






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    AlbertForMaskedLM,
					    AlbertForMultipleChoice,
					    AlbertForPreTraining,
					    AlbertForQuestionAnswering,
					    AlbertForSequenceClassification,
					    AlbertForTokenClassification,
					    AlbertModel,
					)
					from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						List[Any]							,	__A  :						Optional[int]=1_3							,	__A  :						Optional[int]=7							,	__A  :						Optional[Any]=True							,	__A  :						int=True							,	__A  :						Optional[Any]=True							,	__A  :						Dict=True							,	__A  :						Tuple=9_9							,	__A  :						Optional[Any]=1_6							,	__A  :						Dict=3_6							,	__A  :						Optional[Any]=6							,	__A  :						List[Any]=6							,	__A  :						Tuple=6							,	__A  :						Any=3_7							,	__A  :						Optional[Any]="gelu"							,	__A  :						Tuple=0.1							,	__A  :						int=0.1							,	__A  :						List[Any]=5_1_2							,	__A  :						Tuple=1_6							,	__A  :						List[Any]=2							,	__A  :						Any=0.0_2							,	__A  :						int=3							,	__A  :						Any=4							,	__A  :						int=None							,	):
						snake_case__				:   Dict     =						parent
						snake_case__				:   Optional[Any]     =						batch_size
						snake_case__				:   List[str]     =						seq_length
						snake_case__				:   List[str]     =						is_training
						snake_case__				:   Optional[int]     =						use_input_mask
						snake_case__				:   List[Any]     =						use_token_type_ids
						snake_case__				:   List[Any]     =						use_labels
						snake_case__				:   Optional[Any]     =						vocab_size
						snake_case__				:   Optional[int]     =						embedding_size
						snake_case__				:   Optional[Any]     =						hidden_size
						snake_case__				:   Optional[int]     =						num_hidden_layers
						snake_case__				:   List[Any]     =						num_hidden_groups
						snake_case__				:   Any     =						num_attention_heads
						snake_case__				:   int     =						intermediate_size
						snake_case__				:   str     =						hidden_act
						snake_case__				:   Dict     =						hidden_dropout_prob
						snake_case__				:   List[Any]     =						attention_probs_dropout_prob
						snake_case__				:   Dict     =						max_position_embeddings
						snake_case__				:   Tuple     =						type_vocab_size
						snake_case__				:   int     =						type_sequence_label_size
						snake_case__				:   int     =						initializer_range
						snake_case__				:   List[str]     =						num_labels
						snake_case__				:   Optional[int]     =						num_choices
						snake_case__				:   Tuple     =						scope



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   List[str]     =						ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size )

						snake_case__				:   Dict     =						None
						if self.use_input_mask:
							snake_case__				:   List[Any]     =						random_attention_mask([self.batch_size, self.seq_length] )

						snake_case__				:   int     =						None
						if self.use_token_type_ids:
							snake_case__				:   str     =						ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size )

						snake_case__				:   Union[str, Any]     =						None
						snake_case__				:   List[Any]     =						None
						snake_case__				:   str     =						None
						if self.use_labels:
							snake_case__				:   List[Any]     =						ids_tensor([self.batch_size]							,	self.type_sequence_label_size )
							snake_case__				:   Tuple     =						ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels )
							snake_case__				:   List[str]     =						ids_tensor([self.batch_size]							,	self.num_choices )

						snake_case__				:   int     =						self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def  _lowercase  (      self  :						Optional[Any] ):
						return AlbertConfig(
						    vocab_size=self.vocab_size							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	max_position_embeddings=self.max_position_embeddings							,	type_vocab_size=self.type_vocab_size							,	initializer_range=self.initializer_range							,	num_hidden_groups=self.num_hidden_groups							,	)



					def  _lowercase  (      self  :						Dict							,	__A  :						Dict							,	__A  :						List[Any]							,	__A  :						Tuple							,	__A  :						Tuple							,	__A  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						str ):
						snake_case__				:   List[str]     =						AlbertModel(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   Dict     =						model(__A							,	attention_mask=__A							,	token_type_ids=__A )
						snake_case__				:   List[str]     =						model(__A							,	token_type_ids=__A )
						snake_case__				:   Tuple     =						model(__A )
						self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size) )
						self.parent.assertEqual(result.pooler_output.shape							,	(self.batch_size, self.hidden_size) )



					def  _lowercase  (      self  :						Any							,	__A  :						Dict							,	__A  :						Optional[Any]							,	__A  :						Optional[Any]							,	__A  :						Dict							,	__A  :						Union[str, Any]							,	__A  :						Optional[int]							,	__A  :						Dict ):
						snake_case__				:   List[Any]     =						AlbertForPreTraining(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   str     =						model(
						    __A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A							,	sentence_order_label=__A							,	)
						self.parent.assertEqual(result.prediction_logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size) )
						self.parent.assertEqual(result.sop_logits.shape							,	(self.batch_size, config.num_labels) )



					def  _lowercase  (      self  :						List[Any]							,	__A  :						int							,	__A  :						str							,	__A  :						Optional[Any]							,	__A  :						str							,	__A  :						List[str]							,	__A  :						int							,	__A  :						str ):
						snake_case__				:   Any     =						AlbertForMaskedLM(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   Union[str, Any]     =						model(__A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size) )



					def  _lowercase  (      self  :						List[str]							,	__A  :						int							,	__A  :						int							,	__A  :						Optional[int]							,	__A  :						Optional[int]							,	__A  :						Dict							,	__A  :						str							,	__A  :						int ):
						snake_case__				:   List[str]     =						AlbertForQuestionAnswering(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   List[Any]     =						model(
						    __A							,	attention_mask=__A							,	token_type_ids=__A							,	start_positions=__A							,	end_positions=__A							,	)
						self.parent.assertEqual(result.start_logits.shape							,	(self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape							,	(self.batch_size, self.seq_length) )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Tuple							,	__A  :						Any							,	__A  :						Tuple							,	__A  :						Optional[int]							,	__A  :						Dict							,	__A  :						int							,	__A  :						Any ):
						snake_case__				:   Any     =						self.num_labels
						snake_case__				:   str     =						AlbertForSequenceClassification(__A )
						model.to(__A )
						model.eval()
						snake_case__				:   Union[str, Any]     =						model(__A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels) )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Union[str, Any]							,	__A  :						Optional[Any]							,	__A  :						List[str]							,	__A  :						str							,	__A  :						Union[str, Any]							,	__A  :						int							,	__A  :						Union[str, Any] ):
						snake_case__				:   List[str]     =						self.num_labels
						snake_case__				:   Optional[int]     =						AlbertForTokenClassification(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   Tuple     =						model(__A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.num_labels) )



					def  _lowercase  (      self  :						str							,	__A  :						Optional[int]							,	__A  :						Any							,	__A  :						Tuple							,	__A  :						Union[str, Any]							,	__A  :						Optional[Any]							,	__A  :						str							,	__A  :						int ):
						snake_case__				:   str     =						self.num_choices
						snake_case__				:   Union[str, Any]     =						AlbertForMultipleChoice(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   Dict     =						input_ids.unsqueeze(1 ).expand(-1							,	self.num_choices							,	-1 ).contiguous()
						snake_case__				:   Any     =						token_type_ids.unsqueeze(1 ).expand(-1							,	self.num_choices							,	-1 ).contiguous()
						snake_case__				:   Tuple     =						input_mask.unsqueeze(1 ).expand(-1							,	self.num_choices							,	-1 ).contiguous()
						snake_case__				:   Optional[Any]     =						model(
						    __A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A							,	)
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_choices) )






					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   Union[str, Any]     =						self.prepare_config_and_inputs()
						(
						    snake_case__		
						)				:   Union[str, Any]     =						config_and_inputs
						snake_case__				:   Dict     =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict



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





					"""simple docstring"""






					a_			=	(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					a_			=	(
					    {
					        "feature-extraction": AlbertModel,
					        "fill-mask": AlbertForMaskedLM,
					        "question-answering": AlbertForQuestionAnswering,
					        "text-classification": AlbertForSequenceClassification,
					        "token-classification": AlbertForTokenClassification,
					        "zero-shot": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a_			=	True



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						List[str]							,	__A  :						Tuple							,	__A  :						Union[str, Any]=False ):
						snake_case__				:   Optional[Any]     =						super()._prepare_for_class(__A							,	__A							,	return_labels=__A )

						if return_labels:
							if model_class in get_values(__A ):
								snake_case__				:   Optional[Any]     =						torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.seq_length)							,	dtype=torch.long							,	device=__A )
								snake_case__				:   Optional[int]     =						torch.zeros(
								    self.model_tester.batch_size							,	dtype=torch.long							,	device=__A )
						return inputs_dict



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   int     =						AlbertModelTester(self )
						snake_case__				:   int     =						ConfigTester(self							,	config_class=__A							,	hidden_size=3_7 )



					def  _lowercase  (      self  :						str ):
						self.config_tester.run_common_tests()



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A )



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__A )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__A )



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   List[str]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__A )



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__A )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   List[str]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							snake_case__				:   List[str]     =						type
							self.model_tester.create_and_check_model(*__A )






					@slow
					def  _lowercase  (      self  :						str ):
						for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__				:   str     =						AlbertModel.from_pretrained(__A )
							self.assertIsNotNone(__A )



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@slow
					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Optional[int]     =						AlbertModel.from_pretrained("albert-base-v2" )
						snake_case__				:   Union[str, Any]     =						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
						snake_case__				:   Tuple     =						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
						with torch.no_grad():
							snake_case__				:   Any     =						model(__A							,	attention_mask=__A )[0]
						snake_case__				:   str     =						torch.Size((1, 1_1, 7_6_8) )
						self.assertEqual(output.shape							,	__A )
						snake_case__				:   List[str]     =						torch.tensor(
						    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,	__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
	)



