

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 					__UpperCAmelCase     (       lowerCamelCase__						):



   def __init__(							self							:     Union[str, Any],		*__A							:     List[Any],		**__A							:     List[str]						):
     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        __magic_name__    (							self							:     Dict,		__A							:     Union[str, Any]=None,		__A							:     str=None,		__A							:     List[str]=None						):
     UpperCAmelCase       :							Any  = {}
     UpperCAmelCase       :							str  = {}

     if prompt is not None:
       UpperCAmelCase       :							List[Any]  = prompt

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



   def __call__(							self							:     Tuple,		__A							:     Union[str, List[str], "Image.Image", List["Image.Image"]],		**__A							:     Dict						):
     return super().__call__(SCREAMING_SNAKE_CASE_,		**SCREAMING_SNAKE_CASE_						)



   def        __magic_name__    (							self							:     int,		__A							:     Tuple,		__A							:     int=None						):
     UpperCAmelCase       :							Union[str, Any]  = 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.'''						)

       UpperCAmelCase       :							Optional[Any]  = self.model.config.model_type

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

       elif model_type == "pix2struct":
         UpperCAmelCase       :							List[Any]  = 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
         UpperCAmelCase       :							List[str]  = self.image_processor(images=SCREAMING_SNAKE_CASE_,		return_tensors=self.framework						)
         UpperCAmelCase       :							Dict  = 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:
       UpperCAmelCase       :							Any  = self.image_processor(images=SCREAMING_SNAKE_CASE_,		return_tensors=self.framework						)

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

     return model_inputs



   def        __magic_name__    (							self							:     List[Any],		__A							:     List[Any],		__A							:     List[str]=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'''],		SCREAMING_SNAKE_CASE_						)
         and all(x is None for x in model_inputs['''input_ids''']						)
     ):
       UpperCAmelCase       :							Union[str, Any]  = None

     if generate_kwargs is None:
       UpperCAmelCase       :							List[str]  = {}
     # 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.
     UpperCAmelCase       :							Any  = model_inputs.pop(self.model.main_input_name						)
     UpperCAmelCase       :							List[Any]  = self.model.generate(SCREAMING_SNAKE_CASE_,		**SCREAMING_SNAKE_CASE_,		**SCREAMING_SNAKE_CASE_						)
     return model_outputs





   def        __magic_name__    (							self							:     Tuple,		__A							:     Optional[int]						):
     UpperCAmelCase       :							Optional[int]  = []
     for output_ids in model_outputs:
       UpperCAmelCase       :							List[str]  = {
           '''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)




