

"""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

a    :Any			=       logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase__)
class    __a							(UpperCAmelCase__):






     '''simple docstring'''

     def __init__(  self		,			*_a		,			**_a     )     ->					Tuple:


           """simple docstring"""


           super().__init__(*lowercase_		,			**lowercase_     )
           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     _a	(  self		,			_a=None		,			_a=None		,			_a=None     )     ->					Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[Any]											=		{}
           SCREAMING_SNAKE_CASE__      :					Tuple											=		{}

           if prompt is not None:
                 SCREAMING_SNAKE_CASE__      :					Tuple											=		prompt

           if generate_kwargs is not None:
                 SCREAMING_SNAKE_CASE__      :					int											=		generate_kwargs
           if max_new_tokens is not None:
                 if "generate_kwargs" not in forward_kwargs:
                       SCREAMING_SNAKE_CASE__      :					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"""     )
                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		max_new_tokens
           return preprocess_params, forward_kwargs, {}

     def __call__(  self		,			_a		,			**_a     )     ->					Tuple:


           """simple docstring"""


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

     def     _a	(  self		,			_a		,			_a=None     )     ->					Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					int											=		load_image(lowercase_     )

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

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

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

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

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

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

           else:
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		self.image_processor(images=lowercase_		,			return_tensors=self.framework     )

           if self.model.config.model_type == "git" and prompt is None:
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		None

           return model_inputs

     def     _a	(  self		,			_a		,			_a=None     )     ->					Tuple:


           """simple docstring"""


           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs["""input_ids"""]		,			lowercase_     )
               and all(x is None for x in model_inputs["""input_ids"""]     )
           ):
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		None

           if generate_kwargs is None:
                 SCREAMING_SNAKE_CASE__      :					Optional[int]											=		{}
           # 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.
           SCREAMING_SNAKE_CASE__      :					int											=		model_inputs.pop(self.model.main_input_name     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.model.generate(lowercase_		,			**lowercase_		,			**lowercase_     )
           return model_outputs





     def     _a	(  self		,			_a     )     ->					Any:


           """simple docstring"""


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












"""simple docstring"""






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


