





'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]       ):
    # encoder.embeddings are double copied in original FLAVA
    return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					Tuple       ):
    A__				=			{}

    for key, value in state_dict.items():
        if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
            continue

        A__				=			key.replace("heads.cmd.mim_head.cls.predictions"						,       "mmm_image_head"       )
        A__				=			key.replace("heads.cmd.mlm_head.cls.predictions"						,       "mmm_text_head"       )
        A__				=			key.replace("heads.cmd.itm_head.cls"						,       "itm_head"       )
        A__				=			key.replace("heads.cmd.itm_head.pooler"						,       "itm_head.pooler"       )
        A__				=			key.replace("heads.cmd.clip_head.logit_scale"						,       "flava.logit_scale"       )
        A__				=			key.replace("heads.fairseq_mlm.cls.predictions"						,       "mlm_head"       )
        A__				=			key.replace("heads.imagenet.mim_head.cls.predictions"						,       "mim_head"       )
        A__				=			key.replace("mm_text_projection"						,       "flava.text_to_mm_projection"       )
        A__				=			key.replace("mm_image_projection"						,       "flava.image_to_mm_projection"       )
        A__				=			key.replace("image_encoder.module"						,       "flava.image_model"       )
        A__				=			key.replace("text_encoder.module"						,       "flava.text_model"       )
        A__				=			key.replace("mm_encoder.module.encoder.cls_token"						,       "flava.multimodal_model.cls_token"       )
        A__				=			key.replace("mm_encoder.module"						,       "flava.multimodal_model"       )
        A__				=			key.replace("text_projection"						,       "flava.text_projection"       )
        A__				=			key.replace("image_projection"						,       "flava.image_projection"       )

        A__				=			value.float()

    for key, value in codebook_state_dict.items():
        A__				=			value

    return upgrade





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					Any						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Any=None       ):
    if config_path is not None:
        A__				=			FlavaConfig.from_pretrained(_lowerCamelCase       )
    else:
        A__				=			FlavaConfig()

    A__				=			FlavaForPreTraining(_lowerCamelCase       ).eval()

    A__				=			convert_dalle_checkpoint(_lowerCamelCase						,       _lowerCamelCase						,       save_checkpoint=_lowerCamelCase       )

    if os.path.exists(_lowerCamelCase       ):
        A__				=			torch.load(_lowerCamelCase						,       map_location="cpu"       )
    else:
        A__				=			torch.hub.load_state_dict_from_url(_lowerCamelCase						,       map_location="cpu"       )

    A__				=			upgrade_state_dict(_lowerCamelCase						,       _lowerCamelCase       )
    hf_model.load_state_dict(_lowerCamelCase       )
    A__				=			hf_model.state_dict()
    A__				=			count_parameters(_lowerCamelCase       )
    A__				=			count_parameters(_lowerCamelCase       ) + count_parameters(_lowerCamelCase       )

    assert torch.allclose(_lowerCamelCase						,       _lowerCamelCase						,       atol=1e-3       )

    hf_model.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
  __lowerCAmelCase							:	Union[str, Any]			=argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  __lowerCAmelCase							:	Any			=parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)










'''simple docstring'''

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

import black


__lowerCAmelCase							:	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							:	Optional[int]			="    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     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      :Optional[int]							)->							Tuple:
     A__				=			"src/transformers"
     shutil.rmtree(self.transformer_dir							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[str]						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :Tuple=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      :str							)->							Optional[Any]:
     A__				=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[int]:
     # Base copy consistency
     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      :Dict							)->							Any:
     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_							)



