

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser(
      description=(
          'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
          ' Distillation'
      )
  )
  parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
  parser.add_argument('--model_name', default='roberta-large', type=str)
  parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
  parser.add_argument('--vocab_transform', action='store_true')
  lowercase_   							=  parser.parse_args()

  if args.model_type == "roberta":
    lowercase_   							=  RobertaForMaskedLM.from_pretrained(args.model_name)
    lowercase_   							=  'roberta'
  elif args.model_type == "gpt2":
    lowercase_   							=  GPTaLMHeadModel.from_pretrained(args.model_name)
    lowercase_   							=  'transformer'

  lowercase_   							=  model.state_dict()
  lowercase_   							=  {}

  # Embeddings #
  if args.model_type == "gpt2":
    for param_name in ["wte.weight", "wpe.weight"]:
      lowercase_   							=  state_dict[f"{prefix}.{param_name}"]
  else:
    for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
      lowercase_   							=  f"{prefix}.embeddings.{w}.weight"
      lowercase_   							=  state_dict[param_name]
    for w in ["weight", "bias"]:
      lowercase_   							=  f"{prefix}.embeddings.LayerNorm.{w}"
      lowercase_   							=  state_dict[param_name]

    # Transformer Blocks #
  lowercase_   							=  0
  for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
    if args.model_type == "gpt2":
      for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
        for w in ["weight", "bias"]:
          lowercase_   							=  state_dict[
              f"{prefix}.h.{teacher_idx}.{layer}.{w}"
          ]
      lowercase_   							=  state_dict[f"{prefix}.h.{teacher_idx}.attn.bias"]
    else:
      for layer in [
          "attention.self.query",
          "attention.self.key",
          "attention.self.value",
          "attention.output.dense",
          "attention.output.LayerNorm",
          "intermediate.dense",
          "output.dense",
          "output.LayerNorm",
      ]:
        for w in ["weight", "bias"]:
          lowercase_   							=  state_dict[
              f"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
          ]
    std_idx += 1

  # Language Modeling Head ###s
  if args.model_type == "roberta":
    for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
      lowercase_   							=  state_dict[f"{layer}"]
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        lowercase_   							=  state_dict[f"lm_head.dense.{w}"]
        lowercase_   							=  state_dict[f"lm_head.layer_norm.{w}"]
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      lowercase_   							=  state_dict[f"{prefix}.ln_f.{w}"]
    lowercase_   							=  state_dict['lm_head.weight']

  print(f"N layers selected for distillation: {std_idx}")
  print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")

  print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
  torch.save(compressed_sd, args.dump_checkpoint)





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_   							=  '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class 					__lowerCAmelCase	(   unittest.TestCase     ,     SCREAMING_SNAKE_CASE				):




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=load_tool('text-question-answering'   )
        self.tool.setup()
        _lowercase      			=load_tool('text-question-answering'				,  remote=lowerCAmelCase   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.tool(lowerCAmelCase				,  'What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.remote_tool(lowerCAmelCase				,  'What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.tool(text=lowerCAmelCase				,  question='What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.remote_tool(text=lowerCAmelCase				,  question='What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )


