




"""simple docstring"""







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()
a      =							logging.get_logger('transformers.models.speecht5')

a      =							{
    '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',
}
a      =							{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
a      =							{
    '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',
}
a      =							{
    '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',
}
a      =							{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
a      =							{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
a      =							{
    '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',
}
a      =							{
    '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',
}
a      =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a      =							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a      =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a      =							[]
a      =							[
    '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',
]
a      =							IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
a      =							IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
a      =							IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple      ,       snake_case__   : str )     ->  Union[str, Any]:


				'''simple docstring'''






				for attribute in key.split(""".""" ):
								lowerCAmelCase  =				getattr(snake_case__      ,       snake_case__ )

				if weight_type is not None:
								lowerCAmelCase  =				getattr(snake_case__      ,       snake_case__ ).shape
				else:
								lowerCAmelCase  =				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":
								lowerCAmelCase  =				value
				elif weight_type == "weight_g":
								lowerCAmelCase  =				value
				elif weight_type == "weight_v":
								lowerCAmelCase  =				value
				elif weight_type == "bias":
								lowerCAmelCase  =				value
				elif weight_type == "running_mean":
								lowerCAmelCase  =				value
				elif weight_type == "running_var":
								lowerCAmelCase  =				value
				elif weight_type == "num_batches_tracked":
								lowerCAmelCase  =				value
				else:
								lowerCAmelCase  =				value

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





def 							lowercase					(snake_case__   : int      ,       snake_case__   : List[str] )     ->  Optional[Any]:


				'''simple docstring'''






				for key in ignore_keys:
								if key.endswith(""".*""" ):
												if name.startswith(key[:-1] ):
																return True
								elif ".*." in key:
												lowerCAmelCase ,						lowerCAmelCase  =				key.split(""".*.""" )
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple      ,       snake_case__   : Dict )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				[]

				if task == "s2t":
								lowerCAmelCase  =				hf_model.speechta.encoder.prenet.feature_encoder
								lowerCAmelCase  =				MAPPING_S2T
								lowerCAmelCase  =				IGNORE_KEYS_S2T
				elif task == "t2s":
								lowerCAmelCase  =				None
								lowerCAmelCase  =				MAPPING_T2S
								lowerCAmelCase  =				IGNORE_KEYS_T2S
				elif task == "s2s":
								lowerCAmelCase  =				hf_model.speechta.encoder.prenet.feature_encoder
								lowerCAmelCase  =				MAPPING_S2S
								lowerCAmelCase  =				IGNORE_KEYS_S2S
				else:
								raise ValueError(f'''Unsupported task: {task}''' )

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

								lowerCAmelCase  =				False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       hf_model.config.feat_extract_norm == """group"""      ,       )
												lowerCAmelCase  =				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:
																				lowerCAmelCase ,						lowerCAmelCase  =				key.split(""".*.""" )
																				if prefix in name and suffix in name:
																								lowerCAmelCase  =				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				lowerCAmelCase  =				True
																				if "*" in mapped_key:
																								lowerCAmelCase  =				name.split(snake_case__ )[0].split(""".""" )[-2]
																								lowerCAmelCase  =				mapped_key.replace("""*"""      ,       snake_case__ )
																				if "weight_g" in name:
																								lowerCAmelCase  =				"""weight_g"""
																				elif "weight_v" in name:
																								lowerCAmelCase  =				"""weight_v"""
																				elif "bias" in name:
																								lowerCAmelCase  =				"""bias"""
																				elif "weight" in name:
																								lowerCAmelCase  =				"""weight"""
																				elif "running_mean" in name:
																								lowerCAmelCase  =				"""running_mean"""
																				elif "running_var" in name:
																								lowerCAmelCase  =				"""running_var"""
																				elif "num_batches_tracked" in name:
																								lowerCAmelCase  =				"""num_batches_tracked"""
																				else:
																								lowerCAmelCase  =				None
																				set_recursively(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
																continue
								if not is_used:
												unused_weights.append(snake_case__ )

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





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : List[Any]      ,       snake_case__   : str      ,       snake_case__   : List[str]      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				full_name.split("""conv_layers.""" )[-1]
				lowerCAmelCase  =				name.split(""".""" )
				lowerCAmelCase  =				int(items[0] )
				lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
				else:
								unused_weights.append(snake_case__ )





@torch.no_grad()
def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Dict      ,       snake_case__   : Optional[Any]=None      ,       snake_case__   : List[Any]=None      ,       snake_case__   : Tuple=None      ,       )     ->  List[str]:


				'''simple docstring'''






				if config_path is not None:
								lowerCAmelCase  =				SpeechTaConfig.from_pretrained(snake_case__ )
				else:
								lowerCAmelCase  =				SpeechTaConfig()

				if task == "s2t":
								lowerCAmelCase  =				config.max_text_positions
								lowerCAmelCase  =				SpeechTaForSpeechToText(snake_case__ )
				elif task == "t2s":
								lowerCAmelCase  =				1_876
								lowerCAmelCase  =				600
								lowerCAmelCase  =				config.max_speech_positions
								lowerCAmelCase  =				SpeechTaForTextToSpeech(snake_case__ )
				elif task == "s2s":
								lowerCAmelCase  =				1_876
								lowerCAmelCase  =				config.max_speech_positions
								lowerCAmelCase  =				SpeechTaForSpeechToSpeech(snake_case__ )
				else:
								raise ValueError(f'''Unknown task name: {task}''' )

				if vocab_path:
								lowerCAmelCase  =				SpeechTaTokenizer(snake_case__      ,       model_max_length=config.max_text_positions )

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

				lowerCAmelCase  =				SpeechTaFeatureExtractor()
				lowerCAmelCase  =				SpeechTaProcessor(tokenizer=snake_case__      ,       feature_extractor=snake_case__ )
				processor.save_pretrained(snake_case__ )

				lowerCAmelCase  =				torch.load(snake_case__ )
				recursively_load_weights(fairseq_checkpoint["""model"""]      ,       snake_case__      ,       snake_case__ )

				model.save_pretrained(snake_case__ )

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


if __name__ == "__main__":
			a      =							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.'
			)

			a      =							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,
			)











"""simple docstring"""







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : bool      ,       snake_case__   : bool )     ->  Tuple:


				'''simple docstring'''






				def run_func(snake_case__   : Any ):
								@wraps(snake_case__ )
								def run_in_eager_mode(*snake_case__   : Optional[Any]      ,       **snake_case__   : int ):
												return func(*snake_case__      ,       **snake_case__ )

								@wraps(snake_case__ )
								@tf.function(experimental_compile=snake_case__ )
								def run_in_graph_mode(*snake_case__   : int      ,       **snake_case__   : Tuple ):
												return func(*snake_case__      ,       **snake_case__ )

								if do_eager_mode is True:
												if use_xla is not False:
																raise ValueError(
																    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
												return run_in_eager_mode
								else:
												return run_in_graph_mode

				return run_func





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  ["tf.Tensor"]:


				'''simple docstring'''






				lowerCAmelCase  =				random.Random()
				lowerCAmelCase  =				[rng.randint(0      ,       vocab_size - 1 ) for i in range(batch_size * sequence_length )]
				return tf.constant(snake_case__      ,       shape=(batch_size, sequence_length)      ,       dtype=tf.intaa )






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						42
						_a    						=						42
						_a    						=						"TensorFlow"


						@property
						def   __lowercase (							self				:			Optional[int]					):
										return tf.__version__


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_inference					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_train					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_inference					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)

										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_train					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_forward():
														return model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						training=lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_forward():
														return model(lowerCAmelCase						,						training=lowerCAmelCase					)

										lowerCAmelCase  =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.eager_mode is not False:
														raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""					)

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										lowerCAmelCase  =				encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]					):
										with self.args.strategy.scope():
														try:
																		if self.args.is_tpu or self.args.use_xla:
																						# run additional 10 times to stabilize compilation for tpu
																						logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""					)
																						timeit.repeat(lowerCAmelCase						,						repeat=1						,						number=5					)

																		# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																		lowerCAmelCase  =				timeit.repeat(
																		    lowerCAmelCase						,						repeat=self.args.repeat						,						number=10						,						)

																		return min(lowerCAmelCase					) / 10.0
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)




						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Callable[[], None]					):
										logger.info(
										    """Note that TensorFlow allocates more memory than """
										    """it might need to speed up computation. """
										    """The memory reported here corresponds to the memory """
										    """reported by `nvidia-smi`, which can vary depending """
										    """on total available memory on the GPU that is used."""					)
										with self.args.strategy.scope():
														try:
																		if self.args.trace_memory_line_by_line:
																						if not self.args.eager_mode:
																										raise ValueError(
																										    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																										    """ consumption line by line."""					)
																						lowerCAmelCase  =				start_memory_tracing("""transformers"""					)

																		if self.args.is_tpu:
																						# tpu
																						raise NotImplementedError(
																						    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																						    """ with `args.memory=False`"""					)
																		elif self.args.is_gpu:
																						# gpu
																						if not is_pyanvml_available():
																										logger.warning(
																										    """py3nvml not installed, we won't log GPU memory usage. """
																										    """Install py3nvml (pip install py3nvml) to log information about GPU."""					)
																										lowerCAmelCase  =				"""N/A"""
																						else:
																										logger.info(
																										    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																										    """ running on the same GPU."""					)
																										# init nvml
																										nvml.nvmlInit()
																										func()
																										lowerCAmelCase  =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
																										lowerCAmelCase  =				nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase					)
																										lowerCAmelCase  =				meminfo.used
																										lowerCAmelCase  =				Memory(lowerCAmelCase					)
																										# shutdown nvml
																										nvml.nvmlShutdown()
																		else:
																						# cpu
																						if self.args.trace_memory_line_by_line:
																										logger.info(
																										    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																										    """ TensorFlow."""					)
																										lowerCAmelCase  =				None
																						else:
																										lowerCAmelCase  =				measure_peak_memory_cpu(lowerCAmelCase					)
																										lowerCAmelCase  =				Memory(lowerCAmelCase					) if isinstance(lowerCAmelCase						,						lowerCAmelCase					) else memory_bytes
																		if self.args.trace_memory_line_by_line:
																						lowerCAmelCase  =				stop_memory_tracing(lowerCAmelCase					)
																						if memory is None:
																										lowerCAmelCase  =				summary.total
																		else:
																						lowerCAmelCase  =				None

																		return memory, summary
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)
																		return "N/A", None





