from typing import List, Optional, Union

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


class 						lowercase    (     a     ):
							lowercase__						:       Any								=							["""image_processor""", """tokenizer"""]
							lowercase__						:       Optional[Any]								=							"""BlipImageProcessor"""
							lowercase__						:       Optional[int]								=							"""AutoTokenizer"""




							def __init__(						self   :					List[Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Any      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       False
												super().__init__(_UpperCamelCase	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.image_processor




							def __call__(						self   :					Dict	,							_UpperCamelCase   :					ImageInput = None	,							_UpperCamelCase   :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = False	,							_UpperCamelCase   :					Union[bool, str, TruncationStrategy] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					int = 0	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							**_UpperCamelCase   :					int	,							)					->				BatchEncoding:



												'''simple docstring'''

												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:
																	SCREAMING_SNAKE_CASE    			=       self.tokenizer
																	SCREAMING_SNAKE_CASE    			=       self.tokenizer(
																	    text=_UpperCamelCase	,							add_special_tokens=_UpperCamelCase	,							padding=_UpperCamelCase	,							truncation=_UpperCamelCase	,							max_length=_UpperCamelCase	,							stride=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							return_overflowing_tokens=_UpperCamelCase	,							return_special_tokens_mask=_UpperCamelCase	,							return_offsets_mapping=_UpperCamelCase	,							return_token_type_ids=_UpperCamelCase	,							return_length=_UpperCamelCase	,							verbose=_UpperCamelCase	,							return_tensors=_UpperCamelCase	,							**_UpperCamelCase	,							)
																	return text_encoding

												# add pixel_values
												SCREAMING_SNAKE_CASE    			=       self.image_processor(_UpperCamelCase	,							return_tensors=_UpperCamelCase      )

												if text is not None:
																	SCREAMING_SNAKE_CASE    			=       self.tokenizer(
																	    text=_UpperCamelCase	,							add_special_tokens=_UpperCamelCase	,							padding=_UpperCamelCase	,							truncation=_UpperCamelCase	,							max_length=_UpperCamelCase	,							stride=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							return_overflowing_tokens=_UpperCamelCase	,							return_special_tokens_mask=_UpperCamelCase	,							return_offsets_mapping=_UpperCamelCase	,							return_token_type_ids=_UpperCamelCase	,							return_length=_UpperCamelCase	,							verbose=_UpperCamelCase	,							return_tensors=_UpperCamelCase	,							**_UpperCamelCase	,							)
												else:
																	SCREAMING_SNAKE_CASE    			=       None

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

												return encoding_image_processor




							def 	__snake_case(						self   :					List[Any]	,							*_UpperCamelCase   :					Union[str, Any]	,							**_UpperCamelCase   :					Dict      )					->				Optional[int]:



												'''simple docstring'''

												return self.tokenizer.batch_decode(*_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					str	,							*_UpperCamelCase   :					Optional[Any]	,							**_UpperCamelCase   :					Tuple      )					->				str:



												'''simple docstring'''

												return self.tokenizer.decode(*_UpperCamelCase	,							**_UpperCamelCase      )





							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.tokenizer.model_input_names
												SCREAMING_SNAKE_CASE    			=       self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


_lowerCamelCase					:   List[Any]							=		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
							import torch

							print('''Torch version:''', torch.__version__)
							print('''Cuda available:''', torch.cuda.is_available())
							print('''Cuda version:''', torch.version.cuda)
							print('''CuDNN version:''', torch.backends.cudnn.version())
							print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
							print('''Torch version:''', None)

try:
							import transformers

							print('''transformers version:''', transformers.__version__)
except ImportError:
							print('''transformers version:''', None)




