


"""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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=['image_processor', 'tokenizer']
		lowerCamelCase__				='Pix2StructImageProcessor'
		lowerCamelCase__				=('T5Tokenizer', 'T5TokenizerFast')



		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				False
			super().__init__(a_				,       a_					)



		def __call__(self				,       a_=None				,       a_ = None				,       a_ = True				,       a_ = False				,       a_ = None				,       a_ = None				,       a_ = 20_48				,       a_ = 0				,       a_ = None				,       a_ = None				,       a_ = False				,       a_ = False				,       a_ = False				,       a_ = False				,       a_ = False				,       a_ = True				,       a_ = None				,       **a_				,       ):





			'''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 and not self.image_processor.is_vqa:
				__snake_case	:						List[Any]						=				self.tokenizer
				__snake_case	:						List[Any]						=				self.tokenizer(
				    text=a_				,       add_special_tokens=a_				,       padding=a_				,       truncation=a_				,       max_length=a_				,       stride=a_				,       pad_to_multiple_of=a_				,       return_attention_mask=a_				,       return_overflowing_tokens=a_				,       return_special_tokens_mask=a_				,       return_offsets_mapping=a_				,       return_token_type_ids=a_				,       return_length=a_				,       verbose=a_				,       return_tensors=a_				,       **a_				,       )
				return text_encoding

			if not self.image_processor.is_vqa:
				# add pixel_values
				__snake_case	:						List[Any]						=				self.image_processor(
				    a_				,       return_tensors=a_				,       max_patches=a_				,       **a_					)
			else:
				# add pixel_values and bbox
				__snake_case	:						Optional[Any]						=				self.image_processor(
				    a_				,       return_tensors=a_				,       max_patches=a_				,       header_text=a_				,       **a_					)

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

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

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

			return encoding_image_processor



		def 				SCREAMING_SNAKE_CASE  (self				,       *a_				,       **a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       *a_				,       **a_					):





			'''simple docstring'''

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.tokenizer.model_input_names
			__snake_case	:						Tuple						=				self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='yolos'



		def __init__(self				,       a_=7_68				,       a_=12				,       a_=12				,       a_=30_72				,       a_="gelu"				,       a_=0.0				,       a_=0.0				,       a_=0.02				,       a_=1E-12				,       a_=[5_12, 8_64]				,       a_=16				,       a_=3				,       a_=True				,       a_=1_00				,       a_=True				,       a_=False				,       a_=1				,       a_=5				,       a_=2				,       a_=5				,       a_=2				,       a_=0.1				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						Union[str, Any]						=				hidden_size
			__snake_case	:						Union[str, Any]						=				num_hidden_layers
			__snake_case	:						Dict						=				num_attention_heads
			__snake_case	:						str						=				intermediate_size
			__snake_case	:						List[str]						=				hidden_act
			__snake_case	:						Tuple						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						Union[str, Any]						=				initializer_range
			__snake_case	:						Tuple						=				layer_norm_eps
			__snake_case	:						List[Any]						=				image_size
			__snake_case	:						Tuple						=				patch_size
			__snake_case	:						str						=				num_channels
			__snake_case	:						Tuple						=				qkv_bias
			__snake_case	:						Union[str, Any]						=				num_detection_tokens
			__snake_case	:						List[str]						=				use_mid_position_embeddings
			__snake_case	:						Tuple						=				auxiliary_loss
			# Hungarian matcher
			__snake_case	:						List[str]						=				class_cost
			__snake_case	:						int						=				bbox_cost
			__snake_case	:						int						=				giou_cost
			# Loss coefficients
			__snake_case	:						Optional[int]						=				bbox_loss_coefficient
			__snake_case	:						List[str]						=				giou_loss_coefficient
			__snake_case	:						List[Any]						=				eos_coefficient



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.11'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return OrderedDict(
			    [
			        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
			    ]					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12





