
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	:	List[str]									=			["""image_processor""", """tokenizer"""]
	_UpperCamelCase	:	Any									=			"""OwlViTImageProcessor"""
	_UpperCamelCase	:	Dict									=			("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(			self						,       snake_case=None						,       snake_case=None						,       **snake_case			):
		lowercase	    =       None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
			    ' instead.'						,       snake_case						,       )
			lowercase	    =       kwargs.pop('feature_extractor'			)

		lowercase	    =       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__(snake_case						,       snake_case			)





	def __call__(			self						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case="max_length"						,       snake_case="np"						,       **snake_case			):

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

			elif isinstance(snake_case						,       snake_case			) and isinstance(text[0]						,       snake_case			):
				lowercase	    =       []

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

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

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

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

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

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

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

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

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

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

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

			lowercase	    =       BatchEncoding()
			lowercase	    =       input_ids
			lowercase	    =       attention_mask

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

		if images is not None:
			lowercase	    =       self.image_processor(snake_case						,       return_tensors=snake_case						,       **snake_case			)

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		return self.image_processor.post_process(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		return self.image_processor.post_process_object_detection(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		return self.image_processor.post_process_image_guided_detection(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		return self.tokenizer.batch_decode(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		return self.tokenizer.decode(*snake_case						,       **snake_case			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		warnings.warn(
		    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,       snake_case						,       )
		return self.image_processor_class





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		warnings.warn(
		    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       snake_case						,       )
		return self.image_processor







from scipy.stats import pearsonr

import datasets


__magic_name__  :  Union[str, Any]					=       """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__magic_name__  :  Tuple					=       """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__magic_name__  :  int					=       """
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION			)
class     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float'''						),
                   '''references''': datasets.Value('''float'''						),
               }						)       ,						reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']       ,						)



     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict=False						):



       """simple docstring"""
       if return_pvalue:
         lowerCAmelCase__										=      pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)[0]						)}

