





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(lowerCamelCase__  )
class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







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



             """simple docstring"""





             super().__init__(*a__      ,							**a__)
             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							:     List[Any]	=		{}
             _lowerCamelCase							:     Optional[Any]	=		{}

             if prompt is not None:
                    _lowerCamelCase							:     Tuple	=		prompt

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


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



             """simple docstring"""





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


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



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		load_image(a__)

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

                    _lowerCamelCase							:     Dict	=		self.model.config.model_type

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

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

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

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

             else:
                    _lowerCamelCase							:     Tuple	=		self.image_processor(images=a__      ,							return_tensors=self.framework)

             if self.model.config.model_type == "git" and prompt is None:
                    _lowerCamelCase							:     Tuple	=		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''']      ,							a__)
                 and all(x is None for x in model_inputs['''input_ids'''])
             ):
                    _lowerCamelCase							:     Optional[int]	=		None

             if generate_kwargs is None:
                    _lowerCamelCase							:     Tuple	=		{}
             # 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							:     Optional[Any]	=		model_inputs.pop(self.model.main_input_name)
             _lowerCamelCase							:     Any	=		self.model.generate(a__      ,							**a__      ,							**a__)
             return model_outputs



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





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








from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

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

