


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a__				:							Union[str, Any]						=     logging.get_logger(__name__)




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				float			,			**UpperCAmelCase__			:				List[str]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				feature_size
						__SCREAMING_SNAKE_CASE						   =				sampling_rate
						__SCREAMING_SNAKE_CASE						   =				padding_value

						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("padding_side"			,			"right"   )
						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("return_attention_mask"			,			UpperCAmelCase__   )

						super().__init__(**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[
	        BatchFeature,
	        List[BatchFeature],
	        Dict[str, BatchFeature],
	        Dict[str, List[BatchFeature]],
	        List[Dict[str, BatchFeature]],
	    ]			,			UpperCAmelCase__			:				Union[bool, str, PaddingStrategy] = True			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				bool = False			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			UpperCAmelCase__			:				Optional[Union[str, TensorType]] = None			,			)       ->	BatchFeature:
						# If we have a list of dicts, let's convert it in a dict of lists
						# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
						if isinstance(UpperCAmelCase__			,			(list, tuple)   ) and isinstance(processed_features[0]			,			(dict, BatchFeature)   ):
											__SCREAMING_SNAKE_CASE						   =				{
											    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
											}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
											raise ValueError(
											    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
											    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
											    F""" {list(processed_features.keys()   )}"""   )

						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]
						__SCREAMING_SNAKE_CASE						   =				(
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(UpperCAmelCase__   ) == 0:
											if return_attention_mask:
																__SCREAMING_SNAKE_CASE						   =				[]
											return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						__SCREAMING_SNAKE_CASE						   =				required_input[0]
						if isinstance(UpperCAmelCase__			,			(list, tuple)   ):
											# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
											__SCREAMING_SNAKE_CASE						   =				0
											while len(required_input[index]   ) == 0:
																index += 1
											if index < len(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				required_input[index][0]

						if return_tensors is None:
											if is_tf_tensor(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				"tf"
											elif is_torch_tensor(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				"pt"
											elif isinstance(UpperCAmelCase__			,			(int, float, list, tuple, np.ndarray)   ):
																__SCREAMING_SNAKE_CASE						   =				"np"
											else:
																raise ValueError(
																    F"""type of {first_element} unknown: {type(UpperCAmelCase__   )}. """
																    "Should be one of a python, numpy, pytorch or tensorflow object."   )

						for key, value in processed_features.items():
											if isinstance(value[0]			,			(int, float)   ):
																__SCREAMING_SNAKE_CASE						   =				to_numpy(UpperCAmelCase__   )
											else:
																__SCREAMING_SNAKE_CASE						   =				[to_numpy(UpperCAmelCase__   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
						__SCREAMING_SNAKE_CASE						   =				self._get_padding_strategies(padding=UpperCAmelCase__			,			max_length=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]

						__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   )
						if not all(len(UpperCAmelCase__   ) == batch_size for v in processed_features.values()   ):
											raise ValueError("Some items in the output dictionary have a different batch size than others."   )

						__SCREAMING_SNAKE_CASE						   =				[]
						for i in range(UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				{k: v[i] for k, v in processed_features.items()}
											# truncation
											__SCREAMING_SNAKE_CASE						   =				self._truncate(
											    UpperCAmelCase__			,			max_length=UpperCAmelCase__			,			pad_to_multiple_of=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			)
											truncated_inputs.append(UpperCAmelCase__   )

						if padding_strategy == PaddingStrategy.LONGEST:
											# make sure that `max_length` cannot be longer than the longest truncated length
											__SCREAMING_SNAKE_CASE						   =				max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
											__SCREAMING_SNAKE_CASE						   =				PaddingStrategy.MAX_LENGTH

						__SCREAMING_SNAKE_CASE						   =				{}
						for i in range(UpperCAmelCase__   ):
											# padding
											__SCREAMING_SNAKE_CASE						   =				self._pad(
											    truncated_inputs[i]			,			max_length=UpperCAmelCase__			,			padding_strategy=UpperCAmelCase__			,			pad_to_multiple_of=UpperCAmelCase__			,			return_attention_mask=UpperCAmelCase__			,			)

											for key, value in outputs.items():
																if key not in batch_outputs:
																					__SCREAMING_SNAKE_CASE						   =				[]
																if value.dtype is np.dtype(np.floataa   ):
																					__SCREAMING_SNAKE_CASE						   =				value.astype(np.floataa   )
																batch_outputs[key].append(UpperCAmelCase__   )

						return BatchFeature(UpperCAmelCase__			,			tensor_type=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[Dict[str, np.ndarray], BatchFeature]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			)       ->	dict:
						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
											__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   )

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
											__SCREAMING_SNAKE_CASE						   =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						__SCREAMING_SNAKE_CASE						   =				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase__   ) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
											__SCREAMING_SNAKE_CASE						   =				np.ones(len(UpperCAmelCase__   )			,			dtype=np.intaa   )

						if needs_to_be_padded:
											__SCREAMING_SNAKE_CASE						   =				max_length - len(UpperCAmelCase__   )
											if self.padding_side == "right":
																if return_attention_mask:
																					__SCREAMING_SNAKE_CASE						   =				np.pad(
																					    processed_features["attention_mask"]			,			(0, difference)   )
																__SCREAMING_SNAKE_CASE						   =				((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																__SCREAMING_SNAKE_CASE						   =				np.pad(
																    UpperCAmelCase__			,			UpperCAmelCase__			,			"constant"			,			constant_values=self.padding_value   )
											elif self.padding_side == "left":
																if return_attention_mask:
																					__SCREAMING_SNAKE_CASE						   =				np.pad(
																					    processed_features["attention_mask"]			,			(difference, 0)   )
																__SCREAMING_SNAKE_CASE						   =				((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																__SCREAMING_SNAKE_CASE						   =				np.pad(
																    UpperCAmelCase__			,			UpperCAmelCase__			,			"constant"			,			constant_values=self.padding_value   )
											else:
																raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

						return processed_features




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Union[Dict[str, np.ndarray], BatchFeature]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			)       ->	str:
						if not truncation:
											return processed_features
						elif truncation and max_length is None:
											raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."   )

						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
											__SCREAMING_SNAKE_CASE						   =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   ) > max_length

						if needs_to_be_truncated:
											__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]][:max_length]
											if "attention_mask" in processed_features:
																__SCREAMING_SNAKE_CASE						   =				processed_features["attention_mask"][:max_length]

						return processed_features




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Optional[Any]=False			,			UpperCAmelCase__			:				Optional[int]=None   )       ->	str:

						# Get padding strategy
						if padding is not False:
											if padding is True:
																__SCREAMING_SNAKE_CASE						   =				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
											elif not isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				PaddingStrategy(UpperCAmelCase__   )
											elif isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				padding
						else:
											__SCREAMING_SNAKE_CASE						   =				PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
											if padding_strategy == PaddingStrategy.MAX_LENGTH:
																raise ValueError(
																    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""   )

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
											raise ValueError(
											    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
											    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."   )

						return padding_strategy







import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Tuple          =      re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''							)
           UpperCamelCase__							:				str          =      input_file.read()
           UpperCamelCase__							:				List[Any]          =      regexp.search(__magic_name__							)

       return match




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Dict          =      re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',		re.DOTALL							)
           UpperCamelCase__							:				Any          =      input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           UpperCamelCase__							:				Tuple          =      regexp.finditer(__magic_name__							)

       UpperCamelCase__							:				Dict          =      [match for match in matches if match is not None and match.group(1							) is not None]
       return matches[0] if matches else None




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Any          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(__magic_name__							)							):
               raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Optional[Any]          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_print_statements(str(__magic_name__							)							):
               raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."							)

