





'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.mpnet.modeling_tf_mpnet import (
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetModel,
		)

class     A_ :

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=13			,     A_=7			,     A_=True			,     A_=True			,     A_=False			,     A_=True			,     A_=99			,     A_=64			,     A_=2			,     A_=4			,     A_=64			,     A_="gelu"			,     A_=0.1			,     A_=0.1			,     A_=5_12			,     A_=16			,     A_=2			,     A_=0.02			,     A_=3			,     A_=4			,     A_=None			,     ):
								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   seq_length
								_UpperCamelCase											=   is_training
								_UpperCamelCase											=   use_input_mask
								_UpperCamelCase											=   use_token_type_ids
								_UpperCamelCase											=   use_labels
								_UpperCamelCase											=   vocab_size
								_UpperCamelCase											=   hidden_size
								_UpperCamelCase											=   num_hidden_layers
								_UpperCamelCase											=   num_attention_heads
								_UpperCamelCase											=   intermediate_size
								_UpperCamelCase											=   hidden_act
								_UpperCamelCase											=   hidden_dropout_prob
								_UpperCamelCase											=   attention_probs_dropout_prob
								_UpperCamelCase											=   max_position_embeddings
								_UpperCamelCase											=   type_vocab_size
								_UpperCamelCase											=   type_sequence_label_size
								_UpperCamelCase											=   initializer_range
								_UpperCamelCase											=   num_labels
								_UpperCamelCase											=   num_choices
								_UpperCamelCase											=   scope




			def       a	(					self   ):
								_UpperCamelCase											=   ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

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

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

								_UpperCamelCase											=   MPNetConfig(
								    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			,     initializer_range=self.initializer_range			,     )
								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




			def       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   TFMPNetModel(config=lowercase_   )
								_UpperCamelCase											=   {"input_ids": input_ids, "attention_mask": input_mask}
								_UpperCamelCase											=   model(lowercase_   )
								_UpperCamelCase											=   [input_ids, input_mask]
								_UpperCamelCase											=   model(lowercase_   )
								self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   TFMPNetForMaskedLM(config=lowercase_   )
								_UpperCamelCase											=   {"input_ids": input_ids, "attention_mask": input_mask}
								_UpperCamelCase											=   model(lowercase_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   TFMPNetForQuestionAnswering(config=lowercase_   )
								_UpperCamelCase											=   {
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								}
								_UpperCamelCase											=   model(lowercase_   )
								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       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   self.num_labels
								_UpperCamelCase											=   TFMPNetForSequenceClassification(lowercase_   )
								_UpperCamelCase											=   {"input_ids": input_ids, "attention_mask": input_mask}
								_UpperCamelCase											=   model(lowercase_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   self.num_choices
								_UpperCamelCase											=   TFMPNetForMultipleChoice(lowercase_   )
								_UpperCamelCase											=   tf.tile(tf.expand_dims(lowercase_			,     1   )			,     (1, self.num_choices, 1)   )
								_UpperCamelCase											=   tf.tile(tf.expand_dims(lowercase_			,     1   )			,     (1, self.num_choices, 1)   )
								_UpperCamelCase											=   {
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								}
								_UpperCamelCase											=   model(lowercase_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_choices)   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   self.num_labels
								_UpperCamelCase											=   TFMPNetForTokenClassification(lowercase_   )
								_UpperCamelCase											=   {"input_ids": input_ids, "attention_mask": input_mask}
								_UpperCamelCase											=   model(lowercase_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.prepare_config_and_inputs()
								((_UpperCamelCase)				,	(_UpperCamelCase)				,	(_UpperCamelCase)				,	(_UpperCamelCase)				,	(_UpperCamelCase)				,	(_UpperCamelCase))											=   config_and_inputs
								_UpperCamelCase											=   {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_tf
class     A_ (  lowerCamelCase__    ,			lowerCamelCase__    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    (
			    (
			        TFMPNetForMaskedLM,
			        TFMPNetForMultipleChoice,
			        TFMPNetForQuestionAnswering,
			        TFMPNetForSequenceClassification,
			        TFMPNetForTokenClassification,
			        TFMPNetModel,
			    )
			    if is_tf_available()
			    else ()
			)
			_lowerCAmelCase						=    (
			    {
			        'feature-extraction': TFMPNetModel,
			        'fill-mask': TFMPNetForMaskedLM,
			        'question-answering': TFMPNetForQuestionAnswering,
			        'text-classification': TFMPNetForSequenceClassification,
			        'token-classification': TFMPNetForTokenClassification,
			        'zero-shot': TFMPNetForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			_lowerCAmelCase						=    False
			_lowerCAmelCase						=    False




			def       a	(					self   ):
								_UpperCamelCase											=   TFMPNetModelTester(self   )
								_UpperCamelCase											=   ConfigTester(self			,     config_class=lowercase_			,     hidden_size=37   )




			def       a	(					self   ):
								self.config_tester.run_common_tests()




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_model(*lowercase_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_masked_lm(*lowercase_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_   )




			@slow
			def       a	(					self   ):






								for model_name in ["microsoft/mpnet-base"]:
													_UpperCamelCase											=   TFMPNetModel.from_pretrained(lowercase_   )
													self.assertIsNotNone(lowercase_   )




@require_tf
class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   TFMPNetModel.from_pretrained("microsoft/mpnet-base"   )
								_UpperCamelCase											=   tf.constant([[0, 1, 2, 3, 4, 5]]   )
								_UpperCamelCase											=   model(lowercase_   )[0]

								_UpperCamelCase											=   [1, 6, 7_68]
								self.assertEqual(output.shape			,     lowercase_   )

								_UpperCamelCase											=   tf.constant(
								    [
								        [
								            [-0.106_7172, 0.0821_6473, 0.002_4543],
								            [-0.0346_5879, 0.835_4118, -0.0325_2288],
								            [-0.0656_9476, -0.1242_4111, -0.049_4436],
								        ]
								    ]   )
								tf.debugging.assert_near(output[:, :3, :3]			,     lowercase_			,     atol=1e-4   )










'''simple docstring'''



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()
snake_case_   :  Optional[Any]								=      logging.get_logger(__name__)

snake_case_   :  Optional[Any]								=      {
    '''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''',
}
snake_case_   :  Optional[Any]								=      [
    '''lm_head''',
]



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any]				)->   Tuple:






					"""simple docstring"""

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

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

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



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : List[str]				)->   List[str]:






					"""simple docstring"""

					_UpperCamelCase											=   []
					_UpperCamelCase											=   fairseq_model.state_dict()

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

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

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

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



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   path.split("."				)
					return reduce(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Dict ,   _UpperCamelCase  : List[str]				)->   List[str]:






					"""simple docstring"""

					_UpperCamelCase											=   access_by_string(_UpperCamelCase ,   _UpperCamelCase				)
					_UpperCamelCase											=   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."				)
					_UpperCamelCase											=   fsq_value
					logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."				)



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : str				)->   Dict:






					"""simple docstring"""

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

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

					set_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   f"conv_layers.{layer_id}.{layer_type}.{weight_type}"				)



def  lowercase__(		_UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : int				)->   Dict:






					"""simple docstring"""

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

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

					set_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   f"layers.{layer_id}.{layer_type}.{weight_type}"				)



@torch.no_grad()
def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any]=None ,   _UpperCamelCase  : List[Any]=None ,   _UpperCamelCase  : Optional[Any]=True				)->   Optional[int]:






					"""simple docstring"""

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

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

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

					def load_dataavec(_UpperCamelCase  : List[Any]				):
										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
										return model[0].eval()

					_UpperCamelCase											=   load_dataavec(_UpperCamelCase				)

					recursively_load_weights(_UpperCamelCase ,   _UpperCamelCase ,   not is_finetuned				)

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

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

					_UpperCamelCase											=   processor(_UpperCamelCase ,   return_tensors="pt" ,   padding=_UpperCamelCase				)

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

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

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

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

					print(our_output.shape ,   their_output.shape				)
					_UpperCamelCase											=   torch.max(torch.abs(our_output - their_output				)				).item()
					print(f"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
					_UpperCamelCase											=   torch.allclose(_UpperCamelCase ,   _UpperCamelCase ,   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(_UpperCamelCase				)

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


if __name__ == "__main__":
			snake_case_   :  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'''
			)
			snake_case_   :  Any								=      parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)



