
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  			=					logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							*SCREAMING_SNAKE_CASE_  :			List[Any]       ,							**SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Optional[int]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_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  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=None       ,							SCREAMING_SNAKE_CASE_  :			str=None       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       )       ->     List[Any]:
					lowercase_       =					{}
					lowercase_       =					{}

					if prompt is not None:
									lowercase_       =					prompt

					if generate_kwargs is not None:
									lowercase_       =					generate_kwargs
					if max_new_tokens is not None:
									if "generate_kwargs" not in forward_kwargs:
													lowercase_       =					{}
									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'''       )
									lowercase_       =					max_new_tokens
					return preprocess_params, forward_kwargs, {}
	def __call__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Union[str, List[str], "Image.Image", List["Image.Image"]]       ,							**SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Dict:
					return super().__call__(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int=None       )       ->     Dict:
					lowercase_       =					load_image(SCREAMING_SNAKE_CASE_       )

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

									lowercase_       =					self.model.config.model_type

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

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

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

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

					else:
									lowercase_       =					self.image_processor(images=SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )

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

					return model_inputs
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       )       ->     Tuple:
					# 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''']       ,							SCREAMING_SNAKE_CASE_       )
					    and all(x is None for x in model_inputs['''input_ids''']       )
					):
									lowercase_       =					None

					if generate_kwargs is None:
									lowercase_       =					{}
					# 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.
					lowercase_       =					model_inputs.pop(self.model.main_input_name       )
					lowercase_       =					self.model.generate(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					return model_outputs




	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Any:
					lowercase_       =					[]
					for output_ids in model_outputs:
									lowercase_       =					{
									    '''generated_text''': self.tokenizer.decode(
									        SCREAMING_SNAKE_CASE_       ,							skip_special_tokens=SCREAMING_SNAKE_CASE_       ,							)
									}
									records.append(SCREAMING_SNAKE_CASE_       )
					return records






import argparse
from collections import defaultdict

import yaml


__a  			=					'docs/source/en/_toctree.yml'




def 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					defaultdict(snake_case__					)
				for doc in model_doc:
								counts[doc["local"]] += 1
				lowercase_       =					[key for key, value in counts.items() if value > 1]

				lowercase_       =					[]
				for duplicate_key in duplicates:
								lowercase_       =					list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
								if len(snake_case__					) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.'''					)
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

				# Sort
				return sorted(snake_case__						,   key=lambda snake_case__					: s["title"].lower()					)




def 				a						(							snake_case__:					List[Any]=False					):


				'''simple docstring'''
				with open(snake_case__						,   encoding='''utf-8'''					) as f:
								lowercase_       =					yaml.safe_load(f.read()					)

				# Get to the API doc
				lowercase_       =					0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				lowercase_       =					content[api_idx]['''sections''']

				# Then to the model doc
				lowercase_       =					0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				lowercase_       =					api_doc[model_idx]['''sections''']

				lowercase_       =					[(idx, section) for idx, section in enumerate(snake_case__					) if '''sections''' in section]
				lowercase_       =					False
				for idx, modality_doc in modalities_docs:
								lowercase_       =					modality_doc['''sections''']
								lowercase_       =					clean_model_doc_toc(snake_case__					)

								if old_modality_doc != new_modality_doc:
												lowercase_       =					True
												if overwrite:
																lowercase_       =					new_modality_doc

				if diff:
								if overwrite:
												lowercase_       =					model_doc
												lowercase_       =					api_doc
												with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
																f.write(yaml.dump(snake_case__						,   allow_unicode=snake_case__					)					)
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a  			=					parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




