simple docstring"""




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




    def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			List[str]=None				)->					Optional[Any]:

          """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=119				)
          UpperCAmelCase					    =     black.format_str(lowerCAmelCase				,   mode=lowerCAmelCase				)
          UpperCAmelCase					    =     os.path.join(self.transformer_dir				,   '''new_code.py'''				)
          with open(lowerCAmelCase				,   '''w'''				,   newline='''\n'''				) as f:
                f.write(lowerCAmelCase				)
          if overwrite_result is None:
                self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase				)				) == 0				)
          else:
                check_copies.is_copy_consistent(f.name				,   overwrite=lowerCAmelCase				)
                with open(lowerCAmelCase				,   '''r'''				) as f:
                      self.assertTrue(f.read()				,   lowerCAmelCase				)




    def 					a__(		self      :			Tuple				)->					Optional[int]:

          """simple docstring"""




          UpperCAmelCase					    =     check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''				)
          self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)




    def 					a__(		self      :			List[Any]				)->					str:

          """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'''				,   lowerCAmelCase				,   )

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

          # 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'''				,   lowerCAmelCase				,   lowerCAmelCase				)				,   )

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





    def 					a__(		self      :			Any				)->					Tuple:

          """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(
              lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

          self.assertFalse(lowerCAmelCase				)
          self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)

          UpperCAmelCase    ,    UpperCAmelCase					    =     check_copies.convert_to_localized_md(
              lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

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

          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(
              lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

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









'''simple docstring'''



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet, attn in zip(self.resnets				,   self.attentions				):
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)
										UpperCAmelCase					    =     resnets

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet in self.resnets:
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Union[str, Any]=True				)->					Optional[int]:

										"""simple docstring"""




										for resnet, attn in zip(self.resnets				,   self.attentions				):
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict=True				)->					Tuple:

										"""simple docstring"""




										for resnet in self.resnets:
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     [
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
										]

										UpperCAmelCase					    =     []

										for _ in range(self.num_layers				):
																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.in_channels				,   n_heads=self.num_attention_heads				,   d_head=self.in_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions





				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any=True				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.resnets[0](lowerCAmelCase				,   lowerCAmelCase				)
										for attn, resnet in zip(self.attentions				,   self.resnets[1:]				):
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										return hidden_states





