




import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		)
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
		__lowerCAmelCase		:    Tuple	      =		False




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Optional[Any] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					List[str]=14 ,				__lowerCamelCase    :					List[Any]=7 ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					Optional[Any]=False ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					Optional[int]=True ,				__lowerCamelCase    :					Dict=True ,				__lowerCamelCase    :					List[Any]=99 ,				__lowerCamelCase    :					str=32 ,				__lowerCamelCase    :					Optional[Any]=5 ,				__lowerCamelCase    :					List[Any]=37 ,				__lowerCamelCase    :					str="gelu" ,				__lowerCamelCase    :					str=0.1 ,				__lowerCamelCase    :					List[str]=0.1 ,				__lowerCamelCase    :					Any=5_12 ,				__lowerCamelCase    :					Optional[Any]=16 ,				__lowerCamelCase    :					Optional[int]=2 ,				__lowerCamelCase    :					List[str]=3 ,				__lowerCamelCase    :					List[str]=4 ,				__lowerCamelCase    :					List[Any]=None ,				) ->					Optional[int]:
							a   =      parent
							a   =      batch_size
							a   =      seq_length
							a   =      is_training
							a   =      use_token_type_ids
							a   =      use_input_mask
							a   =      use_labels
							a   =      use_mc_token_ids
							a   =      vocab_size
							a   =      hidden_size
							a   =      num_hidden_layers
							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   =      num_labels
							a   =      num_choices
							a   =      scope
							a   =      vocab_size - 1
							a   =      vocab_size - 1
							a   =      vocab_size - 1





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Dict:
							return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[str]=False ,				__lowerCamelCase    :					Optional[Any]=False ,				__lowerCamelCase    :					Tuple=False							) ->					Any:
							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
							if self.use_mc_token_ids:
										a   =      ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length							)

							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(
							    gradient_checkpointing=__lowerCamelCase ,				scale_attn_by_inverse_layer_idx=__lowerCamelCase ,				reorder_and_upcast_attn=__lowerCamelCase ,				)

							return (
							    config,
							    input_ids,
							    input_mask,
							    None,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					int=False ,				__lowerCamelCase    :					str=False ,				__lowerCamelCase    :					Tuple=False							) ->					List[str]:
							return RwkvConfig(
							    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				intermediate_size=self.intermediate_size ,				activation_function=self.hidden_act ,				resid_pdrop=self.hidden_dropout_prob ,				attn_pdrop=self.attention_probs_dropout_prob ,				n_positions=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				use_cache=__lowerCamelCase ,				bos_token_id=self.bos_token_id ,				eos_token_id=self.eos_token_id ,				pad_token_id=self.pad_token_id ,				gradient_checkpointing=__lowerCamelCase ,				scale_attn_by_inverse_layer_idx=__lowerCamelCase ,				reorder_and_upcast_attn=__lowerCamelCase ,				)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							a   =      self.get_config()
							a   =      3_00
							return config





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Tuple:
							(
							    (
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		
							)   =      self.prepare_config_and_inputs()

							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,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Optional[Any] ,				*__lowerCamelCase    :					int							) ->					str:
							a   =      True
							a   =      RwkvModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							a   =      model(__lowerCamelCase							)

							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)
							self.parent.assertEqual(len(result.hidden_states							) ,				config.num_hidden_layers + 1							)





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					str ,				*__lowerCamelCase    :					Optional[int]							) ->					Dict:
							a   =      RwkvForCausalLM(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							a   =      model(__lowerCamelCase ,				labels=__lowerCamelCase							)
							self.parent.assertEqual(result.loss.shape ,				()							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Union[str, Any] ,				*__lowerCamelCase    :					List[Any]							) ->					Tuple:
							a   =      RwkvModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							a   =      model(__lowerCamelCase							)
							a   =      outputs.last_hidden_state

							a   =      model(input_ids[:, :2]							)
							a   =      outputs.last_hidden_state

							# Using the state computed on the first inputs, we will get the same output
							a   =      model(input_ids[:, 2:] ,				state=outputs.state							)
							a   =      outputs.last_hidden_state

							self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,				dim=1							) ,				__lowerCamelCase ,				atol=1e-5							)							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				*__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Dict=False							) ->					Optional[int]:
							a   =      RwkvForCausalLM(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							if gradient_checkpointing:
										model.gradient_checkpointing_enable()

							a   =      model(__lowerCamelCase ,				labels=__lowerCamelCase							)
							self.parent.assertEqual(result.loss.shape ,				()							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)
							result.loss.backward()





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					List[Any]:
							a   =      self.prepare_config_and_inputs()

							(
							    (
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		
							)   =      config_and_inputs

							a   =      {"input_ids": input_ids}

							return config, inputs_dict




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,	reason="""See https://github.com/huggingface/transformers/pull/24204"""       )
@require_torch
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_	:							Any										= (
				    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
				)
				# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= False
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= False
				SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= False
				SCREAMING_SNAKE_CASE_	:							Tuple										= False
				SCREAMING_SNAKE_CASE_	:							List[str]										= False  # Rwkv does not support head masking





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Dict:
							a   =      RwkvModelTester(self							)
							a   =      ConfigTester(
							    self ,				config_class=__lowerCamelCase ,				n_embd=37 ,				common_properties=["hidden_size", "num_hidden_layers"]							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any]=None							) ->					int:
							if isinstance(__lowerCamelCase ,				torch.Tensor							):
										a   ,		a   =      member.max().item(), member.min().item()
							elif isinstance(__lowerCamelCase ,				__lowerCamelCase							) or isinstance(__lowerCamelCase ,				__lowerCamelCase							):
										a   ,		a   =      max(__lowerCamelCase							), min(__lowerCamelCase							)

							if not isinstance(__lowerCamelCase ,				__lowerCamelCase							):
										raise TypeError("container should be a list or tuple"							)
							elif len(__lowerCamelCase							) != 2:
										raise ValueError("container should have 2 elements"							)

							a   ,		a   =      container

							a   =      (min_value >= expected_min) and (max_value <= expected_max)

							if not is_inside_interval:
										a   =      "%s not found in %s" % (safe_repr(__lowerCamelCase							), safe_repr(__lowerCamelCase							))
										self.fail(self._formatMessage(__lowerCamelCase ,				__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					str:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_rwkv_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					List[str]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_causl_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str							) ->					Any:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_state_equivalency(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					int:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a   =      model_class(config=__lowerCamelCase							)
										for name, param in model.named_parameters():
													if "time_decay" in name:
																if param.requires_grad:
																			self.assertTrue(param.data.max().item() == 3.0							)
																			self.assertTrue(param.data.min().item() == -5.0							)
													elif "time_first" in name:
																if param.requires_grad:
																			# check if it's a ones like
																			self.assertTrue(torch.allclose(param.data ,				torch.ones_like(param.data							) ,				atol=1e-5 ,				rtol=1e-5							)							)
													elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]							):
																if param.requires_grad:
																			self.assertInterval(
																			    param.data ,				[0.0, 1.0] ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)
													elif "time_mix_value" in name:
																if param.requires_grad:
																			self.assertInterval(
																			    param.data ,				[0.0, 1.3] ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[int]:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
							a   =      True

							a   =      getattr(self.model_tester ,				"seq_length" ,				__lowerCamelCase							)

							for model_class in self.all_model_classes:
										a   =      True
										a   =      False
										a   =      True
										a   =      model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.eval()

										a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      inputs["input_ids"].shape[0]
										with torch.no_grad():
													a   =      model(**__lowerCamelCase							)
										a   =      outputs.attentions
										self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										a   =      True
										a   =      model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.eval()

										a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      inputs["input_ids"].shape[0]
										with torch.no_grad():
													a   =      model(**__lowerCamelCase							)
										a   =      outputs.attentions
										self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)

										self.assertListEqual(
										    list(attentions[0].shape[-3:]							) ,				[batch_size, seq_len, config.hidden_size] ,				)
										a   =      len(__lowerCamelCase							)

										# Check attention is always last and order is fine
										a   =      True
										a   =      True
										a   =      model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.eval()

										a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      inputs["input_ids"].shape[0]
										with torch.no_grad():
													a   =      model(**__lowerCamelCase							)

										a   =      1
										self.assertEqual(out_len + added_hidden_states ,				len(__lowerCamelCase							)							)

										a   =      outputs.attentions

										self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]							) ,				[batch_size, seq_len, config.hidden_size] ,				)





				@slow
				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Dict:
							for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a   =      RwkvModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,	reason="""See https://github.com/huggingface/transformers/pull/24204"""       )
@slow
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Tuple:
							a   =      "RWKV/rwkv-4-169m-pile"
							a   =      AutoTokenizer.from_pretrained(self.model_id							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Dict:
							a   =      "Hello my name is Jasmine and I am a newbie to the"
							a   =      RwkvForCausalLM.from_pretrained(self.model_id							).to(__lowerCamelCase							)

							a   =      self.tokenizer("Hello my name is" ,				return_tensors="pt"							).input_ids.to(__lowerCamelCase							)
							a   =      model.generate(__lowerCamelCase ,				max_new_tokens=10							)
							a   =      self.tokenizer.decode(output[0].tolist()							)

							self.assertEqual(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
							a   =      "Hello my name is Jasmine and I am a newbie to the"

							a   =      self.tokenizer("Hello my name is" ,				return_tensors="pt"							).input_ids.to(__lowerCamelCase							)
							a   =      RwkvForCausalLM.from_pretrained(self.model_id ,				torch_dtype=torch.bfloataa							).to(__lowerCamelCase							)

							a   =      model.generate(__lowerCamelCase ,				max_new_tokens=10							)
							a   =      self.tokenizer.decode(output[0].tolist()							)

							self.assertEqual(__lowerCamelCase ,				__lowerCamelCase							)












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
		)






