





'''simple docstring'''



import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
		from tests.trainer.test_trainer import (  # noqa
		    RegressionModelConfig,
		    RegressionPreTrainedModel,
		    get_regression_trainer,
		)


set_seed(42)

snake_case_   :  List[Any]								=      get_tests_dir('''fixtures''')
snake_case_   :  List[Any]								=      os.path.join(dirname(get_tests_dir()))
snake_case_   :  Optional[int]								=      dirname(os.path.abspath(__file__))

# default torch.distributed port
snake_case_   :  Dict								=      """10999"""

snake_case_   :  str								=      """t5-small"""

# *** Working Models ***
snake_case_   :  List[str]								=      """hf-internal-testing/tiny-albert"""
snake_case_   :  Dict								=      """sshleifer/bart-tiny-random"""
snake_case_   :  Any								=      """hf-internal-testing/tiny-bert"""
snake_case_   :  List[Any]								=      """hf-internal-testing/tiny-random-bigbird_pegasus"""
snake_case_   :  Optional[int]								=      """hf-internal-testing/tiny-random-big_bird"""
snake_case_   :  Optional[Any]								=      """hf-internal-testing/tiny-random-blenderbot"""
snake_case_   :  Optional[int]								=      """bigscience/bigscience-small-testing"""
snake_case_   :  List[Any]								=      """hf-internal-testing/tiny-random-deberta"""
snake_case_   :  Optional[int]								=      """hf-internal-testing/tiny-random-deberta-v2"""
snake_case_   :  Any								=      """sshleifer/tiny-distilbert-base-cased"""
snake_case_   :  str								=      """hf-internal-testing/tiny-electra"""
snake_case_   :  Optional[int]								=      """hf-internal-testing/tiny-random-flaubert"""
snake_case_   :  Tuple								=      """stas/tiny-wmt19-en-de"""
snake_case_   :  Any								=      """hf-internal-testing/tiny-random-funnel"""
snake_case_   :  Optional[int]								=      """sshleifer/tiny-gpt2"""
snake_case_   :  Optional[Any]								=      """hf-internal-testing/tiny-random-gptj"""
snake_case_   :  Union[str, Any]								=      """hf-internal-testing/tiny-random-gpt_neo"""
snake_case_   :  str								=      """hf-internal-testing/tiny-layoutlm"""
snake_case_   :  str								=      """hf-internal-testing/tiny-random-led"""
snake_case_   :  Tuple								=      """hf-internal-testing/tiny-random-longformer"""
snake_case_   :  Tuple								=      """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
snake_case_   :  Optional[Any]								=      """sshleifer/tiny-marian-en-de"""
snake_case_   :  int								=      """sshleifer/tiny-mbart"""
snake_case_   :  int								=      """hf-internal-testing/tiny-random-mobilebert"""
snake_case_   :  Any								=      """hf-internal-testing/tiny-random-mpnet"""
snake_case_   :  Optional[Any]								=      """stas/pegasus-cnn_dailymail-tiny-random"""
snake_case_   :  List[Any]								=      """hf-internal-testing/tiny-random-prophetnet"""
snake_case_   :  Any								=      """sshleifer/tiny-distilroberta-base"""
snake_case_   :  str								=      """hf-internal-testing/tiny-random-squeezebert"""
snake_case_   :  Union[str, Any]								=      """patrickvonplaten/t5-tiny-random"""
snake_case_   :  Dict								=      """hf-internal-testing/tiny-random-t5-v1.1"""
snake_case_   :  int								=      """hf-internal-testing/tiny-random-vit"""
snake_case_   :  List[str]								=      """hf-internal-testing/tiny-xlm-roberta"""
snake_case_   :  Tuple								=      """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
snake_case_   :  List[str]								=      """hf-internal-testing/tiny-random-mt5"""
snake_case_   :  str								=      """hf-internal-testing/tiny-random-camembert"""
snake_case_   :  Any								=      """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
snake_case_   :  Optional[int]								=      """hf-internal-testing/tiny-random-convbert"""
snake_case_   :  int								=      """hf-internal-testing/tiny-random-layoutlmv2"""
snake_case_   :  List[Any]								=      """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
snake_case_   :  List[str]								=      """hf-internal-testing/tiny-random-ctrl"""
snake_case_   :  Union[str, Any]								=      """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
snake_case_   :  Optional[int]								=      """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
snake_case_   :  Dict								=      """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
snake_case_   :  List[str]								=      """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case_   :  str								=      """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
snake_case_   :  Any								=      """"""
# - "luke" Entities classes - no plan to make such example
snake_case_   :  int								=      """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
snake_case_   :  int								=      """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
snake_case_   :  List[Any]								=      """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
snake_case_   :  int								=      """hf-internal-testing/tiny-random-tapas"""



def  lowercase__(		_UpperCamelCase  : Union[str, Any]=False				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   min(2 ,   get_gpu_count()				) if distributed else 1
					_UpperCamelCase											=   os.environ.get("DS_TEST_PORT" ,   __lowerCamelCase				)
					return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()



def  lowercase__(		)->   Any:






					"""simple docstring"""

					_UpperCamelCase											=   f"{FIXTURE_DIRECTORY}/tests_samples"
					_UpperCamelCase											=   f"{data_dir_samples}/wmt_en_ro"
					_UpperCamelCase											=   f"{data_dir_samples}/xsum"
					_UpperCamelCase											=   """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

					# try to cover as many models as possible once (it's enough to run on one task per model)
					# but need a tiny model for each
					#
					# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
					_UpperCamelCase											=   {
					    """trans""": [
					        """bart""",
					        """fsmt""",
					        """m2m_100""",
					        """marian""",
					        """mbart""",
					        """t5""",
					        """t5_v1""",
					        # "mt5", missing model files
					    ],
					    """sum""": [
					        """pegasus""",
					    ],
					    """clm""": [
					        """big_bird""",
					        """bigbird_pegasus""",
					        """blenderbot""",
					        """bloom""",
					        """gpt2""",
					        """gpt_neo""",
					        """gptj""",
					        """xlm-roberta""",
					        """prophetnet""",
					        # "camembert", missing model files
					    ],
					    """mlm""": [
					        """albert""",
					        """deberta""",
					        """deberta-v2""",
					        """distilbert""",
					        """electra""",
					        """flaubert""",
					        """funnel""",
					        """layoutlm""",
					        # "reformer", # multiple issues with either mlm/qa/clas
					    ],
					    """qa""": [
					        """led""",
					        """longformer""",
					        """mobilebert""",
					        """mpnet""",
					        """roberta""",
					        """squeezebert""",
					        # "convbert", # missing tokenizer files
					        # "layoutlmv2", missing model files
					    ],
					    """clas""": [
					        """bert""",
					        """xlnet""",
					        # "hubert", # missing tokenizer files
					        # "ibert", # multiple issues with either mlm/qa/clas
					        # "transfo-xl", # tokenizer issues as ctrl
					        # "ctrl", # tokenizer issues
					        # "openai-gpt", missing model files
					        # "tapas", multiple issues
					    ],
					    """img_clas""": [
					        """vit""",
					    ],
					}

					_UpperCamelCase											=   f"{ROOT_DIRECTORY}/examples/pytorch"

					_UpperCamelCase											=   {
					    """trans""": f"\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ",
					    """sum""": f"\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ",
					    """clm""": f"\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ",
					    """mlm""": f"\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ",
					    """qa""": f"\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ",
					    """clas""": f"\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ",
					    """img_clas""": f"\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ",
					}

					_UpperCamelCase											=   get_launcher(distributed=__lowerCamelCase				)

					_UpperCamelCase											=   {}
					for task, args in tasks.items():
										_UpperCamelCase											=   args.split()
										for model in tasksamodels[task]:
															_UpperCamelCase											=   globals()[f"{model.upper().replace('-' ,   '_'				)}_TINY"]
															_UpperCamelCase											=   f"--model_name_or_path {model_name}".split()
															_UpperCamelCase											=   launcher + args + args_model + args_main

															# # generation special case
															# if task == "gen":
															#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
															#     args_model += f"--model_type {model}".split()
															#     cmds[f"{task}_{model}"] = launcher + args + args_model
															# else:

					return cmds


snake_case_   :  Any								=      make_task_cmds()

snake_case_   :  Any								=      """zero2"""
snake_case_   :  Optional[Any]								=      """zero3"""

snake_case_   :  Optional[Any]								=      [ZEROa, ZEROa]



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






					"""simple docstring"""

					_UpperCamelCase											=   parameterized.to_safe_name("_".join(str(__lowerCamelCase				) for x in param.args				)				)
					return f"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
snake_case_   :  Tuple								=      list(itertools.product(stages, task_cmds.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class     A_ (  __lowerCamelCase  ):

			'''simple docstring'''





			def       a	(					self			,     A_			,     A_   ):
								# return a ready to run train cmd
								if task not in task_cmds:
													raise ValueError(F"don't know of task {task}, have {task_cmds.keys()}"   )

								_UpperCamelCase											=   task_cmds[task]
								_UpperCamelCase											=   F"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

								_UpperCamelCase											=   self.get_auto_remove_tmp_dir()
								_UpperCamelCase											=   F"--output_dir {output_dir}".split()

								cmd += args_ds + args_out

								return cmd, output_dir




			@parameterized.expand(_lowerCAmelCase			,     name_func=_lowerCAmelCase   )
			def       a	(					self			,     A_			,     A_   ):
								# testing the ability to do a run followed by recovery of full fp32 weights

								_UpperCamelCase											=   self.get_task_cmd(_lowerCAmelCase			,     _lowerCAmelCase   )

								# 1. generate the checkpoint
								cmd += "--save_steps 1".split()
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
								execute_subprocess_async(_lowerCAmelCase			,     env=self.get_env()   )

								# 2. test that the fp32 weights get reconsolidated
								_UpperCamelCase											=   F"{output_dir}/checkpoint-1"
								_UpperCamelCase											=   F"{chkpt_dir}/out.bin"
								_UpperCamelCase											=   F"{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								subprocess.check_call(_lowerCAmelCase			,     shell=_lowerCAmelCase   )
								assert os.path.exists(_lowerCAmelCase   ), F"{recovered_model_path} was not found"

								# possibly could also test that the resulting saved model is usable but given that we use
								# random models we won't know if it's any good










'''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
			)



