
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(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      List[str]    ,     *a					:      Tuple    ,     **a					:      List[Any]   ):






							'''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 							_lowerCamelCase		(						self					:      str    ,     a					:      int=None    ,     a					:      Tuple=None    ,     a					:      Dict=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		{}
							lowerCAmelCase__      :      Dict         =		{}

							if prompt is not None:
										lowerCAmelCase__      :      List[str]         =		prompt

							if generate_kwargs is not None:
										lowerCAmelCase__      :      int         =		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					:      Tuple    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      Optional[Any]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		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__      :      Optional[Any]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
													lowerCAmelCase__      :      List[str]         =		self.tokenizer(text=a    ,     add_special_tokens=a   ).input_ids
													lowerCAmelCase__      :      Tuple         =		[self.tokenizer.cls_token_id] + input_ids
													lowerCAmelCase__      :      List[Any]         =		torch.tensor(a   ).unsqueeze(0   )
													model_inputs.update({'input_ids': input_ids}   )

										elif model_type == "pix2struct":
													lowerCAmelCase__      :      Union[str, Any]         =		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__      :      Union[str, Any]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
													lowerCAmelCase__      :      Optional[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__      :      List[str]         =		None

							return model_inputs






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      List[str]    ,     a					:      Any=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__      :      Any         =		None

							if generate_kwargs is None:
										lowerCAmelCase__      :      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.
							lowerCAmelCase__      :      List[Any]         =		model_inputs.pop(self.model.main_input_name   )
							lowerCAmelCase__      :      str         =		self.model.generate(a    ,     **a    ,     **a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Optional[int]   ):






							'''simple docstring'''







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

import os
from distutils.util import strtobool
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			for e in env_keys:
						lowerCAmelCase__      :      Union[str, Any]         =		int(os.environ.get(SCREAMING_SNAKE_CASE_       ,							-1     )     )
						if val >= 0:
									return val
			return default
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return strtobool(SCREAMING_SNAKE_CASE_     ) == 1  # As its name indicates `strtobool` actually returns an int...







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_="no"     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return value