




"""simple docstring"""







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :  Optional[int]					=      logging.get_logger(__name__)

__A   :  Tuple					=      {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Union[str, Any]       =      "pix2struct_text_model"
				__UpperCAmelCase :     List[str]       =      ["past_key_values"]
				__UpperCAmelCase :     List[Any]       =      {
				    "hidden_size": "hidden_size",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}



				def __init__(     self					:	int    ,   lowercase__					:	Dict=5_0_2_4_4    ,   lowercase__					:	int=7_6_8    ,   lowercase__					:	Optional[Any]=6_4    ,   lowercase__					:	Any=2_0_4_8    ,   lowercase__					:	Tuple=1_2    ,   lowercase__					:	List[str]=1_2    ,   lowercase__					:	int=3_2    ,   lowercase__					:	Tuple=1_2_8    ,   lowercase__					:	Union[str, Any]=0.1    ,   lowercase__					:	List[str]=1e-6    ,   lowercase__					:	Tuple=1.0    ,   lowercase__					:	str="gelu_new"    ,   lowercase__					:	Optional[int]=0    ,   lowercase__					:	int=False    ,   lowercase__					:	Optional[int]=0    ,   lowercase__					:	List[Any]=1    ,   lowercase__					:	Optional[int]=False    ,   lowercase__					:	Optional[Any]=True    ,   **lowercase__					:	Optional[int]    ,   ):
						__lowercase				:		Dict												=				vocab_size
						__lowercase				:		Optional[int]												=				hidden_size
						__lowercase				:		Tuple												=				d_kv
						__lowercase				:		Any												=				d_ff
						__lowercase				:		Dict												=				num_layers
						__lowercase				:		Union[str, Any]												=				num_heads
						__lowercase				:		Tuple												=				relative_attention_num_buckets
						__lowercase				:		int												=				relative_attention_max_distance
						__lowercase				:		Tuple												=				dropout_rate
						__lowercase				:		str												=				layer_norm_epsilon
						__lowercase				:		List[Any]												=				initializer_factor
						__lowercase				:		List[Any]												=				use_cache

						__lowercase				:		int												=				eos_token_id
						__lowercase				:		Optional[Any]												=				decoder_start_token_id

						# for backwards compatibility
						__lowercase				:		str												=				dense_act_fn

						super().__init__(
						    pad_token_id=__UpperCamelCase    ,   eos_token_id=__UpperCamelCase    ,   decoder_start_token_id=__UpperCamelCase    ,   tie_word_embeddings=__UpperCamelCase    ,   is_decoder=__UpperCamelCase    ,   **__UpperCamelCase    ,   )



				@classmethod
				def       snake_case    (     cls					:	str    ,   lowercase__					:	Union[str, os.PathLike]    ,   **lowercase__					:	Dict						):
						cls._set_token_in_kwargs(__UpperCamelCase						)

						__lowercase						,__lowercase				:		List[Any]												=				cls.get_config_dict(__UpperCamelCase    ,   **__UpperCamelCase						)

						# get the text config dict if we are loading from Pix2StructConfig
						if config_dict.get("model_type"						) == "pix2struct":
								__lowercase				:		Optional[Any]												=				config_dict["text_config"]

						if "model_type" in config_dict and hasattr(cls    ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

						return cls.from_dict(__UpperCamelCase    ,   **__UpperCamelCase						)





class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      "pix2struct_vision_model"



				def __init__(     self					:	Optional[Any]    ,   lowercase__					:	Optional[Any]=7_6_8    ,   lowercase__					:	Any=7_6_8    ,   lowercase__					:	Tuple=2_0_4_8    ,   lowercase__					:	List[Any]=6_4    ,   lowercase__					:	Dict=1_2    ,   lowercase__					:	Dict=1_2    ,   lowercase__					:	Union[str, Any]="gelu_new"    ,   lowercase__					:	Optional[int]=1e-6    ,   lowercase__					:	str=0.0    ,   lowercase__					:	int=0.0    ,   lowercase__					:	Optional[int]=1e-10    ,   lowercase__					:	Any=1.0    ,   lowercase__					:	List[str]=4_0_9_6    ,   lowercase__					:	Any=3_2    ,   lowercase__					:	List[Any]=1_2_8    ,   **lowercase__					:	Union[str, Any]    ,   ):
						super().__init__(**__UpperCamelCase						)

						__lowercase				:		Tuple												=				hidden_size
						__lowercase				:		int												=				patch_embed_hidden_size
						__lowercase				:		str												=				d_ff
						__lowercase				:		Tuple												=				dropout_rate
						__lowercase				:		Optional[int]												=				num_hidden_layers
						__lowercase				:		str												=				num_attention_heads
						__lowercase				:		List[Any]												=				initializer_range
						__lowercase				:		Dict												=				initializer_factor
						__lowercase				:		List[Any]												=				attention_dropout
						__lowercase				:		Tuple												=				layer_norm_eps
						__lowercase				:		Union[str, Any]												=				dense_act_fn
						__lowercase				:		List[str]												=				seq_len
						__lowercase				:		str												=				relative_attention_num_buckets
						__lowercase				:		Optional[int]												=				relative_attention_max_distance
						__lowercase				:		Dict												=				d_kv



				@classmethod
				def       snake_case    (     cls					:	Optional[Any]    ,   lowercase__					:	Union[str, os.PathLike]    ,   **lowercase__					:	List[Any]						):
						cls._set_token_in_kwargs(__UpperCamelCase						)

						__lowercase						,__lowercase				:		Optional[int]												=				cls.get_config_dict(__UpperCamelCase    ,   **__UpperCamelCase						)

						# get the vision config dict if we are loading from Pix2StructConfig
						if config_dict.get("model_type"						) == "pix2struct":
								__lowercase				:		List[Any]												=				config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls    ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

						return cls.from_dict(__UpperCamelCase    ,   **__UpperCamelCase						)





class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     int       =      "pix2struct"
				__UpperCAmelCase :     List[Any]       =      True



				def __init__(     self					:	Optional[Any]    ,   lowercase__					:	int=None    ,   lowercase__					:	Union[str, Any]=None    ,   lowercase__					:	Dict=1.0    ,   lowercase__					:	List[Any]=0.0_2    ,   lowercase__					:	Dict=False    ,   lowercase__					:	Tuple=False    ,   lowercase__					:	int=True    ,   **lowercase__					:	int    ,   ):
						super().__init__(tie_word_embeddings=__UpperCamelCase    ,   is_encoder_decoder=__UpperCamelCase    ,   **__UpperCamelCase						)

						if text_config is None:
								__lowercase				:		List[Any]												=				{}
								logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."						)

						if vision_config is None:
								__lowercase				:		str												=				{}
								logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."						)

						__lowercase				:		str												=				PixaStructTextConfig(**__UpperCamelCase						)
						__lowercase				:		Dict												=				PixaStructVisionConfig(**__UpperCamelCase						)

						__lowercase				:		Optional[Any]												=				self.text_config.decoder_start_token_id
						__lowercase				:		int												=				self.text_config.pad_token_id
						__lowercase				:		List[str]												=				self.text_config.eos_token_id

						__lowercase				:		Optional[Any]												=				initializer_factor
						__lowercase				:		List[Any]												=				initializer_range

						__lowercase				:		int												=				self.initializer_range
						__lowercase				:		int												=				self.initializer_range

						__lowercase				:		int												=				is_vqa



				@classmethod
				def       snake_case    (     cls					:	List[str]    ,   lowercase__					:	PixaStructTextConfig    ,   lowercase__					:	PixaStructVisionConfig    ,   **lowercase__					:	int						):
						return cls(text_config=text_config.to_dict()    ,   vision_config=vision_config.to_dict()    ,   **__UpperCamelCase						)



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		str												=				copy.deepcopy(self.__dict__						)
						__lowercase				:		str												=				self.text_config.to_dict()
						__lowercase				:		Dict												=				self.vision_config.to_dict()
						__lowercase				:		str												=				self.__class__.model_type
						return output






"""simple docstring"""







from scipy.stats import spearmanr

import datasets


__A   :  Optional[int]					=      '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

__A   :  List[Any]					=      '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

__A   :  Optional[int]					=      R'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,			_KWARGS_DESCRIPTION    )
class       lowerCAmelCase__       (			datasets.Metric    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						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.spearmanr.html"]    ,   )



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	str    ,   lowercase__					:	Any    ,   lowercase__					:	int=False						):
						__lowercase				:		Optional[int]												=				spearmanr(lowercase__    ,   lowercase__						)
						if return_pvalue:
								return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
								return {"spearmanr": results[0]}
