

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


A_       : Optional[int]         =			random.Random()

def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Union[str, Any]=1.0    ,      lowercase_: Union[str, Any]=None    ,      lowercase_: str=None					)     ->      List[str]:
	if rng is None:
		A__							:    List[Any]        =		global_rng

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

	return values



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=400    ,       A__=2000    ,       A__=1    ,       A__=0.0    ,       A__=1_6000    ,       A__=True    ,       A__=True    ,       ):
						A__							:    str        =		parent
						A__							:    str        =		batch_size
						A__							:    int        =		min_seq_length
						A__							:    Optional[Any]        =		max_seq_length
						A__							:    str        =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A__							:    Union[str, Any]        =		feature_size
						A__							:    List[Any]        =		padding_value
						A__							:    Union[str, Any]        =		sampling_rate
						A__							:    Any        =		return_attention_mask
						A__							:    List[Any]        =		do_normalize






					def   __A			( self			):
						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}






					def   __A			( self    ,       A__=False    ,       A__=False			):
						def _flatten(A__			):
							return list(itertools.chain(*A__			)			)

						if equal_length:
							A__							:    List[Any]        =		floats_list((self.batch_size, self.max_seq_length)			)
						else:
							# make sure that inputs increase in size
							A__							:    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:
							A__							:    int        =		[np.asarray(A__			) for x in speech_inputs]

						return speech_inputs



class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Union[str, Any]  							=       WavaVecaFeatureExtractor






					def   __A			( self			):
						A__							:    Optional[int]        =		WavaVecaFeatureExtractionTester(self			)






					def   __A			( self    ,       A__			):
						self.assertTrue(np.all(np.mean(A__    ,       axis=0			) < 1e-3			)			)
						self.assertTrue(np.all(np.abs(np.var(A__    ,       axis=0			) - 1			) < 1e-3			)			)






					def   __A			( self			):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						A__							:    Any        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						# create three inputs of length 800, 1000, and 1200
						A__							:    List[str]        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Any        =		[np.asarray(A__			) for speech_input in speech_inputs]

						# Test not batched input
						A__							:    Any        =		feat_extract(speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						A__							:    int        =		feat_extract(np_speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)

						# Test batched
						A__							:    Optional[int]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						A__							:    List[str]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						for enc_seq_a, enc_seq_a in zip(A__    ,       A__			):
							self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)

						# Test 2-D numpy arrays are batched.
						A__							:    Any        =		[floats_list((1, x)			)[0] for x in (800, 800, 800)]
						A__							:    Union[str, Any]        =		np.asarray(A__			)
						A__							:    Optional[int]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						A__							:    Union[str, Any]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						for enc_seq_a, enc_seq_a in zip(A__    ,       A__			):
							self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)






					def   __A			( self			):
						A__							:    Optional[int]        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    Optional[int]        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]

						A__							:    List[str]        =		["""longest""", """max_length""", """do_not_pad"""]
						A__							:    Optional[Any]        =		[None, 1600, None]
						for max_length, padding in zip(A__    ,       A__			):
							A__							:    int        =		feat_extract(A__    ,       padding=A__    ,       max_length=A__    ,       return_tensors="""np"""			)
							A__							:    Union[str, Any]        =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800]			)
							self.assertTrue(input_values[0][800:].sum() < 1e-6			)
							self._check_zero_mean_unit_variance(input_values[1][:1000]			)
							self.assertTrue(input_values[0][1000:].sum() < 1e-6			)
							self._check_zero_mean_unit_variance(input_values[2][:1200]			)






					def   __A			( self			):
						A__							:    str        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    int        =		range(800    ,       1400    ,       200			)
						A__							:    List[Any]        =		[floats_list((1, x)			)[0] for x in lengths]

						A__							:    List[str]        =		["""longest""", """max_length""", """do_not_pad"""]
						A__							:    Optional[Any]        =		[None, 1600, None]

						for max_length, padding in zip(A__    ,       A__			):
							A__							:    List[Any]        =		feat_extract(A__    ,       max_length=A__    ,       padding=A__			)
							A__							:    Union[str, Any]        =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800]			)
							self._check_zero_mean_unit_variance(input_values[1][:1000]			)
							self._check_zero_mean_unit_variance(input_values[2][:1200]			)






					def   __A			( self			):
						A__							:    int        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    int        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    str        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=1000    ,       padding="""max_length"""    ,       return_tensors="""np"""			)
						A__							:    Tuple        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1]			)
						self._check_zero_mean_unit_variance(input_values[2]			)






					def   __A			( self			):
						A__							:    Tuple        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    Dict        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Union[str, Any]        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=1000    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						A__							:    Tuple        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1, :1000]			)
						self._check_zero_mean_unit_variance(input_values[2]			)

						# make sure that if max_length < longest -> then pad to max_length
						self.assertTrue(input_values.shape == (3, 1000)			)

						A__							:    Any        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Any        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=2000    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						A__							:    Tuple        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1, :1000]			)
						self._check_zero_mean_unit_variance(input_values[2]			)

						# make sure that if max_length > longest -> then pad to longest
						self.assertTrue(input_values.shape == (3, 1200)			)






					@require_torch
					def   __A			( self			):
						import torch

						A__							:    Tuple        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    Tuple        =		np.random.rand(100			).astype(np.floataa			)
						A__							:    List[str]        =		np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
							A__							:    Optional[int]        =		feature_extractor.pad([{"""input_values""": inputs}]    ,       return_tensors="""np"""			)
							self.assertTrue(np_processed.input_values.dtype == np.floataa			)
							A__							:    List[Any]        =		feature_extractor.pad([{"""input_values""": inputs}]    ,       return_tensors="""pt"""			)
							self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)






					@slow
					@require_torch
					def   __A			( self			):
						# this test makes sure that models that are using
						# group norm don't have their feature extractor return the
						# attention_mask
						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
							A__							:    List[Any]        =		WavaVecaConfig.from_pretrained(A__			)
							A__							:    List[str]        =		WavaVecaFeatureExtractor.from_pretrained(A__			)

							# only "layer" feature extraction norm should make use of
							# attention_mask
							self.assertEqual(feat_extract.return_attention_mask    ,       config.feat_extract_norm == """layer"""			)






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



