

'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch


lowerCAmelCase__      				=			random.Random()







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=1.0 ,UpperCamelCase=None ,UpperCamelCase=None	)     ->     Optional[Any]:
	if rng is None:
		UpperCAmelCase_		:   str													=       global_rng

	UpperCAmelCase_		:   Any													=       []
	for batch_idx in range(shape[0]	):
		values.append([]	)
		for _ in range(shape[1]	):
			values[-1].append(rng.random() * scale	)

	return values



@require_torch
class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=7     ,      _snake_case=400     ,      _snake_case=2000     ,      _snake_case=1     ,      _snake_case=0.0     ,      _snake_case=2_4000     ,      _snake_case=True     ,      )				->					Tuple:
								UpperCAmelCase_		:   int													=       parent
								UpperCAmelCase_		:   int													=       batch_size
								UpperCAmelCase_		:   int													=       min_seq_length
								UpperCAmelCase_		:   Union[str, Any]													=       max_seq_length
								UpperCAmelCase_		:   List[str]													=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								UpperCAmelCase_		:   Union[str, Any]													=       feature_size
								UpperCAmelCase_		:   int													=       padding_value
								UpperCAmelCase_		:   Dict													=       sampling_rate
								UpperCAmelCase_		:   List[str]													=       return_attention_mask


							def     _snake_case				(      self)				->					Dict:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								}





							def     _snake_case				(      self     ,      _snake_case=False     ,      _snake_case=False)				->					str:
								def _flatten(_snake_case):
									return list(itertools.chain(*_snake_case))

								if equal_length:
									UpperCAmelCase_		:   Optional[int]													=       floats_list((self.batch_size, self.max_seq_length))
								else:
									# make sure that inputs increase in size
									UpperCAmelCase_		:   Any													=       [
									    _flatten(floats_list((x, self.feature_size)))
									    for x in range(self.min_seq_length     ,      self.max_seq_length     ,      self.seq_length_diff)
									]

								if numpify:
									UpperCAmelCase_		:   int													=       [np.asarray(_snake_case) for x in audio_inputs]

								return audio_inputs



@require_torch
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Optional[int]= EncodecFeatureExtractor


							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   Union[str, Any]													=       EnCodecFeatureExtractionTester(self)


							def     _snake_case				(      self)				->					int:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								UpperCAmelCase_		:   Any													=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
								# create three inputs of length 800, 1000, and 1200
								UpperCAmelCase_		:   Optional[Any]													=       [floats_list((1, x))[0] for x in range(800     ,      1400     ,      200)]
								UpperCAmelCase_		:   List[str]													=       [np.asarray(_snake_case) for audio_input in audio_inputs]

								# Test not batched input
								UpperCAmelCase_		:   int													=       feat_extract(audio_inputs[0]     ,      return_tensors='np').input_values
								UpperCAmelCase_		:   Union[str, Any]													=       feat_extract(np_audio_inputs[0]     ,      return_tensors='np').input_values
								self.assertTrue(np.allclose(_snake_case     ,      _snake_case     ,      atol=1e-3))

								# Test batched
								UpperCAmelCase_		:   Optional[Any]													=       feat_extract(_snake_case     ,      padding=_snake_case     ,      return_tensors='np').input_values
								UpperCAmelCase_		:   Union[str, Any]													=       feat_extract(_snake_case     ,      padding=_snake_case     ,      return_tensors='np').input_values
								for enc_seq_a, enc_seq_a in zip(_snake_case     ,      _snake_case):
									self.assertTrue(np.allclose(_snake_case     ,      _snake_case     ,      atol=1e-3))


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   Any													=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
								UpperCAmelCase_		:   Any													=       np.random.rand(100).astype(np.floataa)
								UpperCAmelCase_		:   List[str]													=       np_audio_inputs.tolist()

								for inputs in [py_audio_inputs, np_audio_inputs]:
									UpperCAmelCase_		:   Union[str, Any]													=       feature_extractor.pad([{'input_values': inputs}]     ,      return_tensors='np')
									self.assertTrue(np_processed.input_values.dtype == np.floataa)
									UpperCAmelCase_		:   List[str]													=       feature_extractor.pad([{'input_values': inputs}]     ,      return_tensors='pt')
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								from datasets import load_dataset

								UpperCAmelCase_		:   Dict													=       load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,      'clean'     ,      split='validation')
								# automatic decoding with librispeech
								UpperCAmelCase_		:   List[str]													=       ds.sort('id').select(range(_snake_case))[:num_samples]['audio']

								return [x["array"] for x in audio_samples]


							def     _snake_case				(      self)				->					Any:
								# fmt: off
								UpperCAmelCase_		:   Optional[Any]													=       torch.tensor(
								    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
								     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
								     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
								     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
								     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
								     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03])
								# fmt: on
								UpperCAmelCase_		:   Any													=       self._load_datasamples(1)
								UpperCAmelCase_		:   Tuple													=       EncodecFeatureExtractor()
								UpperCAmelCase_		:   Optional[int]													=       feature_extractor(_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(input_values.shape     ,      (1, 1, 9_3680))
								self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,      _snake_case     ,      atol=1e-6))


							def     _snake_case				(      self)				->					Tuple:
								# fmt: off
								UpperCAmelCase_		:   Any													=       torch.tensor(
								    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
								     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
								     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
								     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
								     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
								     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03])
								# fmt: on
								UpperCAmelCase_		:   Tuple													=       self._load_datasamples(1)
								UpperCAmelCase_		:   int													=       [np.tile(input_audio[0][None]     ,      reps=(2, 1))]
								input_audio[0][1] *= 0.5
								UpperCAmelCase_		:   Any													=       EncodecFeatureExtractor(feature_size=2)
								UpperCAmelCase_		:   Optional[Any]													=       feature_extractor(_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(input_values.shape     ,      (1, 2, 9_3680))
								self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,      _snake_case     ,      atol=1e-6))
								self.assertTrue(torch.allclose(input_values[0, 1, :30]     ,      EXPECTED_INPUT_VALUES * 0.5     ,      atol=1e-6))





							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_		:   List[str]													=       self._load_datasamples(2)
								# would be easier if the stride was like
								UpperCAmelCase_		:   Optional[int]													=       EncodecFeatureExtractor(feature_size=1     ,      chunk_length_s=1     ,      overlap=0.01)

								# pad and trunc raise an error ?
								with self.assertRaisesRegex(
								    _snake_case     ,      '^Both padding and truncation were set. Make sure you only set one.$'     ,      ):
									UpperCAmelCase_		:   Dict													=       feature_extractor(
									    _snake_case     ,      padding='max_length'     ,      truncation=_snake_case     ,      return_tensors='pt').input_values

								# truncate to chunk
								UpperCAmelCase_		:   Optional[Any]													=       feature_extractor(_snake_case     ,      truncation=_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (2, 1, 7_1520))  # 2 chunks

								# force truncate to max_length
								UpperCAmelCase_		:   Any													=       feature_extractor(
								    _snake_case     ,      truncation=_snake_case     ,      max_length=4_8000     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (2, 1, 4_8000))

								# pad to chunk
								UpperCAmelCase_		:   List[str]													=       feature_extractor(_snake_case     ,      padding=_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(padded_outputs.shape     ,      (2, 1, 9_5280))

								# pad to chunk
								UpperCAmelCase_		:   str													=       feature_extractor(_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (2, 1, 9_5280))

								# force pad to max length
								UpperCAmelCase_		:   Any													=       feature_extractor(
								    _snake_case     ,      padding='max_length'     ,      max_length=10_0000     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (2, 1, 10_0000))

								# force no pad
								with self.assertRaisesRegex(
								    _snake_case     ,      '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'     ,      ):
									UpperCAmelCase_		:   Any													=       feature_extractor(_snake_case     ,      padding=_snake_case     ,      return_tensors='pt').input_values

								UpperCAmelCase_		:   List[Any]													=       feature_extractor(input_audio[0]     ,      padding=_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (1, 1, 9_3680))

								# no pad if no chunk_length_s
								UpperCAmelCase_		:   str													=       None
								with self.assertRaisesRegex(
								    _snake_case     ,      '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'     ,      ):
									UpperCAmelCase_		:   Union[str, Any]													=       feature_extractor(_snake_case     ,      padding=_snake_case     ,      return_tensors='pt').input_values

								UpperCAmelCase_		:   Optional[int]													=       feature_extractor(input_audio[0]     ,      padding=_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (1, 1, 9_3680))

								# no pad if no overlap
								UpperCAmelCase_		:   int													=       2
								UpperCAmelCase_		:   Optional[int]													=       None
								with self.assertRaisesRegex(
								    _snake_case     ,      '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'     ,      ):
									UpperCAmelCase_		:   Dict													=       feature_extractor(_snake_case     ,      padding=_snake_case     ,      return_tensors='pt').input_values

								UpperCAmelCase_		:   Dict													=       feature_extractor(input_audio[0]     ,      padding=_snake_case     ,      return_tensors='pt').input_values
								self.assertEquals(truncated_outputs.shape     ,      (1, 1, 9_3680))









'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase__      				=			[nn.LayerNorm]

lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("2.0")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.12")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.11")
lowerCAmelCase__      				=			parsed_torch_version_base < version.parse("1.11")







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,self	)     ->     List[Any]:
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self	)
	else:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self.dtype	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 0	)     ->     nn.Linear:
	UpperCAmelCase_		:   Tuple													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		:   List[Any]													=       layer.bias.clone().detach()
		else:
			UpperCAmelCase_		:   Tuple													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   Optional[Any]													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   int													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   str													=       nn.Linear(new_size[1] ,new_size[0] ,bias=layer.bias is not None	).to(layer.weight.device	)
	UpperCAmelCase_		:   str													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Tuple													=       True
	if layer.bias is not None:
		UpperCAmelCase_		:   Union[str, Any]													=       False
		new_layer.bias.copy_(b.contiguous()	)
		UpperCAmelCase_		:   Any													=       True
	return new_layer



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case)				->					int:
								super().__init__()
								UpperCAmelCase_		:   List[Any]													=       nf
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.empty(_snake_case     ,      _snake_case))
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.zeros(_snake_case))
								nn.init.normal_(self.weight     ,      std=0.02)





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Dict													=       x.size()[:-1] + (self.nf,)
								UpperCAmelCase_		:   Optional[Any]													=       torch.addmm(self.bias     ,      x.view(-1     ,      x.size(-1))     ,      self.weight)
								UpperCAmelCase_		:   List[str]													=       x.view(_snake_case)
								return x







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 1	)     ->     ConvaD:
	UpperCAmelCase_		:   List[str]													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Optional[Any]													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if dim == 0:
		UpperCAmelCase_		:   Tuple													=       layer.bias.clone().detach()
	else:
		UpperCAmelCase_		:   str													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   str													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   List[str]													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       ConvaD(new_size[1] ,new_size[0]	).to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Optional[Any]													=       True
	UpperCAmelCase_		:   Any													=       False
	new_layer.bias.copy_(b.contiguous()	)
	UpperCAmelCase_		:   Union[str, Any]													=       True
	return new_layer







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None	)     ->     Union[nn.Linear, ConvaD]:
	if isinstance(UpperCamelCase ,nn.Linear	):
		return prune_linear_layer(UpperCamelCase ,UpperCamelCase ,dim=0 if dim is None else dim	)
	elif isinstance(UpperCamelCase ,UpperCamelCase	):
		return prune_convad_layer(UpperCamelCase ,UpperCamelCase ,dim=1 if dim is None else dim	)
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}"""	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,*UpperCamelCase	)     ->     torch.Tensor:
	assert len(UpperCamelCase	) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		:   str													=       len(inspect.signature(UpperCamelCase	).parameters	)
	if num_args_in_forward_chunk_fn != len(UpperCamelCase	):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase	)} input """
		    'tensors are given'	)

	if chunk_size > 0:
		UpperCAmelCase_		:   Optional[int]													=       input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}"""	)

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}"""	)

		UpperCAmelCase_		:   Any													=       input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		:   int													=       tuple(input_tensor.chunk(UpperCamelCase ,dim=UpperCamelCase	) for input_tensor in input_tensors	)
		# apply forward fn to every tuple
		UpperCAmelCase_		:   List[str]													=       tuple(forward_fn(*UpperCamelCase	) for input_tensors_chunk in zip(*UpperCamelCase	)	)
		# concatenate output at same dimension
		return torch.cat(UpperCamelCase ,dim=UpperCamelCase	)

	return forward_fn(*UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple[Set[int], torch.LongTensor]:
	UpperCAmelCase_		:   List[Any]													=       torch.ones(UpperCamelCase ,UpperCamelCase	)
	UpperCAmelCase_		:   Optional[int]													=       set(UpperCamelCase	) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		:   Any													=       head - sum(1 if h < head else 0 for h in already_pruned_heads	)
		UpperCAmelCase_		:   List[Any]													=       0
	UpperCAmelCase_		:   str													=       mask.view(-1	).contiguous().eq(1	)
	UpperCAmelCase_		:   torch.LongTensor													=       torch.arange(len(UpperCamelCase	)	)[mask].long()
	return heads, index







def     SCREAMING_SNAKE_CASE(	*UpperCamelCase ,UpperCamelCase = None	)     ->     Tuple[torch.Tensor, ...]:
	return torch.meshgrid(*UpperCamelCase ,indexing=UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Tuple[torch.device, int, int]:
	return tensor.device, storage_ptr(UpperCamelCase	), storage_size(UpperCamelCase	)






