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

lowercase_     		= logging.get_logger(__name__)
@add_end_docstrings(__lowercase)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						*__A		,						**__A				) ->     Optional[int]:
								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 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=None				) ->     Tuple:
								_lowerCAmelCase										={}
								_lowerCAmelCase										={}

								if prompt is not None:
														_lowerCAmelCase										=prompt

								if generate_kwargs is not None:
														_lowerCAmelCase										=generate_kwargs
								if max_new_tokens is not None:
														if "generate_kwargs" not in forward_kwargs:
																				_lowerCAmelCase										={}
														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										=max_new_tokens
								return preprocess_params, forward_kwargs, {}






		def __call__(   self		,						__A		,						**__A				) ->     str:
								return super().__call__(__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     Union[str, Any]:
								_lowerCAmelCase										=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										=self.model.config.model_type

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

														elif model_type == "pix2struct":
																				_lowerCAmelCase										=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										=self.image_processor(images=__A		,						return_tensors=self.framework				)
																				_lowerCAmelCase										=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										=self.image_processor(images=__A		,						return_tensors=self.framework				)

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

								return model_inputs






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								# 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']		,						__A				)
								    and all(x is None for x in model_inputs['input_ids']				)
								):
														_lowerCAmelCase										=None

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=[]
								for output_ids in model_outputs:
														_lowerCAmelCase										={
														    'generated_text': self.tokenizer.decode(
														        __A		,						skip_special_tokens=__A		,						)
														}
														records.append(__A				)
								return records






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {'''vocab_file''': '''vocab.txt'''}

lowercase_     		= {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowercase_     		= {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


lowercase_     		= {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      Tuple       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
		lowercase   :      int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      List[str]       =							ConvBertTokenizer






		def __init__(   self		,						__A=None		,						__A=None		,						__A=True		,						__A="[UNK]"		,						__A="[SEP]"		,						__A="[PAD]"		,						__A="[CLS]"		,						__A="[MASK]"		,						__A=True		,						__A=None		,						**__A		,						) ->     Union[str, Any]:
								super().__init__(
								    __A		,						tokenizer_file=__A		,						do_lower_case=__A		,						unk_token=__A		,						sep_token=__A		,						pad_token=__A		,						cls_token=__A		,						mask_token=__A		,						tokenize_chinese_chars=__A		,						strip_accents=__A		,						**__A		,						)

								_lowerCAmelCase										=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'		,						__A				) != do_lower_case
								    or normalizer_state.get('strip_accents'		,						__A				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'		,						__A				) != tokenize_chinese_chars
								):
														_lowerCAmelCase										=getattr(__A		,						normalizer_state.pop('type'				)				)
														_lowerCAmelCase										=do_lower_case
														_lowerCAmelCase										=strip_accents
														_lowerCAmelCase										=tokenize_chinese_chars
														_lowerCAmelCase										=normalizer_class(**__A				)

								_lowerCAmelCase										=do_lower_case






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     int:
								_lowerCAmelCase										=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     List[int]:
								_lowerCAmelCase										=[self.sep_token_id]
								_lowerCAmelCase										=[self.cls_token_id]
								if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     Tuple[str]:
								_lowerCAmelCase										=self._tokenizer.model.save(__A		,						name=__A				)
								return tuple(__A				)





