"""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  snake_case__      (       UpperCamelCase_       ):


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


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)
        __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

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

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

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

        __a       =    self.get_config()

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


 def 						a__   ( self						):
        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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    FlaubertModel(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        __a       =    model(__SCREAMING_SNAKE_CASE    ,		lengths=__SCREAMING_SNAKE_CASE    ,		langs=__SCREAMING_SNAKE_CASE						)
        __a       =    model(__SCREAMING_SNAKE_CASE    ,		langs=__SCREAMING_SNAKE_CASE						)
        __a       =    model(__SCREAMING_SNAKE_CASE						)
        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    FlaubertWithLMHeadModel(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

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


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    FlaubertForQuestionAnsweringSimple(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

        __a       =    model(__SCREAMING_SNAKE_CASE						)

        __a       =    model(__SCREAMING_SNAKE_CASE    ,		start_positions=__SCREAMING_SNAKE_CASE    ,		end_positions=__SCREAMING_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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    FlaubertForQuestionAnswering(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

        __a       =    model(__SCREAMING_SNAKE_CASE						)

        __a       =    model(
            __SCREAMING_SNAKE_CASE    ,		start_positions=__SCREAMING_SNAKE_CASE    ,		end_positions=__SCREAMING_SNAKE_CASE    ,		cls_index=__SCREAMING_SNAKE_CASE    ,		is_impossible=__SCREAMING_SNAKE_CASE    ,		p_mask=__SCREAMING_SNAKE_CASE    ,		)

        __a       =    model(
            __SCREAMING_SNAKE_CASE    ,		start_positions=__SCREAMING_SNAKE_CASE    ,		end_positions=__SCREAMING_SNAKE_CASE    ,		cls_index=__SCREAMING_SNAKE_CASE    ,		is_impossible=__SCREAMING_SNAKE_CASE    ,		)

        (__a)       =    result_with_labels.to_tuple()

        __a       =    model(__SCREAMING_SNAKE_CASE    ,		start_positions=__SCREAMING_SNAKE_CASE    ,		end_positions=__SCREAMING_SNAKE_CASE						)

        (__a)       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    FlaubertForSequenceClassification(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

        __a       =    model(__SCREAMING_SNAKE_CASE						)
        __a       =    model(__SCREAMING_SNAKE_CASE    ,		labels=__SCREAMING_SNAKE_CASE						)

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


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    self.num_labels
        __a       =    FlaubertForTokenClassification(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

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


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    self.num_choices
        __a       =    FlaubertForMultipleChoice(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        __a       =    input_ids.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    token_type_ids.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    input_mask.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    model(
            __SCREAMING_SNAKE_CASE    ,		attention_mask=__SCREAMING_SNAKE_CASE    ,		token_type_ids=__SCREAMING_SNAKE_CASE    ,		labels=__SCREAMING_SNAKE_CASE    ,		)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices)						)






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






@require_torch
class  snake_case__      (       UpperCamelCase_,						UpperCamelCase_,						unittest.TestCase       ):
 _snake_case       :					Optional[Any]							  =						(
     (
         FlaubertModel,
         FlaubertWithLMHeadModel,
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
         FlaubertForSequenceClassification,
         FlaubertForTokenClassification,
         FlaubertForMultipleChoice,
     )
     if is_torch_available()
     else ()
 )
 _snake_case       :					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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):







        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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=False						):
        __a       =    super()._prepare_for_class(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE    ,		return_labels=__SCREAMING_SNAKE_CASE						)

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

        return inputs_dict


 def 						a__   ( self						):
        __a       =    FlaubertModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=__SCREAMING_SNAKE_CASE    ,		emb_dim=37						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_simple_qa(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_token_classif(*__SCREAMING_SNAKE_CASE						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_multiple_choice(*__SCREAMING_SNAKE_CASE						)


 @slow
 def 						a__   ( self						):







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






 @slow
 @require_torch_gpu
 def 						a__   ( self						):
        __a       =    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

               __a       =    True
               __a       =    model_class(config=__SCREAMING_SNAKE_CASE						)

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

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






@require_torch
class  snake_case__      (       unittest.TestCase       ):


 @slow
 def 						a__   ( self						):
        __a       =    FlaubertModel.from_pretrained("flaubert/flaubert_base_cased"						)
        __a       =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
        with torch.no_grad():
               __a       =    model(__SCREAMING_SNAKE_CASE						)[0]
        __a       =    torch.Size((1, 11, 768)						)
        self.assertEqual(output.shape    ,		__SCREAMING_SNAKE_CASE						)
        __a       =    torch.tensor(
            [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]						)

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


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCamelCase  =logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_			)
class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           super().__init__(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           requires_backends(self    ,    '''vision'''	)
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{}
           UpperCamelCase__     : int											=					{}

           if prompt is not None:
                 UpperCamelCase__     : int											=					prompt

           if generate_kwargs is not None:
                 UpperCamelCase__     : int											=					generate_kwargs
           if max_new_tokens is not None:
                 if "generate_kwargs" not in forward_kwargs:
                       UpperCamelCase__     : Union[str, Any]											=					{}
                 if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                       raise ValueError(
                           '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                           ''' please use only one'''	)
                 UpperCamelCase__     : Dict											=					max_new_tokens
           return preprocess_params, forward_kwargs, {}






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return super().__call__(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					load_image(__SCREAMING_SNAKE_CASE	)

           if prompt is not None:
                 if not isinstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                       raise ValueError(
                           F'''Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE	)} - but expected a single string. '''
                           '''Note also that one single text can be provided for conditional image to text generation.'''	)

                 UpperCamelCase__     : Optional[int]											=					self.model.config.model_type

                 if model_type == "git":
                       UpperCamelCase__     : Optional[int]											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)
                       UpperCamelCase__     : str											=					self.tokenizer(text=__SCREAMING_SNAKE_CASE    ,    add_special_tokens=__SCREAMING_SNAKE_CASE	).input_ids
                       UpperCamelCase__     : Dict											=					[self.tokenizer.cls_token_id] + input_ids
                       UpperCamelCase__     : int											=					torch.tensor(__SCREAMING_SNAKE_CASE	).unsqueeze(0	)
                       model_inputs.update({'''input_ids''': input_ids}	)

                 elif model_type == "pix2struct":
                       UpperCamelCase__     : Tuple											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    header_text=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)

                 elif model_type != "vision-encoder-decoder":
                       # vision-encoder-decoder does not support conditional generation
                       UpperCamelCase__     : int											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)
                       UpperCamelCase__     : int											=					self.tokenizer(__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)
                       model_inputs.update(__SCREAMING_SNAKE_CASE	)

                 else:
                       raise ValueError(F'''Model type {model_type} does not support conditional text generation'''	)

           else:
                 UpperCamelCase__     : str											=					self.image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors=self.framework	)

           if self.model.config.model_type == "git" and prompt is None:
                 UpperCamelCase__     : Optional[Any]											=					None

           return model_inputs






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> Optional[Any]:




           """simple docstring"""







           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']    ,    __SCREAMING_SNAKE_CASE	)
               and all(x is None for x in model_inputs['''input_ids''']	)
           ):
                 UpperCamelCase__     : Union[str, Any]											=					None

           if generate_kwargs is None:
                 UpperCamelCase__     : Optional[Any]											=					{}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           UpperCamelCase__     : Any											=					model_inputs.pop(self.model.main_input_name	)
           UpperCamelCase__     : Any											=					self.model.generate(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return model_outputs






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					[]
           for output_ids in model_outputs:
                 UpperCamelCase__     : str											=					{
                     '''generated_text''': self.tokenizer.decode(
                         __SCREAMING_SNAKE_CASE    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE    ,    )
                 }
                 records.append(__SCREAMING_SNAKE_CASE	)
           return records


