



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

lowerCAmelCase__		:					List[str]							=logging.get_logger(__name__)

@add_end_docstrings(UpperCamelCase_	)
class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)
													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				,							_A=None				,							_A=None				,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {}
													__SCREAMING_SNAKE_CASE									= {}

													if prompt is not None:
																				__SCREAMING_SNAKE_CASE									= prompt

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






						def __call__(   self				,							_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(_A		)

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

																				__SCREAMING_SNAKE_CASE									= self.model.config.model_type

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

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

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

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

													else:
																				__SCREAMING_SNAKE_CASE									= self.image_processor(images=_A				,							return_tensors=self.framework		)

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

													return model_inputs






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if (
													    "input_ids" in model_inputs
													    and isinstance(model_inputs['input_ids']				,							_A		)
													    and all(x is None for x in model_inputs['input_ids']		)
													):
																				__SCREAMING_SNAKE_CASE									= None

													if generate_kwargs is None:
																				__SCREAMING_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.
													__SCREAMING_SNAKE_CASE									= model_inputs.pop(self.model.main_input_name		)
													__SCREAMING_SNAKE_CASE									= self.model.generate(_A				,							**_A				,							**_A		)
													return model_outputs






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													for output_ids in model_outputs:
																				__SCREAMING_SNAKE_CASE									= {
																				    'generated_text': self.tokenizer.decode(
																				        _A				,							skip_special_tokens=_A				,							)
																				}
																				records.append(_A		)
													return records











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

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






