


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase__					  =  {
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				for attribute in key.split("."						):
								lowercase__					:   Optional[Any]													=					getattr(__a					,			__a						)

				if weight_type is not None:
								lowercase__					:   Dict													=					getattr(__a					,			__a						).shape
				else:
								lowercase__					:   Optional[int]													=					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":
								lowercase__					:   List[Any]													=					value
				elif weight_type == "weight_g":
								lowercase__					:   Any													=					value
				elif weight_type == "weight_v":
								lowercase__					:   Union[str, Any]													=					value
				elif weight_type == "bias":
								lowercase__					:   Optional[int]													=					value
				else:
								lowercase__					:   List[Any]													=					value

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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

				lowercase__					:   Union[str, Any]													=					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								lowercase__					:   int													=					False
								if "conv_layers" in name:
												load_conv_layer(
												    __a					,			__a					,			__a					,			__a					,			hf_model.config.feat_extract_norm == "group"					,			)
												lowercase__					:   Optional[Any]													=					True
								else:
												for key, mapped_key in MAPPING.items():
																lowercase__					:   Union[str, Any]													=					'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or (key.split("w2v_model."						)[-1] == name.split("."						)[0] and not is_finetuned):
																				lowercase__					:   Any													=					True
																				if "*" in mapped_key:
																								lowercase__					:   Optional[int]													=					name.split(__a						)[0].split("."						)[-2]
																								lowercase__					:   Any													=					mapped_key.replace("*"					,			__a						)
																				if "weight_g" in name:
																								lowercase__					:   List[Any]													=					'''weight_g'''
																				elif "weight_v" in name:
																								lowercase__					:   List[str]													=					'''weight_v'''
																				elif "weight" in name:
																								lowercase__					:   Any													=					'''weight'''
																				elif "bias" in name:
																								lowercase__					:   str													=					'''bias'''
																				else:
																								lowercase__					:   Any													=					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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   int													=					full_name.split("conv_layers."						)[-1]
				lowercase__					:   Any													=					name.split("."						)
				lowercase__					:   List[Any]													=					int(items[0]						)
				lowercase__					:   Optional[int]													=					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."""
												)
												lowercase__					:   Optional[int]													=					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."""
												)
												lowercase__					:   Optional[Any]													=					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."
												)
												lowercase__					:   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."""
												)
												lowercase__					:   Any													=					value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
				else:
								unused_weights.append(__a						)


@torch.no_grad()
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=True						):

				"""simple docstring"""


				if config_path is not None:
								lowercase__					:   Tuple													=					HubertConfig.from_pretrained(__a						)
				else:
								lowercase__					:   Any													=					HubertConfig()

				if is_finetuned:
								if dict_path:
												lowercase__					:   Tuple													=					Dictionary.load(__a						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowercase__					:   Any													=					target_dict.pad_index
												lowercase__					:   Tuple													=					target_dict.bos_index
												lowercase__					:   Optional[int]													=					target_dict.eos_index
												lowercase__					:   Optional[Any]													=					len(target_dict.symbols						)
												lowercase__					:   Tuple													=					os.path.join(__a					,			"vocab.json"						)
												if not os.path.isdir(__a						):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__a						)						)
																return
												os.makedirs(__a					,			exist_ok=__a						)
												with open(__a					,			"w"					,			encoding="utf-8"						) as vocab_handle:
																json.dump(target_dict.indices					,			__a						)
												lowercase__					:   Tuple													=					WavaVecaCTCTokenizer(
												    __a					,			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=__a					,			)
												lowercase__					:   Tuple													=					True if config.feat_extract_norm == '''layer''' else False
												lowercase__					:   List[Any]													=					WavaVecaFeatureExtractor(
												    feature_size=1					,			sampling_rate=16_000					,			padding_value=0					,			do_normalize=__a					,			return_attention_mask=__a					,			)
												lowercase__					:   List[Any]													=					WavaVecaProcessor(feature_extractor=__a					,			tokenizer=__a						)
												processor.save_pretrained(__a						)

								lowercase__					:   Tuple													=					HubertForCTC(__a						)
				else:
								lowercase__					:   Tuple													=					HubertModel(__a						)

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

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

				recursively_load_weights(__a					,			__a					,			__a						)

				hf_wavavec.save_pretrained(__a						)


if __name__ == "__main__":
				lowerCAmelCase__					  =  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__					  =  parser.parse_args()
				convert_hubert_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)









import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, Any]					):
									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=SCREAMING_SNAKE_CASE				,       )
									assert hasattr(self				,       "env"					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=1					):
									# 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}-single"""				,       instance_count=SCREAMING_SNAKE_CASE				,       instance_type=self.instance_type				,       debugger_hook_config=SCREAMING_SNAKE_CASE				,       hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}				,       metric_definitions=self.env.metric_definitions				,       py_version="py36"				,       )







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   Any													=					(
									    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}				,       SCREAMING_SNAKE_CASE					)




