

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


SCREAMING_SNAKE_CASE_        =      random.Random()

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  str=1.0  ,      lowerCAmelCase:  int=None  ,      lowerCAmelCase:  Any=None )   ->       str:
				if rng is None:
								_UpperCAmelCase       :		Union[str, Any] 		=    global_rng

				_UpperCAmelCase       :		Dict 		=    []
				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 				a  (	unittest.TestCase				):


						def __init__(       self						,					A_						,					A_=7						,					A_=400						,					A_=2000						,					A_=1						,					A_=0.0						,					A_=16000						,					A_=True						,					A_=80						,					A_=16						,					A_=64						,					A_="hann_window"						,					A_=80						,					A_=7600						,					A_=1e-10						,					A_=True						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    parent
										_UpperCAmelCase       :		Tuple 		=    batch_size
										_UpperCAmelCase       :		Any 		=    min_seq_length
										_UpperCAmelCase       :		Optional[Any] 		=    max_seq_length
										_UpperCAmelCase       :		Any 		=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										_UpperCAmelCase       :		Union[str, Any] 		=    feature_size
										_UpperCAmelCase       :		str 		=    padding_value
										_UpperCAmelCase       :		Tuple 		=    sampling_rate
										_UpperCAmelCase       :		Dict 		=    do_normalize
										_UpperCAmelCase       :		List[str] 		=    num_mel_bins
										_UpperCAmelCase       :		Any 		=    hop_length
										_UpperCAmelCase       :		List[str] 		=    win_length
										_UpperCAmelCase       :		str 		=    win_function
										_UpperCAmelCase       :		Optional[int] 		=    fmin
										_UpperCAmelCase       :		List[Any] 		=    fmax
										_UpperCAmelCase       :		Optional[int] 		=    mel_floor
										_UpperCAmelCase       :		Optional[Any] 		=    return_attention_mask


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "do_normalize": self.do_normalize,
										    "num_mel_bins": self.num_mel_bins,
										    "hop_length": self.hop_length,
										    "win_length": self.win_length,
										    "win_function": self.win_function,
										    "fmin": self.fmin,
										    "fmax": self.fmax,
										    "mel_floor": self.mel_floor,
										    "return_attention_mask": self.return_attention_mask,
										}


						def        _UpperCAmelCase				(       self						,					A_=False						,					A_=False	):







										'''simple docstring'''


										def _flatten(A_	):
														return list(itertools.chain(*A_	)	)

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

										return speech_inputs





						def        _UpperCAmelCase				(       self						,					A_=False						,					A_=False	):







										'''simple docstring'''


										if equal_length:
														_UpperCAmelCase       :		int 		=    [floats_list((self.max_seq_length, self.num_mel_bins)	) for _ in range(self.batch_size	)]
										else:
														# make sure that inputs increase in size
														_UpperCAmelCase       :		List[Any] 		=    [
														    floats_list((x, self.num_mel_bins)	)
														    for x in range(self.min_seq_length						,					self.max_seq_length						,					self.seq_length_diff	)
														]

										if numpify:
														_UpperCAmelCase       :		Union[str, Any] 		=    [np.asarray(A_	) for x in speech_inputs]

										return speech_inputs







@require_torch
class 				a  (	UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			SpeechTaFeatureExtractor


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    SpeechTaFeatureExtractionTester(self	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										# create three inputs of length 800, 1000, and 1200
										_UpperCAmelCase       :		Union[str, Any] 		=    [floats_list((1, x)	)[0] for x in range(800						,					1400						,					200	)]
										_UpperCAmelCase       :		Dict 		=    [np.asarray(A_	) for speech_input in speech_inputs]

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

										# Test batched
										_UpperCAmelCase       :		str 		=    feat_extract(A_						,					return_tensors="np"	).input_values
										_UpperCAmelCase       :		Optional[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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										_UpperCAmelCase       :		Optional[Any] 		=    ["longest", "max_length", "do_not_pad"]
										_UpperCAmelCase       :		str 		=    [None, 1600, None]
										for max_length, padding in zip(A_						,					A_	):
														_UpperCAmelCase       :		List[Any] 		=    feat_extract(A_						,					padding=A_						,					max_length=A_						,					return_tensors="np"	)
														_UpperCAmelCase       :		str 		=    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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										_UpperCAmelCase       :		Optional[int] 		=    ["longest", "max_length", "do_not_pad"]
										_UpperCAmelCase       :		Union[str, Any] 		=    [None, 1600, None]

										for max_length, padding in zip(A_						,					A_	):
														_UpperCAmelCase       :		List[str] 		=    feat_extract(A_						,					max_length=A_						,					padding=A_	)
														_UpperCAmelCase       :		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][:1200]	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										_UpperCAmelCase       :		int 		=    [floats_list((1, x)	)[0] for x in range(800						,					1400						,					200	)]
										_UpperCAmelCase       :		Any 		=    feat_extract(
										    A_						,					truncation=A_						,					max_length=1000						,					padding="max_length"						,					return_tensors="np"	)
										_UpperCAmelCase       :		Dict 		=    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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										_UpperCAmelCase       :		List[str] 		=    [floats_list((1, x)	)[0] for x in range(800						,					1400						,					200	)]
										_UpperCAmelCase       :		Dict 		=    feat_extract(
										    A_						,					truncation=A_						,					max_length=1000						,					padding="longest"						,					return_tensors="np"	)
										_UpperCAmelCase       :		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]	)

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

										_UpperCAmelCase       :		List[Any] 		=    [floats_list((1, x)	)[0] for x in range(800						,					1400						,					200	)]
										_UpperCAmelCase       :		Optional[Any] 		=    feat_extract(
										    A_						,					truncation=A_						,					max_length=2000						,					padding="longest"						,					return_tensors="np"	)
										_UpperCAmelCase       :		Dict 		=    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)	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										# create three inputs of length 800, 1000, and 1200
										_UpperCAmelCase       :		int 		=    [floats_list((1, x)	)[0] for x in range(800						,					1400						,					200	)]
										_UpperCAmelCase       :		int 		=    [np.asarray(A_	) for speech_input in speech_inputs]

										# Test feature size
										_UpperCAmelCase       :		Tuple 		=    feature_extractor(audio_target=A_						,					padding=A_						,					return_tensors="np"	).input_values
										self.assertTrue(input_values.ndim == 3	)
										self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins	)

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

										# Test batched
										_UpperCAmelCase       :		List[Any] 		=    feature_extractor(A_						,					return_tensors="np"	).input_values
										_UpperCAmelCase       :		List[str] 		=    feature_extractor(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.
										_UpperCAmelCase       :		Any 		=    [floats_list((1, x)	)[0] for x in (800, 800, 800)]
										_UpperCAmelCase       :		List[Any] 		=    np.asarray(A_	)
										_UpperCAmelCase       :		List[Any] 		=    feature_extractor(A_						,					return_tensors="np"	).input_values
										_UpperCAmelCase       :		Optional[Any] 		=    feature_extractor(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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.feat_extract_tester.prepare_inputs_for_target()
										_UpperCAmelCase       :		Optional[Any] 		=    self.feature_extraction_class(**self.feat_extract_dict	)
										_UpperCAmelCase       :		Tuple 		=    feat_extract.model_input_names[0]

										_UpperCAmelCase       :		Union[str, Any] 		=    BatchFeature({input_name: speech_inputs}	)

										self.assertTrue(all(len(A_	) == len(A_	) for x, y in zip(A_						,					processed_features[input_name]	)	)	)

										_UpperCAmelCase       :		Dict 		=    self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_	)
										_UpperCAmelCase       :		int 		=    BatchFeature({input_name: speech_inputs}						,					tensor_type="np"	)

										_UpperCAmelCase       :		Optional[Any] 		=    processed_features[input_name]

										if len(batch_features_input.shape	) < 3:
														_UpperCAmelCase       :		Any 		=    batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)


						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_	)
										_UpperCAmelCase       :		Optional[int] 		=    self.feature_extraction_class(**self.feat_extract_dict	)
										_UpperCAmelCase       :		Any 		=    feat_extract.model_input_names[0]

										_UpperCAmelCase       :		Optional[int] 		=    BatchFeature({input_name: speech_inputs}						,					tensor_type="pt"	)

										_UpperCAmelCase       :		Optional[int] 		=    processed_features[input_name]

										if len(batch_features_input.shape	) < 3:
														_UpperCAmelCase       :		Any 		=    batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)


						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.feature_extraction_class(**self.feat_extract_dict	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.feat_extract_tester.prepare_inputs_for_target()
										_UpperCAmelCase       :		Tuple 		=    feat_extract.model_input_names[0]

										_UpperCAmelCase       :		str 		=    BatchFeature({input_name: speech_inputs}	)

										_UpperCAmelCase       :		Tuple 		=    feat_extract.num_mel_bins  # hack!

										_UpperCAmelCase       :		List[Any] 		=    feat_extract.pad(A_						,					padding="longest"						,					return_tensors="np"	)[input_name]
										_UpperCAmelCase       :		List[str] 		=    feat_extract.pad(A_						,					padding="longest"						,					return_tensors="pt"	)[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1e-2	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.feat_extract_dict
										_UpperCAmelCase       :		int 		=    True
										_UpperCAmelCase       :		int 		=    self.feature_extraction_class(**A_	)
										_UpperCAmelCase       :		Optional[int] 		=    self.feat_extract_tester.prepare_inputs_for_target()
										_UpperCAmelCase       :		str 		=    [len(A_	) for x in speech_inputs]
										_UpperCAmelCase       :		List[str] 		=    feat_extract.model_input_names[0]

										_UpperCAmelCase       :		Tuple 		=    BatchFeature({input_name: speech_inputs}	)

										_UpperCAmelCase       :		Optional[Any] 		=    feat_extract.num_mel_bins  # hack!

										_UpperCAmelCase       :		Optional[int] 		=    feat_extract.pad(A_						,					padding="longest"						,					return_tensors="np"	)
										self.assertIn("attention_mask"						,					A_	)
										self.assertListEqual(list(processed.attention_mask.shape	)						,					list(processed[input_name].shape[:2]	)	)
										self.assertListEqual(processed.attention_mask.sum(-1	).tolist()						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.feat_extract_dict
										_UpperCAmelCase       :		List[Any] 		=    True
										_UpperCAmelCase       :		Union[str, Any] 		=    self.feature_extraction_class(**A_	)
										_UpperCAmelCase       :		List[Any] 		=    self.feat_extract_tester.prepare_inputs_for_target()
										_UpperCAmelCase       :		Optional[Any] 		=    [len(A_	) for x in speech_inputs]
										_UpperCAmelCase       :		Optional[int] 		=    feat_extract.model_input_names[0]

										_UpperCAmelCase       :		Optional[int] 		=    BatchFeature({input_name: speech_inputs}	)
										_UpperCAmelCase       :		str 		=    min(A_	)

										_UpperCAmelCase       :		Any 		=    feat_extract.num_mel_bins  # hack!

										_UpperCAmelCase       :		int 		=    feat_extract.pad(
										    A_						,					padding="max_length"						,					max_length=A_						,					truncation=A_						,					return_tensors="np"	)
										self.assertIn("attention_mask"						,					A_	)
										self.assertListEqual(
										    list(processed_pad.attention_mask.shape	)						,					[processed_pad[input_name].shape[0], max_length]	)
										self.assertListEqual(
										    processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()						,					[max_length for x in speech_inputs]	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										from datasets import load_dataset

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    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       :		Optional[Any] 		=    self._load_datasamples(1	)
										_UpperCAmelCase       :		Optional[int] 		=    SpeechTaFeatureExtractor()
										_UpperCAmelCase       :		Tuple 		=    feature_extractor(A_						,					return_tensors="pt"	).input_values
										self.assertEquals(input_values.shape						,					(1, 93680)	)
										self.assertTrue(torch.allclose(input_values[0, :30]						,					A_						,					atol=1e-6	)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    torch.tensor(
										    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
										     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
										     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
										     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]	)
										# fmt: on

										_UpperCAmelCase       :		List[Any] 		=    self._load_datasamples(1	)
										_UpperCAmelCase       :		Union[str, Any] 		=    SpeechTaFeatureExtractor()
										_UpperCAmelCase       :		List[str] 		=    feature_extractor(audio_target=A_						,					return_tensors="pt"	).input_values
										self.assertEquals(input_values.shape						,					(1, 366, 80)	)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]						,					A_						,					atol=1e-4	)	)





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(args.tf_model_dir  ,      "parameters.json" )
				_UpperCAmelCase       :		List[str] 		=    json.loads(open(lowerCAmelCase ).read() )
				if not params:
								raise ValueError(
								    F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
				if not args.output.endswith(".pt" ):
								_UpperCAmelCase       :		Union[str, Any] 		=    args.output + ".pt"
				_UpperCAmelCase       :		Dict 		=    OrderedDict()
				with tf.device("/CPU:0" ):
								_UpperCAmelCase       :		int 		=    tf.train.load_checkpoint(args.tf_model_dir )
								_UpperCAmelCase       :		int 		=    reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_UpperCAmelCase       :		Dict 		=    reader.get_tensor(lowerCAmelCase ).astype(np.floataa )
												if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
																continue
												if key_name.startswith("pasts/" ):
																if key_name.startswith("pasts/mlp" ):
																				_UpperCAmelCase       :		int 		=    int(key_name[9] )
																elif key_name.startswith("pasts/out" ):
																				_UpperCAmelCase       :		Any 		=    8
																_UpperCAmelCase       :		str 		=    "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/moe" ):
																_UpperCAmelCase       :		List[str] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/switch_gating/kernel" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/softmlp/kernel" ):
																				_UpperCAmelCase       :		List[str] 		=    "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
																				_UpperCAmelCase       :		str 		=    key_name[-9:-7]
																				for i in range(16 ):
																								_UpperCAmelCase       :		Optional[int] 		=    "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_UpperCAmelCase       :		Optional[int] 		=    (
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/mlp" ):
																_UpperCAmelCase       :		Tuple 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/p1/kernel" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p1/bias" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_UpperCAmelCase       :		Tuple 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/kernel" ):
																				_UpperCAmelCase       :		str 		=    "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/bias" ):
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/ln" ):
																_UpperCAmelCase       :		Union[str, Any] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.norm.bias" % player
																				_UpperCAmelCase       :		int 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.norm.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/att" ):
																_UpperCAmelCase       :		Optional[Any] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/qkv/kernel" ):
																				_UpperCAmelCase       :		List[Any] 		=    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_UpperCAmelCase       :		Dict 		=    state[:, 0, :, :]
																				_UpperCAmelCase       :		List[Any] 		=    state[:, 1, :, :]
																				_UpperCAmelCase       :		Tuple 		=    state[:, 2, :, :]
																				_UpperCAmelCase       :		List[str] 		=    (
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    (
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    (
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		List[Any] 		=    "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/o/kernel" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_UpperCAmelCase       :		Tuple 		=    (
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/an" ):
																_UpperCAmelCase       :		Optional[int] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.self_attn.norm.bias" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.norm.weight" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
												elif (
												    key_name.startswith("model/wte" )
												    or key_name.startswith("model/wpe" )
												    or key_name.startswith("model/ete" )
												):
																_UpperCAmelCase       :		Optional[Any] 		=    {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_UpperCAmelCase       :		Dict 		=    "model.%s.weight" % nlayer
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																if key_name.startswith("model/wte" ):
																				_UpperCAmelCase       :		List[str] 		=    "lm_head.weight"
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same in embedded
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/wob" ):
																_UpperCAmelCase       :		Dict 		=    "final_logits_bias"
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		str 		=    state.reshape((1, -1) )
																_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense/kernel":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.weight"
																_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense_1/bias":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.bias"
																_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
				torch.save(lowerCAmelCase  ,      args.output )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser(
					    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
					parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_tf_gptsan_to_pt(args)


