






from typing import TYPE_CHECKING

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


lowerCAmelCase_           = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ViTMAEForPreTraining',
					    'ViTMAELayer',
					    'ViTMAEModel',
					    'ViTMAEPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'TFViTMAEForPreTraining',
					    'TFViTMAEModel',
					    'TFViTMAEPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

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













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_           = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						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		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       int=None		)    ->   Any:


						'''simple docstring'''
						A	 =      {}
						A	 =      {}

						if prompt is not None:
										A	 =      prompt

						if generate_kwargs is not None:
										A	 =      generate_kwargs
						if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
														A	 =      {}
										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'		)
										A	 =      max_new_tokens
						return preprocess_params, forward_kwargs, {}
		def __call__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, List[str], "Image.Image", List["Image.Image"]]						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return super().__call__(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None		)    ->   int:


						'''simple docstring'''
						A	 =      load_image(_SCREAMING_SNAKE_CASE		)

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

										A	 =      self.model.config.model_type

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

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

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

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

						else:
										A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

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

						return model_inputs
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Dict=None		)    ->   List[str]:


						'''simple docstring'''
						# 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']						,_SCREAMING_SNAKE_CASE		)
						    and all(x is None for x in model_inputs['input_ids']		)
						):
										A	 =      None

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







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      []
						for output_ids in model_outputs:
										A	 =      {
										    'generated_text': self.tokenizer.decode(
										        _SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE						,)
										}
										records.append(_SCREAMING_SNAKE_CASE		)
						return records





