




"""simple docstring"""







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[Any]       =      ["image_processor", "tokenizer"]
				__UpperCAmelCase :     Optional[Any]       =      "Pix2StructImageProcessor"
				__UpperCAmelCase :     Tuple       =      ("T5Tokenizer", "T5TokenizerFast")



				def __init__(     self					:	int    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[Any]						):
						__lowercase				:		Union[str, Any]												=				False
						super().__init__(lowercase__    ,   lowercase__						)



				def __call__(     self					:	List[Any]    ,   lowercase__					:	Any=None    ,   lowercase__					:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   lowercase__					:	bool = True    ,   lowercase__					:	Union[bool, str, PaddingStrategy] = False    ,   lowercase__					:	Union[bool, str, TruncationStrategy] = None    ,   lowercase__					:	Optional[int] = None    ,   lowercase__					:	Optional[int] = 2_0_4_8    ,   lowercase__					:	int = 0    ,   lowercase__					:	Optional[int] = None    ,   lowercase__					:	Optional[bool] = None    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = True    ,   lowercase__					:	Optional[Union[str, TensorType]] = None    ,   **lowercase__					:	int    ,   ):
						if images is None and text is None:
								raise ValueError("You have to specify either images or text."						)

						# Get only text
						if images is None and not self.image_processor.is_vqa:
								__lowercase				:		int												=				self.tokenizer
								__lowercase				:		Optional[int]												=				self.tokenizer(
								    text=lowercase__    ,   add_special_tokens=lowercase__    ,   padding=lowercase__    ,   truncation=lowercase__    ,   max_length=lowercase__    ,   stride=lowercase__    ,   pad_to_multiple_of=lowercase__    ,   return_attention_mask=lowercase__    ,   return_overflowing_tokens=lowercase__    ,   return_special_tokens_mask=lowercase__    ,   return_offsets_mapping=lowercase__    ,   return_token_type_ids=lowercase__    ,   return_length=lowercase__    ,   verbose=lowercase__    ,   return_tensors=lowercase__    ,   **lowercase__    ,   )
								return text_encoding

						if not self.image_processor.is_vqa:
								# add pixel_values
								__lowercase				:		Union[str, Any]												=				self.image_processor(
								    lowercase__    ,   return_tensors=lowercase__    ,   max_patches=lowercase__    ,   **lowercase__						)
						else:
								# add pixel_values and bbox
								__lowercase				:		Optional[int]												=				self.image_processor(
								    lowercase__    ,   return_tensors=lowercase__    ,   max_patches=lowercase__    ,   header_text=lowercase__    ,   **lowercase__						)

						if text is not None and not self.image_processor.is_vqa:
								__lowercase				:		str												=				self.tokenizer(
								    text=lowercase__    ,   add_special_tokens=lowercase__    ,   padding=lowercase__    ,   truncation=lowercase__    ,   max_length=lowercase__    ,   stride=lowercase__    ,   pad_to_multiple_of=lowercase__    ,   return_attention_mask=lowercase__    ,   return_overflowing_tokens=lowercase__    ,   return_special_tokens_mask=lowercase__    ,   return_offsets_mapping=lowercase__    ,   return_token_type_ids=lowercase__    ,   return_length=lowercase__    ,   verbose=lowercase__    ,   return_tensors=lowercase__    ,   **lowercase__    ,   )

								if "attention_mask" in text_encoding:
										__lowercase				:		List[Any]												=				text_encoding.pop("attention_mask"						)
								if "input_ids" in text_encoding:
										__lowercase				:		Any												=				text_encoding.pop("input_ids"						)
						else:
								__lowercase				:		List[Any]												=				None

						if text_encoding is not None:
								encoding_image_processor.update(lowercase__						)

						return encoding_image_processor



				def       snake_case    (     self					:	Any    ,   *lowercase__					:	List[str]    ,   **lowercase__					:	Tuple						):
						return self.tokenizer.batch_decode(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Union[str, Any]    ,   *lowercase__					:	Optional[int]    ,   **lowercase__					:	Any						):
						return self.tokenizer.decode(*lowercase__    ,   **lowercase__						)



				@property
				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		List[str]												=				self.tokenizer.model_input_names
						__lowercase				:		Dict												=				self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__A   :  int					=      '\nHuman: <<task>>\n\nAssistant: '


__A   :  List[str]					=      'huggingface-tools/default-prompts'
__A   :  Tuple					=      {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="run"				)			->Optional[int]:






		"""simple docstring"""


		if prompt_or_repo_id is None:
				__lowercase				:		Any												=				DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search("\\s",  _lowerCamelCase				) is not None:
				return prompt_or_repo_id

		__lowercase				:		Optional[Any]												=				cached_file(
		    _lowerCamelCase,  PROMPT_FILES[mode],  repo_type="dataset",  user_agent={"agent": agent_name}				)
		with open(_lowerCamelCase,  "r",  encoding="utf-8"				) as f:
				return f.read()
