





'''simple docstring'''

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCAmelCase							:     List[Any]		     =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCAmelCase							:     int		     ="""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     A__    =      tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir						,	"models/bert/"							)							)
     A__    =      self.transformer_dir
     shutil.copy(
         os.path.join(lowercase_						,	"src/transformers/models/bert/modeling_bert.py"							)						,	os.path.join(self.transformer_dir						,	"models/bert/modeling_bert.py"							)						,	)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Optional[Any]:
     A__    =      "src/transformers"
     shutil.rmtree(self.transformer_dir							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:int						,	lowercase_				:List[str]=None							)->							Optional[Any]:
     A__    =      comment + F"\nclass {class_name}(nn.Module):\n" + class_code
     if overwrite_result is not None:
         A__    =      comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
     A__    =      black.Mode(target_versions={black.TargetVersion.PYaa}						,	line_length=1_19							)
     A__    =      black.format_str(lowercase_						,	mode=lowercase_							)
     A__    =      os.path.join(self.transformer_dir						,	"new_code.py"							)
     with open(lowercase_						,	"w"						,	newline="\n"							) as f:
         f.write(lowercase_							)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(lowercase_							)							) == 0							)
     else:
         check_copies.is_copy_consistent(f.name						,	overwrite=lowercase_							)
         with open(lowercase_						,	"r"							) as f:
             self.assertTrue(f.read()						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Optional[int]:
     A__    =      check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							str:
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,	"BertLMPredictionHead"						,	REFERENCE_CODE + "\n"						,	)

     # With no empty line at the end
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,	"BertLMPredictionHead"						,	lowercase_						,	)

     # Copy consistency with rename
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,	"TestModelLMPredictionHead"						,	re.sub("Bert"						,	"TestModel"						,	lowercase_							)						,	)

     # Copy consistency with a really long name
     A__    =      "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
     self.check_copy_consistency(
         F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"						,	F"{long_class_name}LMPredictionHead"						,	re.sub("Bert"						,	lowercase_						,	lowercase_							)						,	)

     # Copy consistency with overwrite
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,	"TestModelLMPredictionHead"						,	lowercase_						,	overwrite_result=re.sub("Bert"						,	"TestModel"						,	lowercase_							)						,	)



 def 					UpperCAmelCase_							(    self				:Any							)->							Tuple:
     A__    =      check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
         " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
         " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
         " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
         " Luong, Quoc V. Le, Christopher D. Manning."
     )
     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
         " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
         " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
         " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
         " Christopher D. Manning 发布。\n"
     )

     A__,     A__    =      check_copies.convert_to_localized_md(
         lowercase_						,	lowercase_						,	localized_readme["format_model_list"]							)

     self.assertFalse(lowercase_							)
     self.assertEqual(lowercase_						,	lowercase_							)

     A__,     A__    =      check_copies.convert_to_localized_md(
         lowercase_						,	lowercase_						,	localized_readme["format_model_list"]							)

     # Check whether the number of models is equal to README.md after conversion.
     self.assertTrue(lowercase_							)

     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
     )
     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
         " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     A__    =      (
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )

     A__,     A__    =      check_copies.convert_to_localized_md(
         lowercase_						,	lowercase_						,	localized_readme["format_model_list"]							)

     # Check if the model link is synchronized.
     self.assertEqual(lowercase_						,	lowercase_							)






'''simple docstring'''

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

    # pop unnecessary weights
    A__    =      [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase	)

    A__    =      {
        "decoder.project_in_dim.weight": "decoder.project_in.weight",
        "decoder.project_out_dim.weight": "decoder.project_out.weight",
        "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
        "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            A__    =      sd.pop(_lowerCamelCase	)

    A__    =      list(sd.keys()	)
    for key in keys:
        if ".qkv_proj." in key:
            A__    =      sd[key]
            # We split QKV in separate Q,K,V

            A__    =      key.replace(".qkv_proj."						,	".q_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".k_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".v_proj."	)

            A__    =      value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            A__,     A__,     A__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

            A__    =      q
            A__    =      k
            A__    =      v
            del sd[key]

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      OPTModel(_lowerCamelCase	).half().eval()
    model.load_state_dict(_lowerCamelCase	)

    # Check results
    Path(_lowerCamelCase	).mkdir(exist_ok=_lowerCamelCase	)
    model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     List[str]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fairseq_path",
      type=str,
      help=(
          "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
          " https://huggingface.co/models?other=opt_metasq"
      ),
  )
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



