




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""image_processor""", """tokenizer"""]
						UpperCamelCase      =       """Pix2StructImageProcessor"""
						UpperCamelCase      =       ("""T5Tokenizer""", """T5TokenizerFast""")

						def __init__(		self :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=False
										super().__init__(__snake_case     ,			__snake_case			)

						def __call__(		self :Any     ,			__snake_case :List[Any]=None     ,			__snake_case :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,			__snake_case :bool = True     ,			__snake_case :Union[bool, str, PaddingStrategy] = False     ,			__snake_case :Union[bool, str, TruncationStrategy] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = 20_48     ,			__snake_case :int = 0     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = True     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			**__snake_case :Any     ,			):
										'''simple docstring'''

										if images is None and text is None:
														raise ValueError("""You have to specify either images or text."""			)

										# Get only text
										if images is None and not self.image_processor.is_vqa:
														__magic_name__						:						Any										=self.tokenizer
														__magic_name__						:						Dict										=self.tokenizer(
														    text=__snake_case     ,			add_special_tokens=__snake_case     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=__snake_case     ,			stride=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			return_overflowing_tokens=__snake_case     ,			return_special_tokens_mask=__snake_case     ,			return_offsets_mapping=__snake_case     ,			return_token_type_ids=__snake_case     ,			return_length=__snake_case     ,			verbose=__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case     ,			)
														return text_encoding

										if not self.image_processor.is_vqa:
														# add pixel_values
														__magic_name__						:						Dict										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			**__snake_case			)
										else:
														# add pixel_values and bbox
														__magic_name__						:						Optional[int]										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			header_text=__snake_case     ,			**__snake_case			)

										if text is not None and not self.image_processor.is_vqa:
														__magic_name__						:						int										=self.tokenizer(
														    text=__snake_case     ,			add_special_tokens=__snake_case     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=__snake_case     ,			stride=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			return_overflowing_tokens=__snake_case     ,			return_special_tokens_mask=__snake_case     ,			return_offsets_mapping=__snake_case     ,			return_token_type_ids=__snake_case     ,			return_length=__snake_case     ,			verbose=__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case     ,			)

														if "attention_mask" in text_encoding:
																		__magic_name__						:						Tuple										=text_encoding.pop("""attention_mask"""			)
														if "input_ids" in text_encoding:
																		__magic_name__						:						List[str]										=text_encoding.pop("""input_ids"""			)
										else:
														__magic_name__						:						Any										=None

										if text_encoding is not None:
														encoding_image_processor.update(__snake_case			)

										return encoding_image_processor

						def      A__    (		self :List[Any]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[Any]     ,			*__snake_case :Optional[int]     ,			**__snake_case :str			):
										'''simple docstring'''

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


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						int										=self.tokenizer.model_input_names
										__magic_name__						:						Any										=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_lowercase											=    """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

_lowercase											=    """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

_lowercase											=    """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Union[str, Any]				)			->						int:
       return float((preds == labels).mean()				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		=simple_accuracy(UpperCAmelCase_			,							UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=float(fa_score(y_true=UpperCAmelCase_			,							y_pred=UpperCAmelCase_				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				str		=float(pearsonr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=float(spearmanr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }


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


       def 	_snake_case				(    self				)						->    str:
              if self.config_name not in [
                  "sst2",
                  "mnli",
                  "mnli_mismatched",
                  "mnli_matched",
                  "cola",
                  "stsb",
                  "mrpc",
                  "qqp",
                  "qnli",
                  "rte",
                  "wnli",
                  "hans",
              ]:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       inputs_description=_KWARGS_DESCRIPTION						,       features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                          '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                      }				)						,       codebase_urls=[]						,       reference_urls=[]						,       format='''numpy'''						,       )





       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[Any]:
              if self.config_name == "cola":
                     return {"matthews_correlation": matthews_corrcoef(__A						,       __A				)}
              elif self.config_name == "stsb":
                     return pearson_and_spearman(__A						,       __A				)
              elif self.config_name in ["mrpc", "qqp"]:
                     return acc_and_fa(__A						,       __A				)
              elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                     return {"accuracy": simple_accuracy(__A						,       __A				)}
              else:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
