






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a      	=  {
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CTRLForSequenceClassification''',
		    '''CTRLLMHeadModel''',
		    '''CTRLModel''',
		    '''CTRLPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCTRLForSequenceClassification''',
		    '''TFCTRLLMHeadModel''',
		    '''TFCTRLModel''',
		    '''TFCTRLPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
		from .tokenization_ctrl import CTRLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ctrl import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				    CTRLPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_ctrl import (
				    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCTRLForSequenceClassification,
				    TFCTRLLMHeadModel,
				    TFCTRLModel,
				    TFCTRLPreTrainedModel,
				)

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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 (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""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:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			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

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			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       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

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

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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