





'''simple docstring'''




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

a__					:			Tuple          =     logging.get_logger(__name__)






@add_end_docstrings(__magic_name__							)
class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		Dict:
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)
		requires_backends(self      ,       'vision'		)
		self.check_model_type(
		    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING		)





	def   _snake_case   (		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None		)      ->		str:
		snake_case__	       =   {}
		snake_case__	       =   {}

		if prompt is not None:
			snake_case__	       =   prompt

		if generate_kwargs is not None:
			snake_case__	       =   generate_kwargs
		if max_new_tokens is not None:
			if "generate_kwargs" not in forward_kwargs:
				snake_case__	       =   {}
			if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
				raise ValueError(
				    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
				    ' please use only one'		)
			snake_case__	       =   max_new_tokens
		return preprocess_params, forward_kwargs, {}





	def __call__(		self      ,       UpperCamelCase_      ,       **UpperCamelCase_		)      ->		Tuple:
		return super().__call__(UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Optional[int]:
		snake_case__	       =   load_image(UpperCamelCase_		)

		if prompt is not None:
			if not isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
				raise ValueError(
				    F'''Received an invalid text input, got - {type(UpperCamelCase_		)} - but expected a single string. '''
				    'Note also that one single text can be provided for conditional image to text generation.'		)

			snake_case__	       =   self.model.config.model_type

			if model_type == "git":
				snake_case__	       =   self.image_processor(images=UpperCamelCase_      ,       return_tensors=self.framework		)
				snake_case__	       =   self.tokenizer(text=UpperCamelCase_      ,       add_special_tokens=UpperCamelCase_		).input_ids
				snake_case__	       =   [self.tokenizer.cls_token_id] + input_ids
				snake_case__	       =   torch.tensor(UpperCamelCase_		).unsqueeze(0		)
				model_inputs.update({'input_ids': input_ids}		)

			elif model_type == "pix2struct":
				snake_case__	       =   self.image_processor(images=UpperCamelCase_      ,       header_text=UpperCamelCase_      ,       return_tensors=self.framework		)

			elif model_type != "vision-encoder-decoder":
				# vision-encoder-decoder does not support conditional generation
				snake_case__	       =   self.image_processor(images=UpperCamelCase_      ,       return_tensors=self.framework		)
				snake_case__	       =   self.tokenizer(UpperCamelCase_      ,       return_tensors=self.framework		)
				model_inputs.update(UpperCamelCase_		)

			else:
				raise ValueError(F'''Model type {model_type} does not support conditional text generation'''		)

		else:
			snake_case__	       =   self.image_processor(images=UpperCamelCase_      ,       return_tensors=self.framework		)

		if self.model.config.model_type == "git" and prompt is None:
			snake_case__	       =   None

		return model_inputs





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Optional[Any]:
		# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
		# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
		if (
		    "input_ids" in model_inputs
		    and isinstance(model_inputs['input_ids']      ,       UpperCamelCase_		)
		    and all(x is None for x in model_inputs['input_ids']		)
		):
			snake_case__	       =   None

		if generate_kwargs is None:
			snake_case__	       =   {}
		# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
		#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
		#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
		#  in the `_prepare_model_inputs` method.
		snake_case__	       =   model_inputs.pop(self.model.main_input_name		)
		snake_case__	       =   self.model.generate(UpperCamelCase_      ,       **UpperCamelCase_      ,       **UpperCamelCase_		)
		return model_outputs





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   []
		for output_ids in model_outputs:
			snake_case__	       =   {
			    'generated_text': self.tokenizer.decode(
			        UpperCamelCase_      ,       skip_special_tokens=UpperCamelCase_      ,       )
			}
			records.append(UpperCamelCase_		)
		return records










'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



