





'''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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

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


	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							False
				super().__init__(_lowerCamelCase       ,_lowerCamelCase	)


	def __call__(self       ,_lowerCamelCase=None       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = False       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = 2048       ,_lowerCamelCase = 0       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = False       ,_lowerCamelCase = False       ,_lowerCamelCase = False       ,_lowerCamelCase = False       ,_lowerCamelCase = False       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							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 and not self.image_processor.is_vqa:
							__lowercase			  =							self.tokenizer
							__lowercase			  =							self.tokenizer(
							    text=_lowerCamelCase       ,add_special_tokens=_lowerCamelCase       ,padding=_lowerCamelCase       ,truncation=_lowerCamelCase       ,max_length=_lowerCamelCase       ,stride=_lowerCamelCase       ,pad_to_multiple_of=_lowerCamelCase       ,return_attention_mask=_lowerCamelCase       ,return_overflowing_tokens=_lowerCamelCase       ,return_special_tokens_mask=_lowerCamelCase       ,return_offsets_mapping=_lowerCamelCase       ,return_token_type_ids=_lowerCamelCase       ,return_length=_lowerCamelCase       ,verbose=_lowerCamelCase       ,return_tensors=_lowerCamelCase       ,**_lowerCamelCase       ,)
							return text_encoding

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

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

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

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

				return encoding_image_processor


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





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


	@property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





