



'''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

lowerCAmelCase:      str		 =		logging.get_logger(__name__)

@add_end_docstrings(lowerCamelCase__			)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						int		,    *__snake_case				:						Tuple		,    **__snake_case				:						str			):
      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     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Optional[Any]=None		,    __snake_case				:						Optional[Any]=None		,    __snake_case				:						Any=None			):
      a		:      int       =  {}
      a		:      Dict       =  {}

      if prompt is not None:
        a		:      Tuple       =  prompt

      if generate_kwargs is not None:
        a		:      List[Any]       =  generate_kwargs
      if max_new_tokens is not None:
        if "generate_kwargs" not in forward_kwargs:
          a		:      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'			)
        a		:      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				:						Tuple			):
      return super().__call__(__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Any		,    __snake_case				:						List[str]=None			):
      a		:      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.'			)

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

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

        elif model_type == "pix2struct":
          a		:      Optional[Any]       =  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
          a		:      List[Any]       =  self.image_processor(images=__snake_case		,    return_tensors=self.framework			)
          a		:      str       =  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:
        a		:      int       =  self.image_processor(images=__snake_case		,    return_tensors=self.framework			)

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

      return model_inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Tuple		,    __snake_case				:						int=None			):
      # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
      # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
      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']			)
      ):
        a		:      Dict       =  None

      if generate_kwargs is None:
        a		:      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.
      a		:      int       =  model_inputs.pop(self.model.main_input_name			)
      a		:      str       =  self.model.generate(__snake_case		,    **__snake_case		,    **__snake_case			)
      return model_outputs







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




'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def    lowerCamelCase__ (		_A       ):
  return EnvironmentCommand()

class 							a__(      lowerCamelCase__			):







    @staticmethod
    def     lowercase_ (   __snake_case				:						ArgumentParser			):
      a		:      Tuple       =  parser.add_parser('env'			)
      download_parser.set_defaults(func=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  huggingface_hub.__version__

      a		:      List[str]       =  'not installed'
      a		:      List[str]       =  'NA'
      if is_torch_available():
        import torch

        a		:      Optional[Any]       =  torch.__version__
        a		:      int       =  torch.cuda.is_available()

      a		:      Optional[int]       =  'not installed'
      if is_transformers_available():
        import transformers

        a		:      Tuple       =  transformers.__version__

      a		:      Dict       =  'not installed'
      if is_accelerate_available():
        import accelerate

        a		:      int       =  accelerate.__version__

      a		:      Any       =  'not installed'
      if is_xformers_available():
        import xformers

        a		:      Optional[int]       =  xformers.__version__

      a		:      List[str]       =  {
          '`diffusers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
          'Huggingface_hub version': hub_version,
          'Transformers version': transformers_version,
          'Accelerate version': accelerate_version,
          'xFormers version': xformers_version,
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'			)
      print(self.format_dict(__snake_case			)			)

      return info







    @staticmethod
    def     lowercase_ (   __snake_case				:						Union[str, Any]			):
      return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"