



"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCAmelCase							:       List[str]          =       {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       str          =       ['''MobileViTFeatureExtractor''']
					__lowerCAmelCase							:       Optional[int]          =       ['''MobileViTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       Optional[int]          =       [
					    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MobileViTForImageClassification''',
					    '''MobileViTForSemanticSegmentation''',
					    '''MobileViTModel''',
					    '''MobileViTPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       Union[str, Any]          =       [
					    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFMobileViTForImageClassification''',
					    '''TFMobileViTForSemanticSegmentation''',
					    '''TFMobileViTModel''',
					    '''TFMobileViTPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilevit import MobileViTFeatureExtractor
										from .image_processing_mobilevit import MobileViTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevit import (
										    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTForImageClassification,
										    MobileViTForSemanticSegmentation,
										    MobileViTModel,
										    MobileViTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mobilevit import (
										    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMobileViTForImageClassification,
										    TFMobileViTForSemanticSegmentation,
										    TFMobileViTModel,
										    TFMobileViTPreTrainedModel,
										)


else:
					import sys

					__lowerCAmelCase							:       Tuple          =       _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

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    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     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _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					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       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.'''      )

       _A      =       self.model.config.model_type

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

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

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

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # 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''']			,						__lowerCAmelCase      )
        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(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



