


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


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






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, **a__ )						->					Union[str, Any]:
					A             =					feature_size
					A             =					sampling_rate
					A             =					padding_value

					A             =					kwargs.pop("""padding_side"""					, """right""" )
					A             =					kwargs.pop("""return_attention_mask"""					, a__ )

					super().__init__(**a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ = True					, a__ = None					, a__ = False					, a__ = None					, a__ = None					, a__ = 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(a__					, (list, tuple) ) and isinstance(processed_features[0]					, (dict, BatchFeature) ):
						A             =					{
						    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() )}' )

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

					if len(a__ ) == 0:
						if return_attention_mask:
							A             =					[]
						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

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

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

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

        # Convert padding_strategy in PaddingStrategy
					A             =					self._get_padding_strategies(padding=a__					, max_length=a__ )

					A             =					processed_features[self.model_input_names[0]]

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

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

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

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

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

					return BatchFeature(a__					, tensor_type=a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = PaddingStrategy.DO_NOT_PAD					, a__ = None					, a__ = None					, )						->					dict:
					A             =					processed_features[self.model_input_names[0]]

					if padding_strategy == PaddingStrategy.LONGEST:
						A             =					len(a__ )

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

					A             =					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a__ ) < max_length

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

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

					return processed_features

				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, a__ = None					, )						->					Union[str, Any]:
					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.""" )

					A             =					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):
						A             =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

					A             =					len(a__ ) > max_length

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

					return processed_features

				def   _UpperCAmelCase       (    self					, a__=False					, a__=None )						->					str:

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







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]=None    )			-> Union[str, Any]:





	"""simple docstring"""







	if subparsers is not None:
		A             =					subparsers.add_parser("""test"""    )
	else:
		A             =					argparse.ArgumentParser("""Accelerate test command"""    )

	parser.add_argument(
	    """--config_file"""				,       default=UpperCamelCase__				,       help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )				,       )

	if subparsers is not None:
		parser.set_defaults(func=UpperCamelCase__    )
	return parser





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





	"""simple docstring"""







	A             =					os.path.sep.join(__file__.split(os.path.sep    )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]    )

	if args.config_file is None:
		A             =					script_name
	else:
		A             =					f'--config_file={args.config_file} {script_name}'

	A             =					["""accelerate-launch"""] + test_args.split()
	A             =					execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()    )
	if result.returncode == 0:
		print("""Test is a success! You are ready for your distributed training!"""    )





def 	_lowerCAmelCase					( )			-> List[Any]:





	"""simple docstring"""







	A             =					test_command_parser()
	A             =					parser.parse_args()
	test_command(UpperCamelCase__    )


if __name__ == "__main__":
				main()



