



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__a      =				logging.get_logger(__name__)

__a      =				{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__a      =				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						for attribute in key.split("""."""	):
												if is_finetuned:
																		if attribute in ["quantizer", "project_q", "project_hid"]:
																								# those layers are only relevant for pretraining and should be dropped
																								return

																		if attribute == "ctc_proj":
																								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																								snake_case_ :List[Any]						=			"""lm_head"""

												snake_case_ :Union[str, Any]						=			getattr(_lowercase,					_lowercase	)

						if weight_type is not None:
												snake_case_ :str						=			getattr(_lowercase,					_lowercase	).shape
						else:
												snake_case_ :Optional[Any]						=			hf_pointer.shape

						assert hf_shape == value.shape, (
						    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
						    f""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												snake_case_ :Any						=			value
						elif weight_type == "weight_g":
												snake_case_ :Tuple						=			value
						elif weight_type == "weight_v":
												snake_case_ :Optional[int]						=			value
						elif weight_type == "bias":
												snake_case_ :Tuple						=			value
						else:
												snake_case_ :List[Any]						=			value

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







						'''simple docstring'''


						snake_case_ :Dict						=			[]
						snake_case_ :Union[str, Any]						=			fairseq_model.state_dict()

						snake_case_ :str						=			hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case_ :Dict						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase,					_lowercase,					_lowercase,					_lowercase,					hf_model.config.feat_extract_norm == """group""",					)
																		snake_case_ :Dict						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case_ :List[str]						=			"""unispeech.""" + 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]:
																														snake_case_ :List[str]						=			True
																														if "*" in mapped_key:
																																				snake_case_ :Tuple						=			name.split(_lowercase	)[0].split("""."""	)[-2]
																																				snake_case_ :Tuple						=			mapped_key.replace("""*""",					_lowercase	)
																														if "weight_g" in name:
																																				snake_case_ :Dict						=			"""weight_g"""
																														elif "weight_v" in name:
																																				snake_case_ :Dict						=			"""weight_v"""
																														elif "bias" in name:
																																				snake_case_ :Optional[Any]						=			"""bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				snake_case_ :List[str]						=			"""weight"""
																														else:
																																				snake_case_ :Optional[Any]						=			None
																														set_recursively(_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	)
																								continue
												if not is_used:
																		unused_weights.append(_lowercase	)

						logger.warning(f"""Unused weights: {unused_weights}"""	)
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			full_name.split("""conv_layers."""	)[-1]
						snake_case_ :List[str]						=			name.split("""."""	)
						snake_case_ :Any						=			int(items[0]	)
						snake_case_ :str						=			int(items[1]	)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		snake_case_ :Union[str, Any]						=			value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		snake_case_ :List[str]						=			value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		snake_case_ :int						=			value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		snake_case_ :int						=			value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
						else:
												unused_weights.append(_lowercase	)
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None,					_lowercase=None,					_lowercase=True	):







						'''simple docstring'''


						if config_path is not None:
												snake_case_ :str						=			UniSpeechConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :Tuple						=			UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		snake_case_ :Optional[int]						=			Dictionary.load_from_json(_lowercase	)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		snake_case_ :Optional[int]						=			target_dict.pad_index
																		snake_case_ :Optional[int]						=			target_dict.bos_index
																		snake_case_ :Dict						=			target_dict.eos_index
																		snake_case_ :List[str]						=			len(target_dict.symbols	)
																		snake_case_ :int						=			os.path.join(_lowercase,					"""vocab.json"""	)
																		if not os.path.isdir(_lowercase	):
																								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowercase	)	)
																								return
																		os.makedirs(_lowercase,					exist_ok=_lowercase	)
																		snake_case_ :List[str]						=			target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		snake_case_ :Optional[Any]						=			42
																		snake_case_ :List[Any]						=			43
																		with open(_lowercase,					"""w""",					encoding="""utf-8"""	) as vocab_handle:
																								json.dump(_lowercase,					_lowercase	)
																		snake_case_ :Union[str, Any]						=			WavaVecaPhonemeCTCTokenizer(
																		    _lowercase,					unk_token=target_dict.unk_word,					pad_token=target_dict.pad_word,					bos_token=target_dict.bos_word,					eos_token=target_dict.eos_word,					word_delimiter_token="""|""",					do_lower_case=_lowercase,					)
																		snake_case_ :List[Any]						=			True if config.feat_extract_norm == """layer""" else False
																		snake_case_ :Any						=			WavaVecaFeatureExtractor(
																		    feature_size=1,					sampling_rate=16000,					padding_value=0,					do_normalize=_lowercase,					return_attention_mask=_lowercase,					)
																		snake_case_ :str						=			WavaVecaProcessor(feature_extractor=_lowercase,					tokenizer=_lowercase	)
																		processor.save_pretrained(_lowercase	)

												snake_case_ :Optional[Any]						=			UniSpeechForCTC(_lowercase	)
						else:
												snake_case_ :str						=			UniSpeechForPreTraining(_lowercase	)

						if is_finetuned:
												snake_case_,       snake_case_,       snake_case_ :int						=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path],					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	), """w2v_path""": checkpoint_path}	)
						else:
												snake_case_,       snake_case_,       snake_case_ :Dict						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

						snake_case_ :Any						=			model[0].eval()

						recursively_load_weights(_lowercase,					_lowercase,					_lowercase	)

						hf_unispeech.save_pretrained(_lowercase	)


if __name__ == "__main__":
						__a      =				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"
						)
						__a      =				parser.parse_args()
						convert_unispeech_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)





"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""    ,						"""False"""				)				) is not True    ,						reason="""Skipping test because should only be run when releasing minor transformers version"""    ,						)
@pytest.mark.usefixtures("""sm_env"""				)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]				)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Any						)   ->	str:
											if self.framework == "pytorch":
																	subprocess.run(
																	    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()						,     encoding="""utf-8"""						,     check=snake_case						,     )
											assert hasattr(self						,     """env"""						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						)   ->	List[Any]:
											# configuration for running training on smdistributed Model Parallel
											snake_case_ :Tuple						=			{
											    """enabled""": True,
											    """processes_per_host""": 8,
											}
											snake_case_ :List[Any]						=			{
											    """enabled""": True,
											    """parameters""": {
											        """microbatches""": 4,
											        """placement_strategy""": """spread""",
											        """pipeline""": """interleaved""",
											        """optimize""": """speed""",
											        """partitions""": 4,
											        """ddp""": True,
											    },
											}

											snake_case_ :Tuple						=			{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

											snake_case_ :Any						=			"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
											# creates estimator
											return HuggingFace(
											    entry_point=self.script						,     source_dir=self.env.test_path						,     role=self.env.role						,     image_uri=self.env.image_uri						,     base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""						,     instance_count=snake_case						,     instance_type=self.instance_type						,     debugger_hook_config=snake_case						,     hyperparameters={
											        **self.env.hyperparameters,
											        """model_name_or_path""": self.model_name_or_path,
											        """max_steps""": 500,
											    }						,     metric_definitions=self.env.metric_definitions						,     distribution=snake_case						,     py_version="""py36"""						,     )






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Tuple						)   ->	List[str]:
											TrainingJobAnalytics(snake_case						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)






					@parameterized.expand([(1,)]						)
					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	List[Any]:
											# create estimator
											snake_case_ :List[Any]						=			self.create_estimator(snake_case						)

											# run training
											estimator.fit()

											# result dataframe
											snake_case_ :Any						=			TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

											# extract kpis
											snake_case_ :Tuple						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
											snake_case_ :Dict						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]						)
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											snake_case_ :int						=			(
											    Session().describe_training_job(estimator.latest_training_job.name						).get("""TrainingTimeInSeconds"""						,     999_999						)
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy						)
											assert all(t <= self.results["""eval_loss"""] for t in eval_loss						)

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json"""						,     """w"""						) as outfile:
																	json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}						,     snake_case						)
