





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[int]:
       if isinstance(UpperCamelCase_					,		np.ndarray  ):
              return list(tensor.shape  )

       UpperCamelCase_          =  tf.shape(UpperCamelCase_  )

       if tensor.shape == tf.TensorShape(UpperCamelCase_  ):
              return dynamic

       UpperCamelCase_          =  tensor.shape.as_list()

       return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_  )]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None  )     ->		tf.Tensor:
       return tf.nn.softmax(logits=logits + 1e-9					,		axis=UpperCamelCase_					,		name=UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=1e-5					,		UpperCamelCase_=-1  )     ->		List[Any]:
       # This is a very simplified functional layernorm, designed to duplicate
       # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
       # models in Transformers.

       if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_					,		UpperCamelCase_  ):
              raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."  )

       # Get mean and variance on the axis to be normalized
       UpperCamelCase_       , UpperCamelCase_          =  tf.nn.moments(UpperCamelCase_					,		axes=[axis]					,		keepdims=UpperCamelCase_  )

       if axis != -1:
              # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
              # on every dimension except axis
              UpperCamelCase_          =  [1] * inputs.shape.rank
              UpperCamelCase_          =  shape_list(UpperCamelCase_  )[axis]
              UpperCamelCase_          =  tf.reshape(UpperCamelCase_					,		UpperCamelCase_  )
              UpperCamelCase_          =  tf.reshape(UpperCamelCase_					,		UpperCamelCase_  )

       # Compute layer normalization using the batch_normalization
       # function.
       UpperCamelCase_          =  tf.nn.batch_normalization(
           UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		offset=UpperCamelCase_					,		scale=UpperCamelCase_					,		variance_epsilon=UpperCamelCase_					,		)
       return outputs




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=0					,		UpperCamelCase_=-1  )     ->		int:
       # Replicates the behavior of torch.flatten in TF

       # If end_dim or start_dim is negative, count them from the end
       if end_dim < 0:
              end_dim += input.shape.rank
       if start_dim < 0:
              start_dim += input.shape.rank

       if start_dim == end_dim:
              return input

       UpperCamelCase_          =  tf.shape(UpperCamelCase_  )
       UpperCamelCase_          =  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]  )
       UpperCamelCase_          =  tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]					,		axis=0  )
       return tf.reshape(UpperCamelCase_					,		UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		tf.Tensor:
       if not isinstance(UpperCamelCase_					,		tf.Tensor  ):
              UpperCamelCase_          =  tf.convert_to_tensor(UpperCamelCase_  )  # Catches stray NumPy inputs
       if encoder_attention_mask.shape.rank == 3:
              UpperCamelCase_          =  encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.shape.rank == 2:
              UpperCamelCase_          =  encoder_attention_mask[:, None, None, :]
       # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
       # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
       # /transformer/transformer_layers.py#L270
       # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
       # encoder_extended_attention_mask.transpose(-1, -2))
       UpperCamelCase_          =  (
           tf.cast(1					,		encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
       ) * encoder_extended_attention_mask.dtype.min

       return encoder_extended_attention_mask




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = "input_ids"  )     ->		None:
       tf.debugging.assert_less(
           UpperCamelCase_					,		tf.cast(UpperCamelCase_					,		dtype=tensor.dtype  )					,		message=(
               F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_  )}) must be smaller than the embedding '''
               F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
           )					,		)




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Dict:
       UpperCamelCase_          =  64512
       # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
       # because in that case even chunking the array would not make the saving
       # possible.
       UpperCamelCase_          =  [x for x in data if len(UpperCamelCase_  ) > HDF5_OBJECT_HEADER_LIMIT]

       # Expecting this to never be true.
       if bad_attributes:
              raise RuntimeError(
                  "The following attributes cannot be saved to HDF5 file because "
                  F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
                  F'''bytes: {bad_attributes}'''  )

       UpperCamelCase_          =  np.asarray(UpperCamelCase_  )

       UpperCamelCase_          =  1
       UpperCamelCase_          =  np.array_split(UpperCamelCase_					,		UpperCamelCase_  )

       # This will never loop forever thanks to the test above.
       while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data  ):
              num_chunks += 1
              UpperCamelCase_          =  np.array_split(UpperCamelCase_					,		UpperCamelCase_  )

       if num_chunks > 1:
              for chunk_id, chunk_data in enumerate(UpperCamelCase_  ):
                     UpperCamelCase_          =  chunk_data
       else:
              UpperCamelCase_          =  data




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       if name in group.attrs:
              UpperCamelCase_          =  [n.decode("utf8"  ) if hasattr(UpperCamelCase_					,		"decode"  ) else n for n in group.attrs[name]]
       else:
              UpperCamelCase_          =  []
              UpperCamelCase_          =  0
              while "%s%d" % (name, chunk_id) in group.attrs:
                     data.extend(
                         [n.decode("utf8"  ) if hasattr(UpperCamelCase_					,		"decode"  ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]  )
                     chunk_id += 1
       return data







def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:

       def _expand_single_ad_tensor(UpperCamelCase_  ):
              if isinstance(UpperCamelCase_					,		tf.Tensor  ) and t.shape.rank == 1:
                     return tf.expand_dims(UpperCamelCase_					,		axis=-1  )
              return t

       return tf.nest.map_structure(_expand_single_ad_tensor					,		UpperCamelCase_  )








import re
import string

import numpy as np

import datasets


_UpperCAmelCase        =				'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

_UpperCAmelCase        =				'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

_UpperCAmelCase        =				'\n'



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

       def lowercase (		self: Any		)       -> List[str]:




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION					,   citation=_CITATION					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
                      {
                          "predictions": datasets.Value("string"					,   id="sequence"		),
                          "references": datasets.Value("string"					,   id="sequence"		),
                      }		)					,   reference_urls=[]					,   )



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=False					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: Any=False					,   )       -> Optional[Any]:




              """simple docstring"""


              if regexes_to_ignore is not None:
                     for s in regexes_to_ignore:
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in predictions]		)
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in references]		)
              else:
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)

              if ignore_case:
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)

              if ignore_punctuation:
                     UpperCamelCase_          =  string.punctuation.maketrans(""					,   ""					,   string.punctuation		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              if ignore_numbers:
                     UpperCamelCase_          =  string.digits.maketrans(""					,   ""					,   string.digits		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  predictions == references

              return {"exact_match": np.mean(_SCREAMING_SNAKE_CASE		) * 100}

