





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(snake_case__  )
class 			__A       (      snake_case__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             super().__init__(*UpperCAmelCase_      ,							**UpperCAmelCase_)
             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      ,							a__=None      ,							a__=None      ,							a__=None):



             """simple docstring"""





             _lowerCamelCase							:     str	=		{}
             _lowerCamelCase							:     Dict	=		{}

             if prompt is not None:
                    _lowerCamelCase							:     Tuple	=		prompt

             if generate_kwargs is not None:
                    _lowerCamelCase							:     List[str]	=		generate_kwargs
             if max_new_tokens is not None:
                    if "generate_kwargs" not in forward_kwargs:
                           _lowerCamelCase							:     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''')
                    _lowerCamelCase							:     Dict	=		max_new_tokens
             return preprocess_params, forward_kwargs, {}


      def __call__(		self      ,							a__      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__      ,							a__=None):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		load_image(UpperCAmelCase_)

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

                    _lowerCamelCase							:     Union[str, Any]	=		self.model.config.model_type

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

                    elif model_type == "pix2struct":
                           _lowerCamelCase							:     str	=		self.image_processor(images=UpperCAmelCase_      ,							header_text=UpperCAmelCase_      ,							return_tensors=self.framework)

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

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

             else:
                    _lowerCamelCase							:     Optional[int]	=		self.image_processor(images=UpperCAmelCase_      ,							return_tensors=self.framework)

             if self.model.config.model_type == "git" and prompt is None:
                    _lowerCamelCase							:     str	=		None

             return model_inputs


      def 				__snake_case			(		self      ,							a__      ,							a__=None):



             """simple docstring"""





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

             if generate_kwargs is None:
                    _lowerCamelCase							:     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.
             _lowerCamelCase							:     Union[str, Any]	=		model_inputs.pop(self.model.main_input_name)
             _lowerCamelCase							:     Optional[int]	=		self.model.generate(UpperCAmelCase_      ,							**UpperCAmelCase_      ,							**UpperCAmelCase_)
             return model_outputs



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		[]
             for output_ids in model_outputs:
                    _lowerCamelCase							:     Dict	=		{
                        '''generated_text''': self.tokenizer.decode(
                            UpperCAmelCase_      ,							skip_special_tokens=UpperCAmelCase_      ,							)
                    }
                    records.append(UpperCAmelCase_)
             return records








from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaPreLayerNormForCausalLM',
           'RobertaPreLayerNormForMaskedLM',
           'RobertaPreLayerNormForMultipleChoice',
           'RobertaPreLayerNormForQuestionAnswering',
           'RobertaPreLayerNormForSequenceClassification',
           'RobertaPreLayerNormForTokenClassification',
           'RobertaPreLayerNormModel',
           'RobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaPreLayerNormForCausalLM',
           'TFRobertaPreLayerNormForMaskedLM',
           'TFRobertaPreLayerNormForMultipleChoice',
           'TFRobertaPreLayerNormForQuestionAnswering',
           'TFRobertaPreLayerNormForSequenceClassification',
           'TFRobertaPreLayerNormForTokenClassification',
           'TFRobertaPreLayerNormMainLayer',
           'TFRobertaPreLayerNormModel',
           'TFRobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxRobertaPreLayerNormForCausalLM',
           'FlaxRobertaPreLayerNormForMaskedLM',
           'FlaxRobertaPreLayerNormForMultipleChoice',
           'FlaxRobertaPreLayerNormForQuestionAnswering',
           'FlaxRobertaPreLayerNormForSequenceClassification',
           'FlaxRobertaPreLayerNormForTokenClassification',
           'FlaxRobertaPreLayerNormModel',
           'FlaxRobertaPreLayerNormPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

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

