



"""simple docstring"""


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 (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""OwlViTImageProcessor"""
			UpperCamelCase__			      =	("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(		self     :    str				,   __UpperCamelCase     :    Any=None				,   __UpperCamelCase     :    Tuple=None				,   **__UpperCamelCase     :    Tuple   )->Tuple:
							_UpperCAmelCase					=    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,   __UpperCamelCase				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    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__(__UpperCamelCase				,   __UpperCamelCase   )



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

							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(__UpperCamelCase				,   __UpperCamelCase   ) or (isinstance(__UpperCamelCase				,   __UpperCamelCase   ) and not isinstance(text[0]				,   __UpperCamelCase   )):
															_UpperCAmelCase					=    [self.tokenizer(__UpperCamelCase				,   padding=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )]

											elif isinstance(__UpperCamelCase				,   __UpperCamelCase   ) and isinstance(text[0]				,   __UpperCamelCase   ):
															_UpperCAmelCase					=    []

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

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

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

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

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

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

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

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

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

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

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

											_UpperCAmelCase					=    BatchEncoding()
											_UpperCAmelCase					=    input_ids
											_UpperCAmelCase					=    attention_mask

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

							if images is not None:
											_UpperCAmelCase					=    self.image_processor(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

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



			def  lowercase__						(		self     :    List[str]				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    Optional[Any]   )->Optional[int]:
							return self.image_processor.post_process(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   *__UpperCamelCase     :    List[str]				,   **__UpperCamelCase     :    Optional[int]   )->Optional[Any]:
							return self.image_processor.post_process_object_detection(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   *__UpperCamelCase     :    List[Any]				,   **__UpperCamelCase     :    Dict   )->str:
							return self.image_processor.post_process_image_guided_detection(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    str				,   *__UpperCamelCase     :    int				,   **__UpperCamelCase     :    Optional[int]   )->List[Any]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   *__UpperCamelCase     :    int				,   **__UpperCamelCase     :    Optional[int]   )->str:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			@property
			def  lowercase__						(		self     :    Any   )->Optional[int]:
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor_class



			@property
			def  lowercase__						(		self     :    Optional[Any]   )->Any:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor








"""simple docstring"""


import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__A   :							Any										=   "\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


__A   :							Optional[Any]										=   "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


__A   :							Tuple										=   r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION)
class 			_a (					datasets.Metric):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''   ),
							            '''references''': datasets.Value('''string'''   ),
							        }   )				,   homepage='''https://github.com/hendrycks/math'''				,   codebase_urls=['''https://github.com/hendrycks/math''']				,   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    0.0
							for i, j in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											n_correct += 1.0 if math_equivalence.is_equiv(__UpperCamelCase				,   __UpperCamelCase   ) else 0.0
							_UpperCAmelCase					=    n_correct / len(__UpperCamelCase   )
							return {
							    "accuracy": accuracy,
							}



