"""simple docstring"""





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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      Dict:
             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=a	,    )
             assert hasattr(self	,    'env'							)


      def 	_UpperCAmelCase (     self	,    a=1							) ->      Any:
             # 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=a	,    instance_type=self.instance_type	,    debugger_hook_config=a	,    hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}	,    metric_definitions=self.env.metric_definitions	,    py_version='py36'	,    )


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             TrainingJobAnalytics(a							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)


      def 	_UpperCAmelCase (     self							) ->      int:
             # create estimator
             lowercase__    :	Tuple     					=			self.create_estimator()

             # run training
             estimator.fit()

             # result dataframe
             lowercase__    :	int     					=			TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

             # extract kpis
             lowercase__    :	Any     					=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']							)
             lowercase__    :	List[Any]     					=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']							)
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             lowercase__    :	Optional[Any]     					=			(
                 Session().describe_training_job(estimator.latest_training_job.name							).get('TrainingTimeInSeconds'	,    9_9_9_9_9_9							)
             )

             # 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}	,    a							)


"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

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

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__    :	Optional[int]     					=			False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__    :	Optional[Any]     					=			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__    :	List[Any]     					=			'unispeech_sat.' + 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]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'  )[:-1]  ) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__    :	int     					=			True
                                   if "*" in mapped_key:
                                          lowercase__    :	Optional[int]     					=			name.split(_lowerCAmelCase  )[0].split('.'  )[-2]
                                          lowercase__    :	List[str]     					=			mapped_key.replace('*'     ,				_lowerCAmelCase  )
                                   if "weight_g" in name:
                                          lowercase__    :	List[Any]     					=			'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__    :	int     					=			'weight_v'
                                   elif "bias" in name:
                                          lowercase__    :	Dict     					=			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__    :	Union[str, Any]     					=			'weight'
                                   else:
                                          lowercase__    :	int     					=			None
                                   set_recursively(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
                            continue
              if not is_used:
                     unused_weights.append(_lowerCAmelCase  )

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





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	int     					=			full_name.split('conv_layers.'  )[-1]
       lowercase__    :	int     					=			name.split('.'  )
       lowercase__    :	int     					=			int(items[0]  )
       lowercase__    :	Dict     					=			int(items[1]  )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
                     lowercase__    :	Union[str, Any]     					=			value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
                     lowercase__    :	Optional[int]     					=			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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""  )
                     lowercase__    :	List[Any]     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""  )
                     lowercase__    :	int     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
       else:
              unused_weights.append(_lowerCAmelCase  )





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

       lowercase__					,   lowercase__					,   lowercase__    :	int     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
       lowercase__    :	Union[str, Any]     					=			model[0].eval()

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					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"
       )
       _UpperCamelCase     :		str											=					parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

