





"""simple docstring"""






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

A			      =	random.Random()

def      __A   (      a_    :Tuple ,							a_    :Dict=1.0 ,							a_    :str=None ,							a_    :List[Any]=None)		->		Dict:
				if rng is None:
								__a			:    Any      							=	global_rng

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

				return values




class 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=7						,      _UpperCAmelCase=400						,      _UpperCAmelCase=2000						,      _UpperCAmelCase=2048						,      _UpperCAmelCase=128						,      _UpperCAmelCase=1						,      _UpperCAmelCase=512						,      _UpperCAmelCase=30						,      _UpperCAmelCase=44100						,      ):
									__a			:    Any      							=	parent
									__a			:    Tuple      							=	batch_size
									__a			:    Tuple      							=	min_seq_length
									__a			:    List[str]      							=	max_seq_length
									__a			:    List[Any]      							=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__a			:    Tuple      							=	spectrogram_length
									__a			:    int      							=	feature_size
									__a			:    int      							=	num_audio_channels
									__a			:    Tuple      							=	hop_length
									__a			:    List[Any]      							=	chunk_length
									__a			:    Any      							=	sampling_rate







					def 			_lowerCamelCase		(    self  ):
									return {
									    "spectrogram_length": self.spectrogram_length,
									    "feature_size": self.feature_size,
									    "num_audio_channels": self.num_audio_channels,
									    "hop_length": self.hop_length,
									    "chunk_length": self.chunk_length,
									    "sampling_rate": self.sampling_rate,
									}







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase=False						,      _UpperCAmelCase=False  ):
									def _flatten(_UpperCAmelCase  ):
													return list(itertools.chain(*_UpperCAmelCase  )  )

									if equal_length:
													__a			:    Tuple      							=	[floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
									else:
													# make sure that inputs increase in size
													__a			:    Tuple      							=	[
													    floats_list((x, self.feature_size)  )
													    for x in range(self.min_seq_length						,      self.max_seq_length						,      self.seq_length_diff  )
													]
									if numpify:
													__a			:    Optional[Any]      							=	[np.asarray(_UpperCAmelCase  ) for x in speech_inputs]
									return speech_inputs






@require_torch
@require_torchaudio
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	TvltFeatureExtractor







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	TvltFeatureExtractionTester(self  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	self.feature_extraction_class(**self.feat_extract_dict  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''spectrogram_length'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''feature_size'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''num_audio_channels'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''hop_length'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''chunk_length'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''sampling_rate'''  )  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	self.feature_extraction_class(**self.feat_extract_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
													__a			:    List[str]      							=	feat_extract_first.save_pretrained(_UpperCAmelCase  )[0]
													check_json_file_has_correct_format(_UpperCAmelCase  )
													__a			:    Union[str, Any]      							=	self.feature_extraction_class.from_pretrained(_UpperCAmelCase  )

									__a			:    Tuple      							=	feat_extract_first.to_dict()
									__a			:    List[Any]      							=	feat_extract_second.to_dict()
									__a			:    int      							=	dict_first.pop('''mel_filters'''  )
									__a			:    List[Any]      							=	dict_second.pop('''mel_filters'''  )
									self.assertTrue(np.allclose(_UpperCAmelCase						,      _UpperCAmelCase  )  )
									self.assertEqual(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	self.feature_extraction_class(**self.feat_extract_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
													__a			:    int      							=	os.path.join(_UpperCAmelCase						,      '''feat_extract.json'''  )
													feat_extract_first.to_json_file(_UpperCAmelCase  )
													__a			:    Optional[Any]      							=	self.feature_extraction_class.from_json_file(_UpperCAmelCase  )

									__a			:    Optional[Any]      							=	feat_extract_first.to_dict()
									__a			:    Any      							=	feat_extract_second.to_dict()
									__a			:    Optional[Any]      							=	dict_first.pop('''mel_filters'''  )
									__a			:    Dict      							=	dict_second.pop('''mel_filters'''  )
									self.assertTrue(np.allclose(_UpperCAmelCase						,      _UpperCAmelCase  )  )
									self.assertEqual(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									# Initialize feature_extractor
									__a			:    str      							=	self.feature_extraction_class(**self.feat_extract_dict  )

									# create three inputs of length 800, 1000, and 1200
									__a			:    Any      							=	[floats_list((1, x)  )[0] for x in range(800						,      1400						,      200  )]
									__a			:    Union[str, Any]      							=	[np.asarray(_UpperCAmelCase  ) for speech_input in speech_inputs]

									# Test not batched input
									__a			:    List[str]      							=	feature_extractor(np_speech_inputs[0]						,      return_tensors='''np'''						,      sampling_rate=44100  ).audio_values

									self.assertTrue(encoded_audios.ndim == 4  )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

									# Test batched
									__a			:    int      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''np'''						,      sampling_rate=44100  ).audio_values

									self.assertTrue(encoded_audios.ndim == 4  )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

									# Test audio masking
									__a			:    List[Any]      							=	feature_extractor(
									    _UpperCAmelCase						,      return_tensors='''np'''						,      sampling_rate=44100						,      mask_audio=_UpperCAmelCase  ).audio_values

									self.assertTrue(encoded_audios.ndim == 4  )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

									# Test 2-D numpy arrays are batched.
									__a			:    str      							=	[floats_list((1, x)  )[0] for x in (800, 800, 800)]
									__a			:    Any      							=	np.asarray(_UpperCAmelCase  )
									__a			:    Optional[Any]      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''np'''						,      sampling_rate=44100  ).audio_values
									self.assertTrue(encoded_audios.ndim == 4  )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    int      							=	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''						,      '''clean'''						,      split='''validation'''  )
									# automatic decoding with librispeech
									__a			:    int      							=	ds.sort('''id'''  ).select(range(_UpperCAmelCase  )  )[:num_samples]['''audio''']

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







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	self._load_datasamples(1  )
									__a			:    Tuple      							=	TvltFeatureExtractor()
									__a			:    Optional[Any]      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''pt'''  ).audio_values

									self.assertEquals(audio_values.shape						,      (1, 1, 192, 128)  )

									__a			:    Dict      							=	torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]  )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,      _UpperCAmelCase						,      atol=1e-4  )  )






"""simple docstring"""






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			np.asarray(weights[2]			)

			set_param(
			    torch_layer.self_attention.query_key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	int		 =			np.asarray(weights[3]			)

			set_param(
			    torch_layer.self_attention.query							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# lsh weights + output
			a__  :	Dict		 =			weights[0][1]
			if len(lowerCAmelCase__			) < 4:
						set_layer_weights_in_torch_lsh(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)
			else:
						set_layer_weights_in_torch_local(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)

			# intermediate weighs
			a__  :	Optional[int]		 =			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCAmelCase__			) == 4:
						a__  :	List[str]		 =			intermediate_weights[2]

			# layernorm 2
			a__  :	List[Any]		 =			np.asarray(intermediate_weights[0][0]			)
			a__  :	Optional[int]		 =			np.asarray(intermediate_weights[0][1]			)
			set_param(
			    torch_block.feed_forward.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, Any]		 =			np.asarray(intermediate_weights[1][1]			)
			set_param(
			    torch_block.feed_forward.dense.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate out
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	Dict		 =			np.asarray(intermediate_weights[4][1]			)
			set_param(
			    torch_block.feed_forward.output.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

			# word embeds
			a__  :	List[Any]		 =			np.asarray(weights[1]			)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowerCAmelCase__			)							,			)

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	str		 =			np.asarray(weights[3][emb_idx][0]			)
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), F"{position_embeddings[emb_idx]} emb does not match"
									a__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

			a__  :	Optional[Any]		 =			weights[5]
			assert len(torch_model_reformer.encoder.layers			) * 4 == len(
			    lowerCAmelCase__			), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
						a__  :	Union[str, Any]		 =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# output layer norm
			a__  :	str		 =			np.asarray(weights[7][0]			)
			a__  :	List[Any]		 =			np.asarray(weights[7][1]			)
			set_param(
			    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# output embeddings
			a__  :	str		 =			np.asarray(weights[9][0]			)
			a__  :	Optional[int]		 =			np.asarray(weights[9][1]			)
			set_param(
			    torch_model.lm_head.decoder							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	List[str]		 =			pickle.load(lowerCAmelCase__			)["weights"]

			set_model_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			config.hidden_size			)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			torch.save(model.state_dict()							,			lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained Reformer model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)