






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

__lowerCamelCase      :    int   		=   random.Random()







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							str=1.0  ,						snake_case_      :							Dict=None  ,						snake_case_      :							Tuple=None     ):
	if rng is None:
		snake_case__				:   Optional[int]     =						global_rng

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

	return values



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						int=7							,	__A  :						Any=4_0_0							,	__A  :						Tuple=2_0_0_0							,	__A  :						Tuple=2_0_4_8							,	__A  :						List[str]=1_2_8							,	__A  :						Dict=1							,	__A  :						int=5_1_2							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_4_1_0_0							,	):
						snake_case__				:   str     =						parent
						snake_case__				:   Optional[Any]     =						batch_size
						snake_case__				:   Optional[Any]     =						min_seq_length
						snake_case__				:   Optional[int]     =						max_seq_length
						snake_case__				:   List[Any]     =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						snake_case__				:   List[str]     =						spectrogram_length
						snake_case__				:   Union[str, Any]     =						feature_size
						snake_case__				:   int     =						num_audio_channels
						snake_case__				:   Tuple     =						hop_length
						snake_case__				:   str     =						chunk_length
						snake_case__				:   Optional[int]     =						sampling_rate



					def  _lowercase  (      self  :						str ):
						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  _lowercase  (      self  :						int							,	__A  :						Tuple=False							,	__A  :						int=False ):
						def _flatten(__A  :						Union[str, Any] ):
							return list(itertools.chain(*__A ) )

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



@require_torch
@require_torchaudio
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	TvltFeatureExtractor



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						TvltFeatureExtractionTester(self )



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Tuple     =						self.feature_extraction_class(**self.feat_extract_dict )
						self.assertTrue(hasattr(__A							,	"spectrogram_length" ) )
						self.assertTrue(hasattr(__A							,	"feature_size" ) )
						self.assertTrue(hasattr(__A							,	"num_audio_channels" ) )
						self.assertTrue(hasattr(__A							,	"hop_length" ) )
						self.assertTrue(hasattr(__A							,	"chunk_length" ) )
						self.assertTrue(hasattr(__A							,	"sampling_rate" ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   int     =						self.feature_extraction_class(**self.feat_extract_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   Optional[Any]     =						feat_extract_first.save_pretrained(__A )[0]
							check_json_file_has_correct_format(__A )
							snake_case__				:   List[Any]     =						self.feature_extraction_class.from_pretrained(__A )

						snake_case__				:   List[str]     =						feat_extract_first.to_dict()
						snake_case__				:   str     =						feat_extract_second.to_dict()
						snake_case__				:   List[Any]     =						dict_first.pop("mel_filters" )
						snake_case__				:   Any     =						dict_second.pop("mel_filters" )
						self.assertTrue(np.allclose(__A							,	__A ) )
						self.assertEqual(__A							,	__A )



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Dict     =						self.feature_extraction_class(**self.feat_extract_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   List[str]     =						os.path.join(__A							,	"feat_extract.json" )
							feat_extract_first.to_json_file(__A )
							snake_case__				:   Any     =						self.feature_extraction_class.from_json_file(__A )

						snake_case__				:   Dict     =						feat_extract_first.to_dict()
						snake_case__				:   Any     =						feat_extract_second.to_dict()
						snake_case__				:   List[str]     =						dict_first.pop("mel_filters" )
						snake_case__				:   int     =						dict_second.pop("mel_filters" )
						self.assertTrue(np.allclose(__A							,	__A ) )
						self.assertEqual(__A							,	__A )



					def  _lowercase  (      self  :						List[str] ):
						# Initialize feature_extractor
						snake_case__				:   Union[str, Any]     =						self.feature_extraction_class(**self.feat_extract_dict )

						# create three inputs of length 800, 1000, and 1200
						snake_case__				:   Dict     =						[floats_list((1, x) )[0] for x in range(8_0_0							,	1_4_0_0							,	2_0_0 )]
						snake_case__				:   Union[str, Any]     =						[np.asarray(__A ) for speech_input in speech_inputs]

						# Test not batched input
						snake_case__				:   Optional[int]     =						feature_extractor(np_speech_inputs[0]							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).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
						snake_case__				:   Dict     =						feature_extractor(__A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).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
						snake_case__				:   Union[str, Any]     =						feature_extractor(
						    __A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0							,	mask_audio=__A ).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.
						snake_case__				:   Any     =						[floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						snake_case__				:   str     =						np.asarray(__A )
						snake_case__				:   Tuple     =						feature_extractor(__A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).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  _lowercase  (      self  :						Optional[int]							,	__A  :						Dict ):
						snake_case__				:   List[Any]     =						load_dataset("hf-internal-testing/librispeech_asr_dummy"							,	"clean"							,	split="validation" )
						# automatic decoding with librispeech
						snake_case__				:   Tuple     =						ds.sort("id" ).select(range(__A ) )[:num_samples]["audio"]

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






					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Dict     =						self._load_datasamples(1 )
						snake_case__				:   Any     =						TvltFeatureExtractor()
						snake_case__				:   List[str]     =						feature_extractor(__A							,	return_tensors="pt" ).audio_values

						self.assertEquals(audio_values.shape							,	(1, 1, 1_9_2, 1_2_8) )

						snake_case__				:   Optional[int]     =						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]							,	__A							,	atol=1e-4 ) )











import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowerCamelCase      :    int   		=   logging.get_logger(__name__)

__lowerCamelCase      :    int   		=   {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
__lowerCamelCase      :    Tuple   		=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   int     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Optional[Any]     =						getattr(snake_case_  ,						snake_case_     ).shape
	else:
		snake_case__				:   List[str]     =						hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
		    F''' {value.shape} for {full_name}'''     )

	if weight_type == "weight":
		snake_case__				:   str     =						value
	elif weight_type == "weight_g":
		snake_case__				:   Union[str, Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "bias":
		snake_case__				:   str     =						value
	else:
		snake_case__				:   Union[str, Any]     =						value

	logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   str     =						[]
	snake_case__				:   Optional[int]     =						fairseq_model.state_dict()

	snake_case__				:   int     =						hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case__				:   Dict     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   str     =						True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case__				:   Optional[int]     =						"unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("w2v_model."     )[-1] == name.split("."     )[0]:
					if "layer_norm_for_extract" in name and (".".join(name.split("."     )[:-1]     ) != key):
						# special case since naming is very similar
						continue
					snake_case__				:   int     =						True
					if "*" in mapped_key:
						snake_case__				:   Any     =						name.split(snake_case_     )[0].split("."     )[-2]
						snake_case__				:   Any     =						mapped_key.replace("*"  ,						snake_case_     )
					if "weight_g" in name:
						snake_case__				:   List[Any]     =						"weight_g"
					elif "weight_v" in name:
						snake_case__				:   Optional[Any]     =						"weight_v"
					elif "bias" in name:
						snake_case__				:   Optional[Any]     =						"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case__				:   Optional[Any]     =						"weight"
					else:
						snake_case__				:   Optional[Any]     =						None
					set_recursively(snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_     )
				continue
		if not is_used:
			unused_weights.append(snake_case_     )

	logger.warning(F'''Unused weights: {unused_weights}'''     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   Tuple     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Union[str, Any]     =						name.split("."     )
	snake_case__				:   str     =						int(items[0]     )
	snake_case__				:   str     =						int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
			snake_case__				:   Any     =						value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
			snake_case__				:   Any     =						value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''     )
			snake_case__				:   Optional[Any]     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''     )
			snake_case__				:   int     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
	else:
		unused_weights.append(snake_case_     )







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Any=True     ):
	if config_path is not None:
		snake_case__				:   Tuple     =						UniSpeechSatConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   Tuple     =						UniSpeechSatConfig()

	snake_case__				:   str     =						""

	if is_finetuned:
		snake_case__				:   Tuple     =						UniSpeechSatForCTC(snake_case_     )
	else:
		snake_case__				:   Any     =						UniSpeechSatForPreTraining(snake_case_     )

	snake_case__,		snake_case__,		snake_case__				:   Any     =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]  ,						arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
	snake_case__				:   Tuple     =						model[0].eval()

	recursively_load_weights(snake_case_  ,						snake_case_     )

	hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    int   		=   argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	__lowerCamelCase      :    List[Any]   		=   parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)



