
"""simple docstring"""




import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
	raise Exception('requires fairseq >= 0.9.0')


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

snake_case			       =		'Hello world! cécé herlolip'

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     os.path.split(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     DataaVecTextModel.from_pretrained(
					    SCREAMING_SNAKE_CASE_,					checkpoint_file=SCREAMING_SNAKE_CASE_      )
					dataavec.eval()  # disable dropout
					SCREAMING_SNAKE_CASE           =     dataavec.models[0]
					SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.sentence_encoder
					SCREAMING_SNAKE_CASE           =     DataaVecTextConfig(
					    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings,					hidden_size=dataavec_model.args.encoder_embed_dim,					num_hidden_layers=dataavec_model.args.encoder_layers,					num_attention_heads=dataavec_model.args.encoder_attention_heads,					intermediate_size=dataavec_model.args.encoder_ffn_embed_dim,					max_position_embeddings=5_1_4,					type_vocab_size=1,					layer_norm_eps=1E-5,					)
					if classification_head:
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
					print('Our BERT config:',					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE_      ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE_      )
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					SCREAMING_SNAKE_CASE           =     dataavec_sent_encoder.embed_tokens.weight
					SCREAMING_SNAKE_CASE           =     dataavec_sent_encoder.embed_positions.weight
					SCREAMING_SNAKE_CASE           =     torch.zeros_like(
					    model.dataavec_text.embeddings.token_type_embeddings.weight      )  # just zero them out b/c data2vec doesn't use them.
					SCREAMING_SNAKE_CASE           =     dataavec_sent_encoder.layernorm_embedding.weight
					SCREAMING_SNAKE_CASE           =     dataavec_sent_encoder.layernorm_embedding.bias

					for i in range(config.num_hidden_layers      ):
										# Encoder: start of layer
										SCREAMING_SNAKE_CASE           =     model.dataavec_text.encoder.layer[i]
										SCREAMING_SNAKE_CASE           =     dataavec_sent_encoder.layers[i]

										# self attention
										SCREAMING_SNAKE_CASE           =     layer.attention.self
										assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)      ), (
										    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
										    f''' {torch.Size((config.hidden_size, config.hidden_size)      )}'''
										)
										assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)      ), (
										    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
										    f''' {torch.Size((config.hidden_size, config.hidden_size)      )}'''
										)
										assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)      ), (
										    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
										    f''' {torch.Size((config.hidden_size, config.hidden_size)      )}'''
										)

										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.q_proj.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.q_proj.bias
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.k_proj.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.k_proj.bias
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.v_proj.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.v_proj.bias

										# self-attention output
										SCREAMING_SNAKE_CASE           =     layer.attention.output
										assert (
										    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
										), f'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.out_proj.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn.out_proj.bias
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn_layer_norm.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										SCREAMING_SNAKE_CASE           =     layer.intermediate
										assert (
										    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
										), f'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										SCREAMING_SNAKE_CASE           =     dataavec_layer.fca.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.fca.bias

										# output
										SCREAMING_SNAKE_CASE           =     layer.output
										assert (
										    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
										), f'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										SCREAMING_SNAKE_CASE           =     dataavec_layer.fca.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.fca.bias
										SCREAMING_SNAKE_CASE           =     dataavec_layer.final_layer_norm.weight
										SCREAMING_SNAKE_CASE           =     dataavec_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'].dense.weight
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'].dense.bias
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'].out_proj.weight
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'].out_proj.bias
					else:
										# LM Head
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.dense.weight
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.dense.bias
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.layer_norm.weight
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.layer_norm.bias
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.weight
										SCREAMING_SNAKE_CASE           =     dataavec_model.encoder.lm_head.bias

					# Let's check that we get the same results.
					SCREAMING_SNAKE_CASE           =     dataavec.encode(SCREAMING_SNAKE_CASE_      ).unsqueeze(0      )  # batch of size 1

					SCREAMING_SNAKE_CASE           =     model(SCREAMING_SNAKE_CASE_      )[0]
					if classification_head:
										SCREAMING_SNAKE_CASE           =     dataavec.model.classification_heads['mnli'](dataavec.extract_features(SCREAMING_SNAKE_CASE_      )      )
					else:
										SCREAMING_SNAKE_CASE           =     dataavec_model(SCREAMING_SNAKE_CASE_      )[0]
					print(our_output.shape,					their_output.shape      )
					SCREAMING_SNAKE_CASE           =     torch.max(torch.abs(our_output - their_output      )      ).item()
					print(f'''max_absolute_diff = {max_absolute_diff}'''      )  # ~ 1e-7
					SCREAMING_SNAKE_CASE           =     torch.allclose(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					atol=1E-3      )
					print('Do both models output the same tensors?',					'🔥' if success else '💩'      )
					if not success:
										raise Exception('Something went wRoNg'      )

					pathlib.Path(SCREAMING_SNAKE_CASE_      ).mkdir(parents=SCREAMING_SNAKE_CASE_,					exist_ok=SCREAMING_SNAKE_CASE_      )
					print(f'''Saving model to {pytorch_dump_folder_path}'''      )
					model.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	snake_case			       =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
	)
	snake_case			       =		parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)


"""simple docstring"""




import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
	import torch

	from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
	import pyanvml.pyanvml as nvml


snake_case			       =		logging.get_logger(__name__)




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				PyTorchBenchmarkArguments
				UpperCAmelCase_    :				PretrainedConfig
				UpperCAmelCase_    :				str						  =    "PyTorch"


				@property
				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									return torch.__version__


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									if self.args.torchscript:
														SCREAMING_SNAKE_CASE           =     True

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_MAPPING[config.__class__](lowercase__      )

									model.eval()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (batch_size, sequence_length)		,     dtype=torch.long		,     device=self.args.device      )

									if self.args.fpaa:
														logger.info('Running training in Mixed Precision...'      )
														if not self.args.is_gpu:
																			raise ValueError('Mixed precision is possible only for GPU.'      )
														# amp seems to have memory leaks so that memory usage
														# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
														model.half()

									if self.args.torchscript:
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     torch.jit.trace(lowercase__		,     lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									def encoder_decoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__		,     decoder_input_ids=lowercase__      )
														return outputs

									def encoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__      )
														return outputs

									SCREAMING_SNAKE_CASE           =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__      )

									if self.args.torchscript:
														raise NotImplementedError('Training for torchscript is currently not implemented'      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									model.train()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (batch_size, sequence_length)		,     dtype=torch.long		,     device=self.args.device      )

									if self.args.fpaa:
														logger.info('Running training in Mixed Precision...'      )
														if not self.args.is_gpu:
																			raise ValueError('Mixed precision is possible only for GPU.'      )

														# amp seems to have memory leaks so that memory usage
														# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
														model.half()

									def compute_loss_and_backprob_encoder():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									def compute_loss_and_backprob_encoder_decoder():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     decoder_input_ids=lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									SCREAMING_SNAKE_CASE           =     (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train


				def 							A (    self		,     lowercase__      )   ->   float:
									"""simple docstring"""







									try:
														if self.args.is_tpu or self.args.torchscript:
																			# run additional 10 times to stabilize compilation for tpu and torchscript
																			logger.info('Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'      )
																			timeit.repeat(
																			    lowercase__		,     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
														SCREAMING_SNAKE_CASE           =     timeit.repeat(
														    lowercase__		,     repeat=self.args.repeat		,     number=10		,     )

														if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																			import torch_xla.debug.metrics as met

																			self.print_fn(met.metrics_report()      )

														return min(lowercase__      ) / 10.0
									except RuntimeError as e:
														self.print_fn(f'''Doesn\'t fit on GPU. {e}'''      )
														return "N/A"




				def 							A (    self		,     lowercase__      )   ->   [Memory, MemorySummary]:
									"""simple docstring"""







									try:
														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     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'
																			    ' `--no-memory` or `args.memory=False`'      )
														elif self.args.is_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.'      )
																								SCREAMING_SNAKE_CASE           =     '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()
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetMemoryInfo(lowercase__      )
																								SCREAMING_SNAKE_CASE           =     meminfo.used
																								SCREAMING_SNAKE_CASE           =     Memory(lowercase__      )
																								# shutdown nvml
																								nvml.nvmlShutdown()
														else:
																			# cpu
																			SCREAMING_SNAKE_CASE           =     measure_peak_memory_cpu(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     Memory(lowercase__      ) if isinstance(lowercase__		,     lowercase__      ) else memory_bytes

														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     stop_memory_tracing(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     None

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