






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

__UpperCamelCase   :   int      =    logging.get_logger(__name__)


@add_end_docstrings(_lowerCAmelCase )
class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				Any							,					*_lowerCAmelCase   :				Any							,					**_lowerCAmelCase   :				int      )		->			Dict:
		"""simple docstring"""







		super().__init__(*_lowerCAmelCase							,					**_lowerCAmelCase      )
		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 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				List[Any]=None							,					_lowerCAmelCase   :				Any=None							,					_lowerCAmelCase   :				Dict=None      )		->			int:
		"""simple docstring"""







		__lowercase   							=	{}
		__lowercase   							=	{}

		if prompt is not None:
			__lowercase   							=	prompt

		if generate_kwargs is not None:
			__lowercase   							=	generate_kwargs
		if max_new_tokens is not None:
			if "generate_kwargs" not in forward_kwargs:
				__lowercase   							=	{}
			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"""      )
			__lowercase   							=	max_new_tokens
		return preprocess_params, forward_kwargs, {}







	def __call__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Union[str, List[str], "Image.Image", List["Image.Image"]]							,					**_lowerCAmelCase   :				Any      )		->			Tuple:
		"""simple docstring"""







		return super().__call__(_lowerCAmelCase							,					**_lowerCAmelCase      )







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				Any=None      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	load_image(_lowerCAmelCase      )

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

			__lowercase   							=	self.model.config.model_type

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

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

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

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

		else:
			__lowercase   							=	self.image_processor(images=_lowerCAmelCase							,					return_tensors=self.framework      )

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

		return model_inputs







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				List[str]=None      )		->			Tuple:
		"""simple docstring"""







		if (
		    "input_ids" in model_inputs
		    and isinstance(model_inputs["""input_ids"""]							,					_lowerCAmelCase      )
		    and all(x is None for x in model_inputs["""input_ids"""]      )
		):
			__lowercase   							=	None

		if generate_kwargs is None:
			__lowercase   							=	{}
		# 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.
		__lowercase   							=	model_inputs.pop(self.model.main_input_name      )
		__lowercase   							=	self.model.generate(_lowerCAmelCase							,					**_lowerCAmelCase							,					**_lowerCAmelCase      )
		return model_outputs







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







		__lowercase   							=	[]
		for output_ids in model_outputs:
			__lowercase   							=	{
			    """generated_text""": self.tokenizer.decode(
			        _lowerCAmelCase							,					skip_special_tokens=_lowerCAmelCase							,					)
			}
			records.append(_lowerCAmelCase      )
		return records







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:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

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