




"""simple docstring"""




import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    YosoForMaskedLM,
		    YosoForMultipleChoice,
		    YosoForQuestionAnswering,
		    YosoForSequenceClassification,
		    YosoForTokenClassification,
		    YosoModel,
		)
		from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					int=13 ,				__lowerCamelCase    :					Any=7 ,				__lowerCamelCase    :					List[str]=True ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					Dict=True ,				__lowerCamelCase    :					Tuple=True ,				__lowerCamelCase    :					Tuple=99 ,				__lowerCamelCase    :					Optional[int]=32 ,				__lowerCamelCase    :					List[str]=5 ,				__lowerCamelCase    :					Any=4 ,				__lowerCamelCase    :					int=37 ,				__lowerCamelCase    :					str="gelu" ,				__lowerCamelCase    :					List[str]=0.1 ,				__lowerCamelCase    :					Optional[int]=0.1 ,				__lowerCamelCase    :					Tuple=5_12 ,				__lowerCamelCase    :					int=16 ,				__lowerCamelCase    :					List[str]=2 ,				__lowerCamelCase    :					Tuple=0.02 ,				__lowerCamelCase    :					int=3 ,				__lowerCamelCase    :					Optional[int]=4 ,				__lowerCamelCase    :					Dict=None ,				) ->					Tuple:
							a   =      parent
							a   =      batch_size
							a   =      seq_length
							a   =      is_training
							a   =      use_input_mask
							a   =      use_token_type_ids
							a   =      use_labels
							a   =      vocab_size
							a   =      hidden_size
							a   =      num_hidden_layers
							a   =      num_attention_heads
							a   =      intermediate_size
							a   =      hidden_act
							a   =      hidden_dropout_prob
							a   =      attention_probs_dropout_prob
							a   =      max_position_embeddings
							a   =      type_vocab_size
							a   =      type_sequence_label_size
							a   =      initializer_range
							a   =      num_labels
							a   =      num_choices
							a   =      scope





				def 							__UpperCAmelCase							(				self    :					int							) ->					str:
							a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

							a   =      None
							if self.use_input_mask:
										a   =      random_attention_mask([self.batch_size, self.seq_length]							)

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

							a   =      None
							a   =      None
							a   =      None
							if self.use_labels:
										a   =      ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
										a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)
										a   =      ids_tensor([self.batch_size] ,				self.num_choices							)

							a   =      self.get_config()

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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Dict:
							return YosoConfig(
							    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 ,				is_decoder=SCREAMING_SNAKE_CASE__ ,				initializer_range=self.initializer_range ,				)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					int:
							a   =      self.get_config()
							a   =      3_00
							return config





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Union[str, Any]:
							(
							    a      
							)   =      self.prepare_config_and_inputs()

							a   =      True
							a   =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
							a   =      ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2							)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Any							) ->					List[str]:
							a   =      YosoModel(config=SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__							)
							a   =      model(SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__							)
							a   =      model(SCREAMING_SNAKE_CASE__							)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					str ,				) ->					List[str]:
							a   =      True
							a   =      YosoModel(SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(
							    SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,				encoder_attention_mask=SCREAMING_SNAKE_CASE__ ,				)
							a   =      model(
							    SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,				)
							a   =      model(SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__							)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Union[str, Any]							) ->					int:
							a   =      YosoForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				labels=SCREAMING_SNAKE_CASE__							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[int]							) ->					Optional[int]:
							a   =      YosoForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(
							    SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				start_positions=SCREAMING_SNAKE_CASE__ ,				end_positions=SCREAMING_SNAKE_CASE__ ,				)
							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 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[Any]							) ->					List[str]:
							a   =      self.num_labels
							a   =      YosoForSequenceClassification(SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				labels=SCREAMING_SNAKE_CASE__							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any							) ->					Optional[Any]:
							a   =      self.num_labels
							a   =      YosoForTokenClassification(config=SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      model(SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				labels=SCREAMING_SNAKE_CASE__							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[Any]							) ->					Tuple:
							a   =      self.num_choices
							a   =      YosoForMultipleChoice(config=SCREAMING_SNAKE_CASE__							)
							model.to(SCREAMING_SNAKE_CASE__							)
							model.eval()
							a   =      input_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
							a   =      token_type_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
							a   =      input_mask.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
							a   =      model(
							    SCREAMING_SNAKE_CASE__ ,				attention_mask=SCREAMING_SNAKE_CASE__ ,				token_type_ids=SCREAMING_SNAKE_CASE__ ,				labels=SCREAMING_SNAKE_CASE__ ,				)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Union[str, Any]:
							a   =      self.prepare_config_and_inputs()
							(
							    a      
							)   =      config_and_inputs
							a   =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class    snake_case__							(_a   ,	_a   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= (
				    (
				        YosoModel,
				        YosoForMaskedLM,
				        YosoForMultipleChoice,
				        YosoForQuestionAnswering,
				        YosoForSequenceClassification,
				        YosoForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_	:							Any										= False
				SCREAMING_SNAKE_CASE_	:							Tuple										= False
				SCREAMING_SNAKE_CASE_	:							Dict										= False

				SCREAMING_SNAKE_CASE_	:							Any										= ()
				SCREAMING_SNAKE_CASE_	:							int										= (
				    {
				        '''feature-extraction''': YosoModel,
				        '''fill-mask''': YosoForMaskedLM,
				        '''question-answering''': YosoForQuestionAnswering,
				        '''text-classification''': YosoForSequenceClassification,
				        '''token-classification''': YosoForTokenClassification,
				        '''zero-shot''': YosoForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
							a   =      YosoModelTester(self							)
							a   =      ConfigTester(self ,				config_class=SCREAMING_SNAKE_CASE__ ,				hidden_size=37							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Union[str, Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					str:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
							a   =      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										a   =      type
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					int							) ->					List[str]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Any:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Tuple:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[Any]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Optional[int]:
							for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a   =      YosoModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Any:
							return




@require_torch
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					int							) ->					List[Any]:
							a   =      YosoModel.from_pretrained("uw-madison/yoso-4096"							)
							a   =      torch.tensor([[0, 1, 2, 3, 4, 5]]							)

							with torch.no_grad():
										a   =      model(SCREAMING_SNAKE_CASE__							)[0]

							a   =      torch.Size((1, 6, 7_68)							)
							self.assertEqual(output.shape ,				SCREAMING_SNAKE_CASE__							)

							a   =      torch.tensor(
							    [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]]							)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				SCREAMING_SNAKE_CASE__ ,				atol=1e-4							)							)





				@slow
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					int:
							a   =      YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"							)
							a   =      torch.tensor([[0, 1, 2, 3, 4, 5]]							)

							with torch.no_grad():
										a   =      model(SCREAMING_SNAKE_CASE__							)[0]

							a   =      5_02_65

							a   =      torch.Size((1, 6, vocab_size)							)
							self.assertEqual(output.shape ,				SCREAMING_SNAKE_CASE__							)

							a   =      torch.tensor(
							    [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]]							)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				SCREAMING_SNAKE_CASE__ ,				atol=1e-4							)							)





				@slow
				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
							a   =      YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"							)
							a   =      torch.arange(40_96							).unsqueeze(0							)

							with torch.no_grad():
										a   =      model(SCREAMING_SNAKE_CASE__							)[0]

							a   =      5_02_65

							a   =      torch.Size((1, 40_96, vocab_size)							)
							self.assertEqual(output.shape ,				SCREAMING_SNAKE_CASE__							)

							a   =      torch.tensor(
							    [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]]							)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				SCREAMING_SNAKE_CASE__ ,				atol=1e-4							)							)












import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__lowerCAmelCase		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__lowerCAmelCase		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			for attribute in key.split("."       ):
						a   =      getattr(A,  A       )

			if weight_type is not None:
						a   =      getattr(A,  A       ).shape
			else:
						a   =      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":
						a   =      value
			elif weight_type == "weight_g":
						a   =      value
			elif weight_type == "weight_v":
						a   =      value
			elif weight_type == "bias":
						a   =      value
			else:
						a   =      value

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




def       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






			'''simple docstring'''







			a   =      []
			a   =      fairseq_model.state_dict()

			if not is_headless:
						a   =      hf_model.dataavec_audio.feature_extractor
						a   =      hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						a   =      hf_model.feature_extractor
						a   =      hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						a   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															a   =      "data2vec_audio." + 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]:
															a   =      True
															if "*" in mapped_key:
																		a   =      name.split(A       )[0].split("."       )[-2]
																		a   =      mapped_key.replace("*",  A       )
															if "weight_g" in name:
																		a   =      "weight_g"
															elif "weight_v" in name:
																		a   =      "weight_v"
															elif "bias" in name:
																		a   =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a   =      "weight"
															else:
																		a   =      None
															set_recursively(A,  A,  A,  A,  A       )
												continue
						if not is_used:
									unused_weights.append(A       )

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




def       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      access_by_string(A,  A       )
			a   =      hf_weight.data

			if fsq_value.shape != hf_value.shape:
						raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""       )
			a   =      fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      full_name.split("conv_layers."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id == 0:
						a   =      "conv"
			elif type_id == 2:
						a   =      "layer_norm"
			else:
						unused_weights.append(A       )
						return

			set_weights(A,  A,  A,  F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''simple docstring'''







			a   =      full_name.split("pos_conv."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id != 0:
						unused_weights.append(A       )
						return
			else:
						a   =      "conv"

			set_weights(A,  A,  A,  F"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			if config_path is not None:
						a   =      DataaVecAudioConfig.from_pretrained(A       )
			else:
						a   =      DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						a   =      DataaVecAudioModel(A       )
						a   =      os.path.dirname(A       )

						a   =      torch.load(A       )
						a   =      state_dict["model"].pop("final_proj.0.weight"       )
						a   =      state_dict["model"].pop("final_proj.0.bias"       )
						a   =      os.path.join(A,  "converted.pt"       )
						torch.save(A,  A       )
			else:
						a   =      DataaVecAudioForCTC(A       )
						a   =      checkpoint_path

			def load_dataavec(A		: Optional[Any]       ):
						a   ,		a   ,		a   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
						return model[0].eval()

			a   =      load_dataavec(A       )

			recursively_load_weights(A,  A,  not is_finetuned       )

			a   =      WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"       )

			a   =      load_dataset("patrickvonplaten/librispeech_asr_dummy",  "clean",  split="validation"       )
			a   =      [x["array"] for x in ds[:4]["audio"]]

			a   =      processor(A,  return_tensors="pt",  padding=A       )

			a   =      inputs.input_values
			a   =      inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

			hf_wavavec.eval()
			model.eval()
			if is_finetuned:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "encoder_out"
						].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["logits"]

						a   =      torch.argmax(A,  dim=-1       )
						a   =      processor.batch_decode(A       )

						print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""       )
			else:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "layer_results"
						][-1][0].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["last_hidden_state"]

			print(our_output.shape,  their_output.shape       )
			a   =      torch.max(torch.abs(our_output - their_output       )       ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
			a   =      torch.allclose(A,  A,  atol=1E-3       )
			print("Do both models output the same tensors?",  "🔥" if success else "💩"       )
			if not success:
						raise Exception("Something went wRoNg"       )

			hf_wavavec.save_pretrained(A       )

			if is_finetuned:
						processor.save_pretrained(A       )
			else:
						processor.feature_extractor.save_pretrained(A       )


if __name__ == "__main__":
		__lowerCAmelCase		:    List[str]	      =		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		:    int	      =		parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)






