


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCAmelCase__      		= logging.get_logger('transformers.models.speecht5')

lowerCAmelCase__      		= {
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
lowerCAmelCase__      		= {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
lowerCAmelCase__      		= {
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
lowerCAmelCase__      		= {
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
lowerCAmelCase__      		= {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowerCAmelCase__      		= {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowerCAmelCase__      		= {
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
lowerCAmelCase__      		= {
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
lowerCAmelCase__      		= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase__      		= {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__      		= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__      		= []
lowerCAmelCase__      		= [
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
lowerCAmelCase__      		= IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowerCAmelCase__      		= IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowerCAmelCase__      		= IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  Tuple						):
       for attribute in key.split("."						):
              _A      :       Tuple				    = getattr(UpperCamelCase__				,							UpperCamelCase__						)

       if weight_type is not None:
              _A      :       str				    = getattr(UpperCamelCase__				,							UpperCamelCase__						).shape
       else:
              _A      :       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":
              _A      :       Optional[int]				    = value
       elif weight_type == "weight_g":
              _A      :       str				    = value
       elif weight_type == "weight_v":
              _A      :       Optional[int]				    = value
       elif weight_type == "bias":
              _A      :       Union[str, Any]				    = value
       elif weight_type == "running_mean":
              _A      :       Any				    = value
       elif weight_type == "running_var":
              _A      :       List[str]				    = value
       elif weight_type == "num_batches_tracked":
              _A      :       Any				    = value
       else:
              _A      :       List[str]				    = value

       logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  List[Any]						):
       for key in ignore_keys:
              if key.endswith(".*"						):
                     if name.startswith(key[:-1]						):
                            return True
              elif ".*." in key:
                     _A    ,			_A      :       Optional[int]				    = key.split(".*."						)
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       List[str]				    = []

       if task == "s2t":
              _A      :       List[str]				    = hf_model.speechta.encoder.prenet.feature_encoder
              _A      :       int				    = MAPPING_S2T
              _A      :       List[Any]				    = IGNORE_KEYS_S2T
       elif task == "t2s":
              _A      :       Tuple				    = None
              _A      :       Optional[Any]				    = MAPPING_T2S
              _A      :       Any				    = IGNORE_KEYS_T2S
       elif task == "s2s":
              _A      :       int				    = hf_model.speechta.encoder.prenet.feature_encoder
              _A      :       Tuple				    = MAPPING_S2S
              _A      :       str				    = IGNORE_KEYS_S2S
       else:
              raise ValueError(f"Unsupported task: {task}"						)

       for name, value in fairseq_dict.items():
              if should_ignore(UpperCamelCase__				,							UpperCamelCase__						):
                     logger.info(f"{name} was ignored"						)
                     continue

              _A      :       List[str]				    = False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							hf_model.config.feat_extract_norm == "group"				,							)
                     _A      :       Union[str, Any]				    = True
              else:
                     for key, mapped_key in MAPPING.items():
                            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                            if "*" in key:
                                   _A    ,			_A      :       str				    = key.split(".*."						)
                                   if prefix in name and suffix in name:
                                          _A      :       Union[str, Any]				    = suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   _A      :       List[Any]				    = True
                                   if "*" in mapped_key:
                                          _A      :       Optional[int]				    = name.split(UpperCamelCase__						)[0].split("."						)[-2]
                                          _A      :       Dict				    = mapped_key.replace("*"				,							UpperCamelCase__						)
                                   if "weight_g" in name:
                                          _A      :       List[Any]				    = "weight_g"
                                   elif "weight_v" in name:
                                          _A      :       str				    = "weight_v"
                                   elif "bias" in name:
                                          _A      :       List[Any]				    = "bias"
                                   elif "weight" in name:
                                          _A      :       Dict				    = "weight"
                                   elif "running_mean" in name:
                                          _A      :       Any				    = "running_mean"
                                   elif "running_var" in name:
                                          _A      :       Optional[int]				    = "running_var"
                                   elif "num_batches_tracked" in name:
                                          _A      :       Optional[int]				    = "num_batches_tracked"
                                   else:
                                          _A      :       Optional[int]				    = 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 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  str				,							UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  str						):
       _A      :       List[str]				    = full_name.split("conv_layers."						)[-1]
       _A      :       int				    = name.split("."						)
       _A      :       Any				    = int(items[0]						)
       _A      :       Tuple				    = 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."						)
                     _A      :       List[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."						)
                     _A      :       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:
                     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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
                     _A      :       int				    = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
                     _A      :       Tuple				    = value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
       else:
              unused_weights.append(UpperCamelCase__						)
@torch.no_grad()
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  Union[str, Any]=None				,							UpperCamelCase__     :  Optional[int]=None				,							UpperCamelCase__     :  str=None				,							):
       if config_path is not None:
              _A      :       List[Any]				    = SpeechTaConfig.from_pretrained(UpperCamelCase__						)
       else:
              _A      :       Optional[int]				    = SpeechTaConfig()

       if task == "s2t":
              _A      :       Tuple				    = config.max_text_positions
              _A      :       Optional[Any]				    = SpeechTaForSpeechToText(UpperCamelCase__						)
       elif task == "t2s":
              _A      :       Optional[Any]				    = 1876
              _A      :       List[Any]				    = 600
              _A      :       Optional[Any]				    = config.max_speech_positions
              _A      :       List[Any]				    = SpeechTaForTextToSpeech(UpperCamelCase__						)
       elif task == "s2s":
              _A      :       Optional[int]				    = 1876
              _A      :       str				    = config.max_speech_positions
              _A      :       Optional[int]				    = SpeechTaForSpeechToSpeech(UpperCamelCase__						)
       else:
              raise ValueError(f"Unknown task name: {task}"						)

       if vocab_path:
              _A      :       Dict				    = SpeechTaTokenizer(UpperCamelCase__				,							model_max_length=config.max_text_positions						)

              # Mask token behaves like a normal word, i.e. include the space before it
              _A      :       Dict				    = AddedToken("<mask>"				,							lstrip=UpperCamelCase__				,							rstrip=UpperCamelCase__						)
              _A      :       Dict				    = mask_token
              tokenizer.add_special_tokens({"mask_token": mask_token}						)
              tokenizer.add_tokens(["<ctc_blank>"]						)

       _A      :       int				    = SpeechTaFeatureExtractor()
       _A      :       List[str]				    = SpeechTaProcessor(tokenizer=UpperCamelCase__				,							feature_extractor=UpperCamelCase__						)
       processor.save_pretrained(UpperCamelCase__						)

       _A      :       Union[str, Any]				    = torch.load(UpperCamelCase__						)
       recursively_load_weights(fairseq_checkpoint["model"]				,							UpperCamelCase__				,							UpperCamelCase__						)

       model.save_pretrained(UpperCamelCase__						)

       if repo_id:
              print("Pushing to the hub..."						)
              processor.push_to_hub(UpperCamelCase__						)
              model.push_to_hub(UpperCamelCase__						)


if __name__ == "__main__":
    lowerCAmelCase__      		= argparse.ArgumentParser()
    parser.add_argument(
        '--task',
        default='s2t',
        type=str,
        help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
    )
    parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )

    lowerCAmelCase__      		= parser.parse_args()
    convert_speechta_checkpoint(
        args.task,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.vocab_path,
        args.push_to_hub,
    )




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            for model_result in results.values():
                   for batch_size, sequence_length in zip(model_result["bs"] ,						model_result["ss"]):
                          _A      :       Optional[int]				    = model_result["result"][batch_size][sequence_length]
                          self.assertIsNotNone(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Optional[int]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Optional[Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = "sgugger/tiny-distilbert-classification"
            _A      :       str				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						only_pretrain_model=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Tuple				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						torchscript=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       List[str]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Cant do half precision")
     def  _lowerCamelCase (						self)			->				int:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						fpaa=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Any				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Union[str, Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Any				    = AutoConfig.from_pretrained(__lowerCamelCase)
            # set architectures equal to `None`
            _A      :       Dict				    = None
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Can't do half precision")
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						fpaa=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            _A      :       Union[str, Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Tuple				    = "sshleifer/tinier_bart"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[int]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Optional[int]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Dict				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       int				    = "sshleifer/tinier_bart"
            _A      :       str				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						save_to_csv=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						inference_time_csv_file=os.path.join(__lowerCamelCase ,						"inf_time.csv") ,						train_memory_csv_file=os.path.join(__lowerCamelCase ,						"train_mem.csv") ,						inference_memory_csv_file=os.path.join(__lowerCamelCase ,						"inf_mem.csv") ,						train_time_csv_file=os.path.join(__lowerCamelCase ,						"train_time.csv") ,						env_info_csv_file=os.path.join(__lowerCamelCase ,						"env.csv") ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase)
                   benchmark.run()
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"env.csv")).exists())


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Dict				    = "sshleifer/tiny-gpt2"

            def _check_summary_is_not_empty(__lowerCamelCase):
                   self.assertTrue(hasattr(__lowerCamelCase ,						"sequential"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"cumulative"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"current"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"total"))

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Union[str, Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						log_filename=os.path.join(__lowerCamelCase ,						"log.txt") ,						log_print=__lowerCamelCase ,						trace_memory_line_by_line=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Optional[int]				    = PyTorchBenchmark(__lowerCamelCase)
                   _A      :       Dict				    = benchmark.run()
                   _check_summary_is_not_empty(result.inference_summary)
                   _check_summary_is_not_empty(result.train_summary)
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"log.txt")).exists())
