





'''simple docstring'''







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
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_  :							List[str]            =	logging.get_logger(__name__)

snake_case_  :							Optional[Any]            =	"Hello world! cécé herlolip"





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      bool )			->						List[str]:
						UpperCAmelCase_      :   Dict      =	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
						roberta.eval()  # disable dropout
						UpperCAmelCase_      :   Optional[Any]      =	roberta.model.encoder.sentence_encoder
						UpperCAmelCase_      :   Optional[int]      =	RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,      hidden_size=roberta.args.encoder_embed_dim,      num_hidden_layers=roberta.args.encoder_layers,      num_attention_heads=roberta.args.encoder_attention_heads,      intermediate_size=roberta.args.encoder_ffn_embed_dim,      max_position_embeddings=514,      type_vocab_size=1,      layer_norm_eps=1E-5,      )
						if classification_head:
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our BERT config:''',      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Optional[Any]      =	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__ )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						UpperCAmelCase_      :   Any      =	roberta_sent_encoder.embed_tokens.weight
						UpperCAmelCase_      :   Optional[int]      =	roberta_sent_encoder.embed_positions.weight
						UpperCAmelCase_      :   Any      =	torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c RoBERTa doesn't use them.
						UpperCAmelCase_      :   Any      =	roberta_sent_encoder.emb_layer_norm.weight
						UpperCAmelCase_      :   List[Any]      =	roberta_sent_encoder.emb_layer_norm.bias

						for i in range(config.num_hidden_layers ):
												# Encoder: start of layer
												UpperCAmelCase_      :   BertLayer      =	model.roberta.encoder.layer[i]
												UpperCAmelCase_      :   TransformerSentenceEncoderLayer      =	roberta_sent_encoder.layers[i]

												# self attention
												UpperCAmelCase_      :   BertSelfAttention      =	layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size) )
												)

												UpperCAmelCase_      :   List[str]      =	roberta_layer.self_attn.q_proj.weight
												UpperCAmelCase_      :   Union[str, Any]      =	roberta_layer.self_attn.q_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.k_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.k_proj.bias
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.v_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta_layer.self_attn.v_proj.bias

												# self-attention output
												UpperCAmelCase_      :   BertSelfOutput      =	layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.out_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.out_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn_layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.self_attn_layer_norm.bias

												# intermediate
												UpperCAmelCase_      :   BertIntermediate      =	layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.fca.weight
												UpperCAmelCase_      :   List[str]      =	roberta_layer.fca.bias

												# output
												UpperCAmelCase_      :   BertOutput      =	layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												UpperCAmelCase_      :   str      =	roberta_layer.fca.weight
												UpperCAmelCase_      :   str      =	roberta_layer.fca.bias
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.final_layer_norm.weight
												UpperCAmelCase_      :   str      =	roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.classification_heads['''mnli'''].out_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.bias
						else:
												# LM Head
												UpperCAmelCase_      :   Optional[int]      =	roberta.model.encoder.lm_head.dense.weight
												UpperCAmelCase_      :   Any      =	roberta.model.encoder.lm_head.dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.encoder.lm_head.layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.layer_norm.bias
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						UpperCAmelCase_      :   torch.Tensor      =	roberta.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 )  # batch of size 1

						UpperCAmelCase_      :   Optional[int]      =	model(SCREAMING_SNAKE_CASE__ )[0]
						if classification_head:
												UpperCAmelCase_      :   List[Any]      =	roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__ ) )
						else:
												UpperCAmelCase_      :   Tuple      =	roberta.model(SCREAMING_SNAKE_CASE__ )[0]
						print(our_output.shape,      their_output.shape )
						UpperCAmelCase_      :   Tuple      =	torch.max(torch.abs(our_output - their_output ) ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
						UpperCAmelCase_      :   List[Any]      =	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_  :							Any            =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--roberta_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_  :							Dict            =	parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)













"""simple docstring"""




import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase											=     {
    """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
    """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
    """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
    """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
}

UpperCamelCase											=     [
    """Hello, my name is""",
    """Are unicorns real? Unicorns are""",
    """For the first time in several years,""",
    """My name is Julien and I am""",
    """The goal of life is""",
    """Whenever I'm sad, I like to""",
]






def 							lowerCAmelCase			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."       )
  parser.add_argument("model_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the model to try."       )
  parser.add_argument(
      "--tokenizer_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the tokenizer (if different from the model."       )
  parser.add_argument("--is_causal"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="Whether or not the model is causal."       )
  parser.add_argument(
      "--model_revision"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The revision to use for the model checkpoint."       )
  parser.add_argument("--torch_dtype"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The dtype for the model."       )
  parser.add_argument("--disk_offload"  ,			action="store_true"       )

  _a					      =						parser.parse_args()

  # Sanitize args
  if args.model_name in DEFAULT_MODELS:
    _a					      =						DEFAULT_MODELS[args.model_name]
    _a					      =						defaults["model"]
    if args.tokenizer_name is None:
      _a					      =						defaults.get("tokenizer"  ,			args.model_name       )
    if args.is_causal is None:
      _a					      =						defaults["is_causal"]
    if args.model_revision is None:
      _a					      =						defaults.get("model_revision"  ,			"main"       )

  if args.is_causal is None:
    raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."       )
  if args.tokenizer_name is None:
    _a					      =						args.model_name
  if args.model_revision is None:
    _a					      =						"main"

  return args






def 							lowerCAmelCase			(   )							->				str:







  '''simple docstring'''







  transformers.utils.logging.set_verbosity_error()
  _a					      =						parse_args()

  if args.torch_dtype is None:
    _a					      =						AutoConfig.from_pretrained(args.model_name       )
    _a					      =						getattr(UpperCamelCase_  ,			"torch_dtype"  ,			torch.floataa       )
  else:
    _a					      =						getattr(UpperCamelCase_  ,			args.torch_dtype       )
  _a					      =						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
  _a					      =						{
      "torch_dtype": torch_dtype,
      "revision": args.model_revision,
  }
  if args.disk_offload:
    _a					      =						"tmp_offload"
    _a					      =						True

  _a					      =						start_measure()
  _a					      =						model_cls.from_pretrained(args.model_name  ,			device_map="auto"  ,			**UpperCamelCase_       )
  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model loading"       )

  _a					      =						compute_module_sizes(UpperCamelCase_       )
  _a					      =						{v: 0 for v in model.hf_device_map.values()}
  for module, device in model.hf_device_map.items():
    device_size[device] += module_sizes[module]
  _a					      =						"\n".join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]       )
  print(f'''\nTheoretical use:\n{message}'''       )

  _a					      =						AutoTokenizer.from_pretrained(args.tokenizer_name       )

  _a					      =						start_measure()
  _a					      =						[]
  _a					      =						[]
  _a					      =						[]
  for prompt in PROMPTS:
    _a					      =						tokenizer(UpperCamelCase_  ,			return_tensors="pt"       ).to(0       )
    _a					      =						inputs["input_ids"][0].tolist()
    _a					      =						time.time()
    _a					      =						model.generate(inputs["input_ids"]       )
    _a					      =						time.time()
    _a					      =						outputs[0].tolist()
    _a					      =						len(UpperCamelCase_       ) if outputs[: len(UpperCamelCase_       )] != tokens else len(UpperCamelCase_       ) - len(UpperCamelCase_       )
    _a					      =						after_generate - before_generate

    _a					      =						tokenizer.decode(UpperCamelCase_  ,			skip_special_tokens=UpperCamelCase_       )
    texts_outs.append(UpperCamelCase_       )
    generation_times.append(UpperCamelCase_       )
    gen_tokens.append(UpperCamelCase_       )
    print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''       )

  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model generation"       )

  _a					      =						[gen / tok for gen, tok in zip(UpperCamelCase_  ,			UpperCamelCase_       )]
  _a					      =						sum(UpperCamelCase_       ) / len(UpperCamelCase_       )
  print(f'''Average time of generation per token: {avg_gen:.2f}s'''       )
  print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''       )
  _a					      =						sum(generation_times_per_token[1:]       ) / (len(UpperCamelCase_       ) - 1)
  print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''       )


if __name__ == "__main__":
     main()


