




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    :		Dict			   =       logging.get_logger(__name__)







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



	'''simple docstring'''


	def __init__(    self :  str						,							*A_ :  Optional[Any]						,							**A_ :  str  )       ->     int:

							"""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 :  List[str]						,							A_ :  List[Any]=None						,							A_ :  Any=None						,							A_ :  Union[str, Any]=None  )       ->     Dict:

							"""simple docstring"""






							lowerCamelCase_							=   {}
							lowerCamelCase_							=   {}

							if prompt is not None:
													lowerCamelCase_							=   prompt

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

	def __call__(    self :  Dict						,							A_ :  Union[str, List[str], "Image.Image", List["Image.Image"]]						,							**A_ :  Dict  )       ->     Dict:

							"""simple docstring"""






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

	def     a__			(    self :  List[Any]						,							A_ :  str						,							A_ :  Optional[Any]=None  )       ->     Optional[Any]:

							"""simple docstring"""






							lowerCamelCase_							=   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.'  )

													lowerCamelCase_							=   self.model.config.model_type

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

													elif model_type == "pix2struct":
																			lowerCamelCase_							=   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
																			lowerCamelCase_							=   self.image_processor(images=A_						,							return_tensors=self.framework  )
																			lowerCamelCase_							=   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:
													lowerCamelCase_							=   self.image_processor(images=A_						,							return_tensors=self.framework  )

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

							return model_inputs

	def     a__			(    self :  Optional[Any]						,							A_ :  List[Any]						,							A_ :  Union[str, Any]=None  )       ->     List[str]:

							"""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']  )
							):
													lowerCamelCase_							=   None

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





	def     a__			(    self :  Optional[int]						,							A_ :  int  )       ->     str:

							"""simple docstring"""






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









import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''OwlViTImageProcessor'''
 UpperCamelCase						       =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'						,							A_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(A_						,							A_  )

 def __call__(    self :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       if text is None and query_images is None and images is None:
             raise ValueError(
                 'You have to specify at least one text or query image or image. All three cannot be none.'  )

       if text is not None:
             if isinstance(A_						,							A_  ) or (isinstance(A_						,							A_  ) and not isinstance(text[0]						,							A_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

                   # Maximum number of queries across batch
                   lowerCamelCase_							=   max([len(A_  ) for t in text]  )

                   # Pad all batch samples to max number of text queries
                   for t in text:
                         if len(A_  ) != max_num_queries:
                               lowerCamelCase_							=   t + [' '] * (max_num_queries - len(A_  ))

                         lowerCamelCase_							=   self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )
                         encodings.append(A_  )
             else:
                   raise TypeError('Input text should be a string, a list of strings or a nested list of strings'  )

             if return_tensors == "np":
                   lowerCamelCase_							=   np.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   np.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "jax" and is_flax_available():
                   import jax.numpy as jnp

                   lowerCamelCase_							=   jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "pt" and is_torch_available():
                   import torch

                   lowerCamelCase_							=   torch.cat([encoding['input_ids'] for encoding in encodings]						,							dim=0  )
                   lowerCamelCase_							=   torch.cat([encoding['attention_mask'] for encoding in encodings]						,							dim=0  )

             elif return_tensors == "tf" and is_tf_available():
                   import tensorflow as tf

                   lowerCamelCase_							=   tf.stack([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   tf.stack([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             else:
                   raise ValueError('Target return tensor type could not be returned'  )

             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   input_ids
             lowerCamelCase_							=   attention_mask

       if query_images is not None:
             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   self.image_processor(
                 A_						,							return_tensors=A_						,							**A_  ).pixel_values
             lowerCamelCase_							=   query_pixel_values

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       if text is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif query_images is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif text is not None or query_images is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**A_  )						,							tensor_type=A_  )

 def     a__			(    self :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

 def     a__			(    self :  List[str]						,							*A_ :  Any						,							**A_ :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,							A_						,							)
       return self.image_processor_class





 @property
 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,							A_						,							)
       return self.image_processor



