


"""simple docstring"""



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

UpperCAmelCase      		=			logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase     )
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    int  ,     *snake_case__  :    Optional[Any]  ,     **snake_case__  :    Dict				):

              '''simple docstring'''






              super().__init__(*snake_case__  ,     **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  __snake_case						(   self  :    int  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    int=None  ,     snake_case__  :    Union[str, Any]=None				):

              '''simple docstring'''






              lowercase		:Dict       							=				{}
              lowercase		:Tuple       							=				{}

              if prompt is not None:
                     lowercase		:Any       							=				prompt

              if generate_kwargs is not None:
                     lowercase		:Tuple       							=				generate_kwargs
              if max_new_tokens is not None:
                     if "generate_kwargs" not in forward_kwargs:
                            lowercase		:Tuple       							=				{}
                     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'''				)
                     lowercase		:List[Any]       							=				max_new_tokens
              return preprocess_params, forward_kwargs, {}






       def __call__(   self  :    Optional[int]  ,     snake_case__  :    Union[str, List[str], "Image.Image", List["Image.Image"]]  ,     **snake_case__  :    str				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    int  ,     snake_case__  :    Any=None				):

              '''simple docstring'''






              lowercase		:List[str]       							=				load_image(snake_case__				)

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

                     lowercase		:int       							=				self.model.config.model_type

                     if model_type == "git":
                            lowercase		:int       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)
                            lowercase		:List[str]       							=				self.tokenizer(text=snake_case__  ,     add_special_tokens=snake_case__				).input_ids
                            lowercase		:Union[str, Any]       							=				[self.tokenizer.cls_token_id] + input_ids
                            lowercase		:str       							=				torch.tensor(snake_case__				).unsqueeze(0				)
                            model_inputs.update({'''input_ids''': input_ids}				)

                     elif model_type == "pix2struct":
                            lowercase		:Optional[int]       							=				self.image_processor(images=snake_case__  ,     header_text=snake_case__  ,     return_tensors=self.framework				)

                     elif model_type != "vision-encoder-decoder":
                            # vision-encoder-decoder does not support conditional generation
                            lowercase		:Optional[Any]       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)
                            lowercase		:List[Any]       							=				self.tokenizer(snake_case__  ,     return_tensors=self.framework				)
                            model_inputs.update(snake_case__				)

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

              else:
                     lowercase		:Optional[Any]       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)

              if self.model.config.model_type == "git" and prompt is None:
                     lowercase		:Dict       							=				None

              return model_inputs






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    str  ,     snake_case__  :    Any=None				):

              '''simple docstring'''






              if (
                  "input_ids" in model_inputs
                  and isinstance(model_inputs['''input_ids''']  ,     snake_case__				)
                  and all(x is None for x in model_inputs['''input_ids''']				)
              ):
                     lowercase		:Optional[int]       							=				None

              if generate_kwargs is None:
                     lowercase		:List[str]       							=				{}
              # 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.
              lowercase		:int       							=				model_inputs.pop(self.model.main_input_name				)
              lowercase		:List[Any]       							=				self.model.generate(snake_case__  ,     **snake_case__  ,     **snake_case__				)
              return model_outputs







       def  __snake_case						(   self  :    int  ,     snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:int       							=				[]
              for output_ids in model_outputs:
                     lowercase		:str       							=				{
                         '''generated_text''': self.tokenizer.decode(
                             snake_case__  ,     skip_special_tokens=snake_case__  ,     )
                     }
                     records.append(snake_case__				)
              return records





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      		=			{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XLMRobertaForCausalLM''',
     '''XLMRobertaForMaskedLM''',
     '''XLMRobertaForMultipleChoice''',
     '''XLMRobertaForQuestionAnswering''',
     '''XLMRobertaForSequenceClassification''',
     '''XLMRobertaForTokenClassification''',
     '''XLMRobertaModel''',
     '''XLMRobertaPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFXLMRobertaForCausalLM''',
     '''TFXLMRobertaForMaskedLM''',
     '''TFXLMRobertaForMultipleChoice''',
     '''TFXLMRobertaForQuestionAnswering''',
     '''TFXLMRobertaForSequenceClassification''',
     '''TFXLMRobertaForTokenClassification''',
     '''TFXLMRobertaModel''',
     '''TFXLMRobertaPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FlaxXLMRobertaForMaskedLM''',
     '''FlaxXLMRobertaForCausalLM''',
     '''FlaxXLMRobertaForMultipleChoice''',
     '''FlaxXLMRobertaForQuestionAnswering''',
     '''FlaxXLMRobertaForSequenceClassification''',
     '''FlaxXLMRobertaForTokenClassification''',
     '''FlaxXLMRobertaModel''',
     '''FlaxXLMRobertaPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_xlm_roberta import (
     XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLMRobertaConfig,
     XLMRobertaOnnxConfig,
 )

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta import XLMRobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_roberta import (
      XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMRobertaForCausalLM,
      XLMRobertaForMaskedLM,
      XLMRobertaForMultipleChoice,
      XLMRobertaForQuestionAnswering,
      XLMRobertaForSequenceClassification,
      XLMRobertaForTokenClassification,
      XLMRobertaModel,
      XLMRobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm_roberta import (
      TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMRobertaForCausalLM,
      TFXLMRobertaForMaskedLM,
      TFXLMRobertaForMultipleChoice,
      TFXLMRobertaForQuestionAnswering,
      TFXLMRobertaForSequenceClassification,
      TFXLMRobertaForTokenClassification,
      TFXLMRobertaModel,
      TFXLMRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xlm_roberta import (
      FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxXLMRobertaForCausalLM,
      FlaxXLMRobertaForMaskedLM,
      FlaxXLMRobertaForMultipleChoice,
      FlaxXLMRobertaForQuestionAnswering,
      FlaxXLMRobertaForSequenceClassification,
      FlaxXLMRobertaForTokenClassification,
      FlaxXLMRobertaModel,
      FlaxXLMRobertaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

