

'''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     _SCREAMING_SNAKE_CASE       (       lowercase_      ):



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



 def 					snake_case__    (       self						:       List[str]     ):
      __magic_name__		  =							ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size     )
      __magic_name__		  =							random_attention_mask([self.batch_size, self.seq_length]     )

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

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

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

      __magic_name__		  =							self.get_config()

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



 def 					snake_case__    (       self						:       str     ):
      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 					snake_case__    (       self						:       Dict					, a__						:       Tuple					, a__						:       List[str]					, a__						:       Optional[int]					, a__						:       str					, a__						:       Optional[Any]					, a__						:       List[str]					, a__						:       List[str]					, a__						:       str					, a__						:       Union[str, Any]					, ):
      __magic_name__		  =							FlaubertModel(config=_lowercase     )
      model.to(_lowercase     )
      model.eval()
      __magic_name__		  =							model(_lowercase					, lengths=_lowercase					, langs=_lowercase     )
      __magic_name__		  =							model(_lowercase					, langs=_lowercase     )
      __magic_name__		  =							model(_lowercase     )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)     )



 def 					snake_case__    (       self						:       int					, a__						:       Union[str, Any]					, a__						:       Union[str, Any]					, a__						:       List[str]					, a__						:       str					, a__						:       Any					, a__						:       Optional[int]					, a__						:       Dict					, a__						:       str					, a__						:       List[Any]					, ):
      __magic_name__		  =							FlaubertWithLMHeadModel(_lowercase     )
      model.to(_lowercase     )
      model.eval()

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



 def 					snake_case__    (       self						:       str					, a__						:       Optional[Any]					, a__						:       Optional[Any]					, a__						:       Optional[int]					, a__						:       Dict					, a__						:       str					, a__						:       Optional[Any]					, a__						:       Tuple					, a__						:       Tuple					, a__						:       List[str]					, ):
      __magic_name__		  =							FlaubertForQuestionAnsweringSimple(_lowercase     )
      model.to(_lowercase     )
      model.eval()

      __magic_name__		  =							model(_lowercase     )

      __magic_name__		  =							model(_lowercase					, start_positions=_lowercase					, end_positions=_lowercase     )
      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 					snake_case__    (       self						:       Tuple					, a__						:       Optional[Any]					, a__						:       Tuple					, a__						:       Any					, a__						:       Dict					, a__						:       Dict					, a__						:       List[Any]					, a__						:       Optional[int]					, a__						:       int					, a__						:       Any					, ):
      __magic_name__		  =							FlaubertForQuestionAnswering(_lowercase     )
      model.to(_lowercase     )
      model.eval()

      __magic_name__		  =							model(_lowercase     )

      __magic_name__		  =							model(
          _lowercase					, start_positions=_lowercase					, end_positions=_lowercase					, cls_index=_lowercase					, is_impossible=_lowercase					, p_mask=_lowercase					, )

      __magic_name__		  =							model(
          _lowercase					, start_positions=_lowercase					, end_positions=_lowercase					, cls_index=_lowercase					, is_impossible=_lowercase					, )

      ((__magic_name__    )  ,        )		  =							result_with_labels.to_tuple()

      __magic_name__		  =							model(_lowercase					, start_positions=_lowercase					, end_positions=_lowercase     )

      ((__magic_name__    )  ,        )		  =							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 					snake_case__    (       self						:       Tuple					, a__						:       Any					, a__						:       Optional[Any]					, a__						:       Any					, a__						:       List[str]					, a__						:       List[str]					, a__						:       List[Any]					, a__						:       List[Any]					, a__						:       Tuple					, a__						:       str					, ):
      __magic_name__		  =							FlaubertForSequenceClassification(_lowercase     )
      model.to(_lowercase     )
      model.eval()

      __magic_name__		  =							model(_lowercase     )
      __magic_name__		  =							model(_lowercase					, labels=_lowercase     )

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



 def 					snake_case__    (       self						:       int					, a__						:       Any					, a__						:       Optional[Any]					, a__						:       Union[str, Any]					, a__						:       int					, a__						:       Optional[Any]					, a__						:       str					, a__						:       int					, a__						:       Any					, a__						:       Dict					, ):
      __magic_name__		  =							self.num_labels
      __magic_name__		  =							FlaubertForTokenClassification(_lowercase     )
      model.to(_lowercase     )
      model.eval()

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



 def 					snake_case__    (       self						:       Union[str, Any]					, a__						:       str					, a__						:       Union[str, Any]					, a__						:       List[str]					, a__						:       List[str]					, a__						:       Union[str, Any]					, a__						:       str					, a__						:       Optional[int]					, a__						:       Union[str, Any]					, a__						:       List[Any]					, ):
      __magic_name__		  =							self.num_choices
      __magic_name__		  =							FlaubertForMultipleChoice(config=_lowercase     )
      model.to(_lowercase     )
      model.eval()
      __magic_name__		  =							input_ids.unsqueeze(1     ).expand(-1					, self.num_choices					, -1     ).contiguous()
      __magic_name__		  =							token_type_ids.unsqueeze(1     ).expand(-1					, self.num_choices					, -1     ).contiguous()
      __magic_name__		  =							input_mask.unsqueeze(1     ).expand(-1					, self.num_choices					, -1     ).contiguous()
      __magic_name__		  =							model(
          _lowercase					, attention_mask=_lowercase					, token_type_ids=_lowercase					, labels=_lowercase					, )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices)     )



 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							self.prepare_config_and_inputs()
      (
          (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,    (
          __magic_name__    
      )  ,        
      )		  =							config_and_inputs
      __magic_name__		  =							{
          '''input_ids''': input_ids,
          '''token_type_ids''': token_type_ids,
          '''lengths''': input_lengths,
          '''attention_mask''': input_mask,
      }
      return config, inputs_dict






@require_torch
class     _SCREAMING_SNAKE_CASE       (       lowercase_						,lowercase_						,unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE	:str     			=	(
     (
         FlaubertModel,
         FlaubertWithLMHeadModel,
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
         FlaubertForSequenceClassification,
         FlaubertForTokenClassification,
         FlaubertForMultipleChoice,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE	:Tuple     			=	(
     {
         """feature-extraction""": FlaubertModel,
         """fill-mask""": FlaubertWithLMHeadModel,
         """question-answering""": FlaubertForQuestionAnsweringSimple,
         """text-classification""": FlaubertForSequenceClassification,
         """token-classification""": FlaubertForTokenClassification,
         """zero-shot""": FlaubertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )



 def 					snake_case__    (       self						:       List[str]					, a__						:       Tuple					, a__						:       Dict					, a__						:       str					, a__						:       List[str]					, a__						:       Union[str, 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 					snake_case__    (       self						:       Optional[Any]					, a__						:       Optional[int]					, a__						:       Tuple					, a__						:       List[Any]=False     ):
      __magic_name__		  =							super()._prepare_for_class(_lowercase					, _lowercase					, return_labels=_lowercase     )

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

      return inputs_dict



 def 					snake_case__    (       self						:       str     ):
      __magic_name__		  =							FlaubertModelTester(self     )
      __magic_name__		  =							ConfigTester(self					, config_class=_lowercase					, emb_dim=37     )



 def 					snake_case__    (       self						:       Tuple     ):
      self.config_tester.run_common_tests()



 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*_lowercase     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*_lowercase     )



 def 					snake_case__    (       self						:       List[str]     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_simple_qa(*_lowercase     )



 def 					snake_case__    (       self						:       Dict     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*_lowercase     )



 def 					snake_case__    (       self						:       List[Any]     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*_lowercase     )



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_token_classif(*_lowercase     )



 def 					snake_case__    (       self						:       Any     ):
      __magic_name__		  =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_multiple_choice(*_lowercase     )



 @slow
 def 					snake_case__    (       self						:       Dict     ):
      for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__		  =							FlaubertModel.from_pretrained(_lowercase     )
           self.assertIsNotNone(_lowercase     )



 @slow
 @require_torch_gpu
 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__  ,    __magic_name__		  =							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

           __magic_name__		  =							True
           __magic_name__		  =							model_class(config=_lowercase     )

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

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






@require_torch
class     _SCREAMING_SNAKE_CASE       (       unittest.TestCase      ):



 @slow
 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''     )
      __magic_name__		  =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]     )
      with torch.no_grad():
           __magic_name__		  =							model(_lowercase     )[0]
      __magic_name__		  =							torch.Size((1, 11, 768)     )
      self.assertEqual(output.shape					, _lowercase     )
      __magic_name__		  =							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]					, _lowercase					, atol=1E-4     )     )









'''simple docstring'''

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Tuple=None   )      -> Union[str, Any]:
							if rng is None:
														SCREAMING_SNAKE_CASE_				=				random.Random()

							SCREAMING_SNAKE_CASE_				=				1
							for dim in shape:
														total_dims *= dim

							SCREAMING_SNAKE_CASE_				=				[]
							for _ in range(lowerCAmelCase__   ):
														values.append(rng.randint(0		,vocab_size - 1   )   )

							SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__		,dtype=jnp.intaa   ).reshape(lowerCAmelCase__   )

							return output




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str=None   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				ids_tensor(lowerCAmelCase__		,vocab_size=2		,rng=lowerCAmelCase__   )
							# make sure that at least one token is attended to for each batch
							SCREAMING_SNAKE_CASE_				=				1
							return attn_mask






@require_flax
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     ()



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'].shape[-1] // 2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'][:max_batch_size, :sequence_length]

											SCREAMING_SNAKE_CASE_				=				jnp.ones_like(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											SCREAMING_SNAKE_CASE_				=				input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
																		# hack to allow generate for models such as GPT2 as is done in `generate()`
																		SCREAMING_SNAKE_CASE_				=				config.eos_token_id
											return config, input_ids, attention_mask, max_length



				@is_pt_flax_cross_test
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				pt_model_class(_lowercase       ).eval()
																		SCREAMING_SNAKE_CASE_				=				load_flax_weights_in_pytorch_model(_lowercase,     flax_model.params       )

																		SCREAMING_SNAKE_CASE_				=				flax_model.generate(_lowercase       ).sequences
																		SCREAMING_SNAKE_CASE_				=				pt_model.generate(torch.tensor(_lowercase,     dtype=torch.long       )       )

																		if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																									SCREAMING_SNAKE_CASE_				=				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																		self.assertListEqual(pt_generation_outputs.numpy().tolist(),     flax_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[0],     input_ids.shape[0] * config.num_return_sequences       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0.8
											SCREAMING_SNAKE_CASE_				=				10
											SCREAMING_SNAKE_CASE_				=				0.3
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )





				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )






@require_flax
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'       )
											SCREAMING_SNAKE_CASE_				=				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'       )

											SCREAMING_SNAKE_CASE_				=				'Hello world'
											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase,     return_tensors='np'       ).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(_lowercase,     'do_samples'       ):
																		model.generate(_lowercase,     do_samples=_lowercase       )

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(_lowercase,     'foo'       ):
																		SCREAMING_SNAKE_CASE_				=				{'foo': 'bar'}
																		model.generate(_lowercase,     **_lowercase       )





