






"""simple docstring"""







import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     FlaubertForMultipleChoice,
     FlaubertForQuestionAnswering,
     FlaubertForQuestionAnsweringSimple,
     FlaubertForSequenceClassification,
     FlaubertForTokenClassification,
     FlaubertModel,
     FlaubertWithLMHeadModel,
 )
 from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class   UpperCamelCase_ (       a_  ):



  def __init__(      self ,		snake_case__ ,		snake_case__=13 ,		snake_case__=7 ,		snake_case__=True ,		snake_case__=True ,		snake_case__=True ,		snake_case__=True ,		snake_case__=True ,		snake_case__=False ,		snake_case__=False ,		snake_case__=False ,		snake_case__=2 ,		snake_case__=99 ,		snake_case__=0 ,		snake_case__=32 ,		snake_case__=5 ,		snake_case__=4 ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=5_12 ,		snake_case__=12 ,		snake_case__=2 ,		snake_case__=0.02 ,		snake_case__=3 ,		snake_case__=4 ,		snake_case__="last" ,		snake_case__=None ,		snake_case__=None ,		)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			seq_length
         UpperCAmelCase	    =			is_training
         UpperCAmelCase	    =			use_input_lengths
         UpperCAmelCase	    =			use_token_type_ids
         UpperCAmelCase	    =			use_labels
         UpperCAmelCase	    =			gelu_activation
         UpperCAmelCase	    =			sinusoidal_embeddings
         UpperCAmelCase	    =			causal
         UpperCAmelCase	    =			asm
         UpperCAmelCase	    =			n_langs
         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			n_special
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         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_labels
         UpperCAmelCase	    =			num_choices
         UpperCAmelCase	    =			summary_type
         UpperCAmelCase	    =			use_proj
         UpperCAmelCase	    =			scope



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





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

         UpperCAmelCase	    =			None
         if self.use_input_lengths:
                UpperCAmelCase	    =			(
                    ids_tensor([self.batch_size] ,		vocab_size=2				) + self.seq_length - 2
                )  # small variation of seq_length

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

         UpperCAmelCase	    =			None
         UpperCAmelCase	    =			None
         UpperCAmelCase	    =			None
         if self.use_labels:
                UpperCAmelCase	    =			ids_tensor([self.batch_size] ,		self.type_sequence_label_size				)
                UpperCAmelCase	    =			ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels				)
                UpperCAmelCase	    =			ids_tensor([self.batch_size] ,		2				).float()
                UpperCAmelCase	    =			ids_tensor([self.batch_size] ,		self.num_choices				)

         UpperCAmelCase	    =			self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         return FlaubertConfig(
             vocab_size=self.vocab_size ,		n_special=self.n_special ,		emb_dim=self.hidden_size ,		n_layers=self.num_hidden_layers ,		n_heads=self.num_attention_heads ,		dropout=self.hidden_dropout_prob ,		attention_dropout=self.attention_probs_dropout_prob ,		gelu_activation=self.gelu_activation ,		sinusoidal_embeddings=self.sinusoidal_embeddings ,		asm=self.asm ,		causal=self.causal ,		n_langs=self.n_langs ,		max_position_embeddings=self.max_position_embeddings ,		initializer_range=self.initializer_range ,		summary_type=self.summary_type ,		use_proj=self.use_proj ,		)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertModel(config=snake_case__				)
         model.to(snake_case__				)
         model.eval()
         UpperCAmelCase	    =			model(snake_case__ ,		lengths=snake_case__ ,		langs=snake_case__				)
         UpperCAmelCase	    =			model(snake_case__ ,		langs=snake_case__				)
         UpperCAmelCase	    =			model(snake_case__				)
         self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertWithLMHeadModel(snake_case__				)
         model.to(snake_case__				)
         model.eval()

         UpperCAmelCase	    =			model(snake_case__ ,		token_type_ids=snake_case__ ,		labels=snake_case__				)
         self.parent.assertEqual(result.loss.shape ,		()				)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertForQuestionAnsweringSimple(snake_case__				)
         model.to(snake_case__				)
         model.eval()

         UpperCAmelCase	    =			model(snake_case__				)

         UpperCAmelCase	    =			model(snake_case__ ,		start_positions=snake_case__ ,		end_positions=snake_case__				)
         self.parent.assertEqual(result.start_logits.shape ,		(self.batch_size, self.seq_length)				)
         self.parent.assertEqual(result.end_logits.shape ,		(self.batch_size, self.seq_length)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertForQuestionAnswering(snake_case__				)
         model.to(snake_case__				)
         model.eval()

         UpperCAmelCase	    =			model(snake_case__				)

         UpperCAmelCase	    =			model(
             snake_case__ ,		start_positions=snake_case__ ,		end_positions=snake_case__ ,		cls_index=snake_case__ ,		is_impossible=snake_case__ ,		p_mask=snake_case__ ,		)

         UpperCAmelCase	    =			model(
             snake_case__ ,		start_positions=snake_case__ ,		end_positions=snake_case__ ,		cls_index=snake_case__ ,		is_impossible=snake_case__ ,		)

         ((UpperCAmelCase)    ,       )	    =			result_with_labels.to_tuple()

         UpperCAmelCase	    =			model(snake_case__ ,		start_positions=snake_case__ ,		end_positions=snake_case__				)

         ((UpperCAmelCase)    ,       )	    =			result_with_labels.to_tuple()

         self.parent.assertEqual(result_with_labels.loss.shape ,		()				)
         self.parent.assertEqual(result.start_top_log_probs.shape ,		(self.batch_size, model.config.start_n_top)				)
         self.parent.assertEqual(result.start_top_index.shape ,		(self.batch_size, model.config.start_n_top)				)
         self.parent.assertEqual(
             result.end_top_log_probs.shape ,		(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
         self.parent.assertEqual(
             result.end_top_index.shape ,		(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
         self.parent.assertEqual(result.cls_logits.shape ,		(self.batch_size,)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertForSequenceClassification(snake_case__				)
         model.to(snake_case__				)
         model.eval()

         UpperCAmelCase	    =			model(snake_case__				)
         UpperCAmelCase	    =			model(snake_case__ ,		labels=snake_case__				)

         self.parent.assertEqual(result.loss.shape ,		()				)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.num_labels
         UpperCAmelCase	    =			FlaubertForTokenClassification(snake_case__				)
         model.to(snake_case__				)
         model.eval()

         UpperCAmelCase	    =			model(snake_case__ ,		attention_mask=snake_case__ ,		labels=snake_case__				)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.num_labels)				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.num_choices
         UpperCAmelCase	    =			FlaubertForMultipleChoice(config=snake_case__				)
         model.to(snake_case__				)
         model.eval()
         UpperCAmelCase	    =			input_ids.unsqueeze(1				).expand(-1 ,		self.num_choices ,		-1				).contiguous()
         UpperCAmelCase	    =			token_type_ids.unsqueeze(1				).expand(-1 ,		self.num_choices ,		-1				).contiguous()
         UpperCAmelCase	    =			input_mask.unsqueeze(1				).expand(-1 ,		self.num_choices ,		-1				).contiguous()
         UpperCAmelCase	    =			model(
             snake_case__ ,		attention_mask=snake_case__ ,		token_type_ids=snake_case__ ,		labels=snake_case__ ,		)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_choices)				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





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





@require_torch
class   UpperCamelCase_ (       a_   ,					a_   ,					unittest.TestCase  ):
  _A       :					Optional[int]				=	(
      (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _A       :					Union[str, Any]				=	(
      {
          'feature-extraction': FlaubertModel,
          'fill-mask': FlaubertWithLMHeadModel,
          'question-answering': FlaubertForQuestionAnsweringSimple,
          'text-classification': FlaubertForSequenceClassification,
          'token-classification': FlaubertForTokenClassification,
          'zero-shot': FlaubertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			str:



         """simple docstring"""





         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith("""Fast"""				)
         ):
                # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                return True

         return False



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__=False				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			super()._prepare_for_class(snake_case__ ,		snake_case__ ,		return_labels=snake_case__				)

         if return_labels:
                if model_class.__name__ == "FlaubertForQuestionAnswering":
                       UpperCAmelCase	    =			torch.zeros(
                           self.model_tester.batch_size ,		dtype=torch.long ,		device=snake_case__				)
                       UpperCAmelCase	    =			torch.zeros(
                           self.model_tester.batch_size ,		dtype=torch.long ,		device=snake_case__				)

         return inputs_dict



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertModelTester(self				)
         UpperCAmelCase	    =			ConfigTester(self ,		config_class=snake_case__ ,		emb_dim=37				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         self.config_tester.run_common_tests()



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_simple_qa(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_token_classif(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_multiple_choice(*snake_case__				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase	    =			FlaubertModel.from_pretrained(snake_case__				)
                self.assertIsNotNone(snake_case__				)



  @slow
  @require_torch_gpu
  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
                # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
                if model_class == FlaubertForMultipleChoice:
                       return

                UpperCAmelCase	    =			True
                UpperCAmelCase	    =			model_class(config=snake_case__				)

                UpperCAmelCase	    =			self._prepare_for_class(snake_case__ ,		snake_case__				)
                UpperCAmelCase	    =			torch.jit.trace(
                    snake_case__ ,		(inputs_dict["""input_ids"""].to("""cpu"""				), inputs_dict["""attention_mask"""].to("""cpu"""				))				)

                with tempfile.TemporaryDirectory() as tmp:
                       torch.jit.save(snake_case__ ,		os.path.join(snake_case__ ,		"""traced_model.pt"""				)				)
                       UpperCAmelCase	    =			torch.jit.load(os.path.join(snake_case__ ,		"""traced_model.pt"""				) ,		map_location=snake_case__				)
                       loaded(inputs_dict["""input_ids"""].to(snake_case__				) ,		inputs_dict["""attention_mask"""].to(snake_case__				)				)





@require_torch
class   UpperCamelCase_ (       unittest.TestCase  ):



  @slow
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""				)
         UpperCAmelCase	    =			torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]				)
         with torch.no_grad():
                UpperCAmelCase	    =			model(snake_case__				)[0]
         UpperCAmelCase	    =			torch.Size((1, 11, 7_68)				)
         self.assertEqual(output.shape ,		snake_case__				)
         UpperCAmelCase	    =			torch.tensor(
             [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]				)

         self.assertTrue(torch.allclose(output[:, :3, :3] ,		snake_case__ ,		atol=1e-4				)				)








"""simple docstring"""







import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowerCAmelCase_	:    List[str]            =      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              UpperCAmelCase	    =			cached_file(
                  lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       # Load PyTorch checkpoint in tf2 model:
       UpperCAmelCase	    =			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase	,      lowerCAmelCase		)

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

              with torch.no_grad():
                     UpperCAmelCase	    =			pt_model(**pt_model.dummy_inputs		)

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			np.amax(np.abs(np_pt - np_tf		)		)
              print(F'''Max absolute difference between models outputs {diff}'''		)
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''		)
       tf_model.save_weights(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {model_type}'''		)
              print("""=""" * 100		)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			list(aws_model_maps.keys()		)
              if config_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      start=1		):
                     print("""-""" * 100		)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
                                   continue
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase	,      pytorch_checkpoint_path=lowerCAmelCase	,      config_file=lowerCAmelCase	,      tf_dump_path=os.path.join(lowerCAmelCase	,      model_shortcut_name + """-tf_model.h5"""		)	,      compare_with_pt_model=lowerCAmelCase	,      )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase		)
                            os.remove(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )
