






'''simple docstring'''





import os
from pathlib import Path
def __UpperCAmelCase   (					A					:			Dict			,       A					:			int			,       A					:			Tuple		)    ->   List[Any]:
			UpperCAmelCase_      :       Any          =       {
			    "en": "Machine learning is great, isn't it?",
			    "ru": "Машинное обучение - это здорово, не так ли?",
			    "de": "Maschinelles Lernen ist großartig, oder?",
			}

			# BLUE scores as follows:
			# "pair": [fairseq, transformers]
			UpperCAmelCase_      :       List[str]          =       {
			    "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
			    "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
			    "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
			    "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
			}
			UpperCAmelCase_      :       List[Any]          =       F"{src_lang}-{tgt_lang}"

			UpperCAmelCase_      :       Union[str, Any]          =       F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
			os.makedirs(A			,       exist_ok=A		)
			UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''README.md'''		)
			print(F"Generating {path}"		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
						f.write(A		)


# make sure we are under the root of the project
_UpperCamelCase						:						Optional[int]  =					Path(__file__).resolve().parent.parent.parent
_UpperCamelCase						:						str  =					repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
						_UpperCamelCase ,							_UpperCamelCase ,							_UpperCamelCase						:						Tuple  =					model_name.split('-')
						_UpperCamelCase						:						Dict  =					model_cards_dir / 'facebook' / model_name
						write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)






"""simple docstring"""



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

    if model_size == "tiny":
        snake_case       : str    =       96
        snake_case       : List[Any]    =       (2, 2, 6, 2)
        snake_case       : Union[str, Any]    =       (3, 6, 12, 24)
    elif model_size == "small":
        snake_case       : int    =       96
        snake_case       : Optional[Any]    =       (2, 2, 18, 2)
        snake_case       : Any    =       (3, 6, 12, 24)
    elif model_size == "base":
        snake_case       : List[str]    =       128
        snake_case       : int    =       (2, 2, 18, 2)
        snake_case       : Any    =       (4, 8, 16, 32)
    else:
        snake_case       : List[str]    =       192
        snake_case       : str    =       (2, 2, 18, 2)
        snake_case       : Any    =       (6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





    if "patch_embed.proj" in name:
        snake_case       : int    =       name.replace("patch_embed.proj"		,				"embeddings.patch_embeddings.projection"					)
    if "patch_embed.norm" in name:
        snake_case       : Union[str, Any]    =       name.replace("patch_embed.norm"		,				"embeddings.norm"					)
    if "layers" in name:
        snake_case       : Optional[Any]    =       "encoder." + name
    if "attn.proj" in name:
        snake_case       : List[Any]    =       name.replace("attn.proj"		,				"attention.output.dense"					)
    if "attn" in name:
        snake_case       : List[str]    =       name.replace("attn"		,				"attention.self"					)
    if "norm1" in name:
        snake_case       : List[str]    =       name.replace("norm1"		,				"layernorm_before"					)
    if "norm2" in name:
        snake_case       : Optional[int]    =       name.replace("norm2"		,				"layernorm_after"					)
    if "mlp.fc1" in name:
        snake_case       : int    =       name.replace("mlp.fc1"		,				"intermediate.dense"					)
    if "mlp.fc2" in name:
        snake_case       : Tuple    =       name.replace("mlp.fc2"		,				"output.dense"					)

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case       : List[Any]    =       key.split("."					)
            snake_case       : Dict    =       int(key_split[1]					)
            snake_case       : Dict    =       int(key_split[3]					)
            snake_case       : str    =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case       : Dict    =       val[:dim, :]
                snake_case       : Tuple    =       val[
                    dim : dim * 2, :
                ]
                snake_case       : List[str]    =       val[-dim:, :]
            else:
                snake_case       : Dict    =       val[
                    :dim
                ]
                snake_case       : List[str]    =       val[
                    dim : dim * 2
                ]
                snake_case       : Union[str, Any]    =       val[
                    -dim:
                ]
        else:
            snake_case       : Any    =       val

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

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

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





