"""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      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     Tuple	,      A_							:     Any=13	,      A_							:     Tuple=7	,      A_							:     str=True	,      A_							:     str=True	,      A_							:     Dict=True	,      A_							:     Union[str, Any]=True	,      A_							:     Tuple=True	,      A_							:     Union[str, Any]=False	,      A_							:     Union[str, Any]=False	,      A_							:     List[Any]=False	,      A_							:     Optional[int]=2	,      A_							:     Tuple=99	,      A_							:     List[str]=0	,      A_							:     int=32	,      A_							:     List[str]=5	,      A_							:     List[str]=4	,      A_							:     List[Any]=0.1	,      A_							:     Optional[Any]=0.1	,      A_							:     Optional[Any]=512	,      A_							:     Any=12	,      A_							:     Optional[int]=2	,      A_							:     Union[str, Any]=0.02	,      A_							:     Optional[Any]=3	,      A_							:     Optional[Any]=4	,      A_							:     List[Any]="last"	,      A_							:     str=None	,      A_							:     Optional[int]=None	,      )    ->       Any:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_input_lengths
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							gelu_activation
         __snake_case				=							sinusoidal_embeddings
         __snake_case				=							causal
         __snake_case				=							asm
         __snake_case				=							n_langs
         __snake_case				=							vocab_size
         __snake_case				=							n_special
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_labels
         __snake_case				=							num_choices
         __snake_case				=							summary_type
         __snake_case				=							use_proj
         __snake_case				=							scope
    def  lowercase					(		self							:     str    )    ->       Tuple:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )
         __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

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

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

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

         __snake_case				=							self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         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  lowercase					(		self							:     Optional[Any]	,      A_							:     Tuple	,      A_							:     int	,      A_							:     Tuple	,      A_							:     Optional[int]	,      A_							:     Any	,      A_							:     Dict	,      A_							:     Union[str, Any]	,      A_							:     Union[str, Any]	,      A_							:     Tuple	,      )    ->       int:
         __snake_case				=							FlaubertModel(config=A_    )
         model.to(A_    )
         model.eval()
         __snake_case				=							model(A_	,      lengths=A_	,      langs=A_    )
         __snake_case				=							model(A_	,      langs=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     Dict	,      A_							:     Optional[Any]	,      A_							:     Optional[Any]	,      A_							:     Optional[Any]	,      A_							:     Dict	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     Tuple	,      A_							:     List[Any]	,      A_							:     Dict	,      )    ->       List[Any]:
         __snake_case				=							FlaubertWithLMHeadModel(A_    )
         model.to(A_    )
         model.eval()

         __snake_case				=							model(A_	,      token_type_ids=A_	,      labels=A_    )
         self.parent.assertEqual(result.loss.shape	,      ()    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)    )
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Optional[Any]	,      A_							:     Union[str, Any]	,      A_							:     int	,      A_							:     Optional[Any]	,      A_							:     List[str]	,      A_							:     List[str]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]	,      )    ->       Optional[Any]:
         __snake_case				=							FlaubertForQuestionAnsweringSimple(A_    )
         model.to(A_    )
         model.eval()

         __snake_case				=							model(A_    )

         __snake_case				=							model(A_	,      start_positions=A_	,      end_positions=A_    )
         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  lowercase					(		self							:     Dict	,      A_							:     int	,      A_							:     Optional[int]	,      A_							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     Union[str, Any]	,      A_							:     Optional[int]	,      A_							:     Any	,      A_							:     Union[str, Any]	,      )    ->       Tuple:
         __snake_case				=							FlaubertForQuestionAnswering(A_    )
         model.to(A_    )
         model.eval()

         __snake_case				=							model(A_    )

         __snake_case				=							model(
             A_	,      start_positions=A_	,      end_positions=A_	,      cls_index=A_	,      is_impossible=A_	,      p_mask=A_	,      )

         __snake_case				=							model(
             A_	,      start_positions=A_	,      end_positions=A_	,      cls_index=A_	,      is_impossible=A_	,      )

         ((__snake_case)		,	)				=							result_with_labels.to_tuple()

         __snake_case				=							model(A_	,      start_positions=A_	,      end_positions=A_    )

         ((__snake_case)		,	)				=							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  lowercase					(		self							:     List[str]	,      A_							:     Dict	,      A_							:     List[Any]	,      A_							:     List[Any]	,      A_							:     Any	,      A_							:     Optional[Any]	,      A_							:     Dict	,      A_							:     Dict	,      A_							:     int	,      A_							:     Union[str, Any]	,      )    ->       Union[str, Any]:
         __snake_case				=							FlaubertForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()

         __snake_case				=							model(A_    )
         __snake_case				=							model(A_	,      labels=A_    )

         self.parent.assertEqual(result.loss.shape	,      ()    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     Optional[Any]	,      A_							:     str	,      A_							:     int	,      A_							:     Dict	,      A_							:     str	,      A_							:     Optional[int]	,      A_							:     int	,      A_							:     List[Any]	,      A_							:     List[str]	,      )    ->       Dict:
         __snake_case				=							self.num_labels
         __snake_case				=							FlaubertForTokenClassification(A_    )
         model.to(A_    )
         model.eval()

         __snake_case				=							model(A_	,      attention_mask=A_	,      labels=A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]	,      A_							:     Tuple	,      A_							:     Optional[Any]	,      A_							:     int	,      A_							:     str	,      A_							:     Tuple	,      A_							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     List[Any]	,      )    ->       Any:
         __snake_case				=							self.num_choices
         __snake_case				=							FlaubertForMultipleChoice(config=A_    )
         model.to(A_    )
         model.eval()
         __snake_case				=							input_ids.unsqueeze(1    ).expand(-1	,      self.num_choices	,      -1    ).contiguous()
         __snake_case				=							token_type_ids.unsqueeze(1    ).expand(-1	,      self.num_choices	,      -1    ).contiguous()
         __snake_case				=							input_mask.unsqueeze(1    ).expand(-1	,      self.num_choices	,      -1    ).contiguous()
         __snake_case				=							model(
             A_	,      attention_mask=A_	,      token_type_ids=A_	,      labels=A_	,      )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_choices)    )







    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''lengths''': input_lengths,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					(
        (
            FlaubertModel,
            FlaubertWithLMHeadModel,
            FlaubertForQuestionAnswering,
            FlaubertForQuestionAnsweringSimple,
            FlaubertForSequenceClassification,
            FlaubertForTokenClassification,
            FlaubertForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    UpperCamelCase_	:					Optional[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  lowercase					(		self							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     Any	,      A_							:     List[Any]	,      A_							:     str	,      A_							:     List[str]    )    ->       Optional[Any]:
         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  lowercase					(		self							:     str	,      A_							:     List[str]	,      A_							:     Any	,      A_							:     Optional[int]=False    )    ->       Optional[Any]:
         __snake_case				=							super()._prepare_for_class(A_	,      A_	,      return_labels=A_    )

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

         return inputs_dict
    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							FlaubertModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      emb_dim=37    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*A_    )
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_simple_qa(*A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*A_    )
    def  lowercase					(		self							:     Any    )    ->       Any:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_token_classif(*A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_multiple_choice(*A_    )
    @slow
    def  lowercase					(		self							:     Any    )    ->       Tuple:
         for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __snake_case				=							FlaubertModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )







    @slow
    @require_torch_gpu
    def  lowercase					(		self							:     int    )    ->       Any:
         __snake_case		,	__snake_case				=							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

              __snake_case				=							True
              __snake_case				=							model_class(config=A_    )

              __snake_case				=							self._prepare_for_class(A_	,      A_    )
              __snake_case				=							torch.jit.trace(
                  A_	,      (inputs_dict['''input_ids'''].to('''cpu'''    ), inputs_dict['''attention_mask'''].to('''cpu'''    ))    )

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






@require_torch
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         __snake_case				=							FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''    )
         __snake_case				=							torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]    )
         with torch.no_grad():
              __snake_case				=							model(A_    )[0]
         __snake_case				=							torch.Size((1, 11, 768)    )
         self.assertEqual(output.shape	,      A_    )
         __snake_case				=							torch.tensor(
             [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]]    )

         self.assertTrue(torch.allclose(output[:, :3, :3]	,      A_	,      atol=1E-4    )    )
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}