import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
a_							=      """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
a_							=      direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_							=      re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a_							=      re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a_							=      re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_							=      [
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  str:
  __UpperCAmelCase						    =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"						,	A_					)
  return [m.group(0					) for m in matches]




def       __lowerCAmelCase      (						)    ->  Optional[Any]:
  __UpperCAmelCase						    =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __UpperCAmelCase						    =       {
      config.replace("Config"						,	""					): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  __UpperCAmelCase						    =       collections.defaultdict(A_					)
  __UpperCAmelCase						    =       collections.defaultdict(A_					)
  __UpperCAmelCase						    =       collections.defaultdict(A_					)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(A_					):
    __UpperCAmelCase						    =       None
    if _re_tf_models.match(A_					) is not None:
      __UpperCAmelCase						    =       tf_models
      __UpperCAmelCase						    =       _re_tf_models.match(A_					).groups()[0]
    elif _re_flax_models.match(A_					) is not None:
      __UpperCAmelCase						    =       flax_models
      __UpperCAmelCase						    =       _re_flax_models.match(A_					).groups()[0]
    elif _re_pt_models.match(A_					) is not None:
      __UpperCAmelCase						    =       pt_models
      __UpperCAmelCase						    =       _re_pt_models.match(A_					).groups()[0]

    if lookup_dict is not None:
      while len(A_					) > 0:
        if attr_name in model_prefix_to_model_type:
          __UpperCAmelCase						    =       True
          break
        # Try again after removing the last word in the name
        __UpperCAmelCase						    =       "".join(camel_case_split(A_					)[:-1]					)

  __UpperCAmelCase						    =       set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
  __UpperCAmelCase						    =       list(A_					)
  all_models.sort()

  __UpperCAmelCase						    =       {"model_type": all_models}
  __UpperCAmelCase						    =       [pt_models[t] for t in all_models]
  __UpperCAmelCase						    =       [tf_models[t] for t in all_models]
  __UpperCAmelCase						    =       [flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  __UpperCAmelCase						    =       {}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      __UpperCAmelCase						    =       "AutoProcessor"
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      __UpperCAmelCase						    =       "AutoTokenizer"
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      __UpperCAmelCase						    =       "AutoFeatureExtractor"
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      __UpperCAmelCase						    =       "AutoTokenizer"

  __UpperCAmelCase						    =       [processors[t] for t in all_models]

  return pd.DataFrame(A_					)




def       __lowerCAmelCase      (						A_   : Any					)    ->  List[Any]:
  __UpperCAmelCase						    =       [
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    __UpperCAmelCase						    =       [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
    __UpperCAmelCase						    =       [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
    # Loop through all three frameworks
    for module, cls, mapping in zip(A_						,	A_						,	A_					):
      # The type of pipeline may not exist in this framework
      if not hasattr(A_						,	A_					):
        continue
      # First extract all model_names
      __UpperCAmelCase						    =       []
      for name in getattr(A_						,	A_					).values():
        if isinstance(A_						,	A_					):
          model_names.append(A_					)
        else:
          model_names.extend(list(A_					)					)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}					)

  return table




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict					)    ->  Any:
  __UpperCAmelCase						    =       get_frameworks_table()
  __UpperCAmelCase						    =       Dataset.from_pandas(A_					)

  __UpperCAmelCase						    =       hf_hub_download(
      "huggingface/transformers-metadata"						,	"pipeline_tags.json"						,	repo_type="dataset"						,	token=A_					)
  __UpperCAmelCase						    =       Dataset.from_json(A_					)
  __UpperCAmelCase						    =       {
      tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
      for i in range(len(A_					)					)
  }
  __UpperCAmelCase						    =       update_pipeline_and_auto_class_table(A_					)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  __UpperCAmelCase						    =       sorted(table.keys()					)
  __UpperCAmelCase						    =       pd.DataFrame(
      {
          "model_class": model_classes,
          "pipeline_tag": [table[m][0] for m in model_classes],
          "auto_class": [table[m][1] for m in model_classes],
      }					)
  __UpperCAmelCase						    =       Dataset.from_pandas(A_					)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(A_						,	"frameworks.json"					)					)
    tags_dataset.to_json(os.path.join(A_						,	"pipeline_tags.json"					)					)

    if commit_sha is not None:
      __UpperCAmelCase						    =       (
          F'''Update with commit {commit_sha}\n\nSee: '''
          F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
      )
    else:
      __UpperCAmelCase						    =       "Update"

    upload_folder(
        repo_id="huggingface/transformers-metadata"						,	folder_path=A_						,	repo_type="dataset"						,	token=A_						,	commit_message=A_						,	)




def       __lowerCAmelCase      (						)    ->  str:
  __UpperCAmelCase						    =       {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  __UpperCAmelCase						    =       transformers_module.pipelines.SUPPORTED_TASKS
  __UpperCAmelCase						    =       []
  for key in pipeline_tasks:
    if key not in in_table:
      __UpperCAmelCase						    =       pipeline_tasks[key]["pt"]
      if isinstance(A_						,	(list, tuple)					):
        __UpperCAmelCase						    =       model[0]
      __UpperCAmelCase						    =       model.__name__
      if model not in in_table.values():
        missing.append(A_					)

  if len(A_					) > 0:
    __UpperCAmelCase						    =       ", ".join(A_					)
    raise ValueError(
        "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
        F'''`utils/update_metadata.py`: {msg}. Please add them!'''					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
       parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
       parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
       a_							=      parser.parse_args()

       if args.check_only:
              check_pipeline_tags()
       else:
              update_metadata(args.token, args.commit_sha)




import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def       __lowerCAmelCase      (						A_   : int					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __UpperCAmelCase						    =       4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       48
    __UpperCAmelCase						    =       "pixelshuffle_aux"
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       60
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       "pixelshuffledirect"
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       "nearest+conv"
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1_26
    __UpperCAmelCase						    =       7
    __UpperCAmelCase						    =       2_55.0
    __UpperCAmelCase						    =       ""

  return config




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : str					)    ->  Dict:
  if "patch_embed.proj" in name and "layers" not in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"embeddings.patch_embeddings.projection"					)
  if "patch_embed.norm" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.norm"						,	"embeddings.patch_embeddings.layernorm"					)
  if "layers" in name:
    __UpperCAmelCase						    =       name.replace("layers"						,	"encoder.stages"					)
  if "residual_group.blocks" in name:
    __UpperCAmelCase						    =       name.replace("residual_group.blocks"						,	"layers"					)
  if "attn.proj" in name:
    __UpperCAmelCase						    =       name.replace("attn.proj"						,	"attention.output.dense"					)
  if "attn" in name:
    __UpperCAmelCase						    =       name.replace("attn"						,	"attention.self"					)
  if "norm1" in name:
    __UpperCAmelCase						    =       name.replace("norm1"						,	"layernorm_before"					)
  if "norm2" in name:
    __UpperCAmelCase						    =       name.replace("norm2"						,	"layernorm_after"					)
  if "mlp.fc1" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc1"						,	"intermediate.dense"					)
  if "mlp.fc2" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc2"						,	"output.dense"					)
  if "q_bias" in name:
    __UpperCAmelCase						    =       name.replace("q_bias"						,	"query.bias"					)
  if "k_bias" in name:
    __UpperCAmelCase						    =       name.replace("k_bias"						,	"key.bias"					)
  if "v_bias" in name:
    __UpperCAmelCase						    =       name.replace("v_bias"						,	"value.bias"					)
  if "cpb_mlp" in name:
    __UpperCAmelCase						    =       name.replace("cpb_mlp"						,	"continuous_position_bias_mlp"					)
  if "patch_embed.proj" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"patch_embed.projection"					)

  if name == "norm.weight":
    __UpperCAmelCase						    =       "layernorm.weight"
  if name == "norm.bias":
    __UpperCAmelCase						    =       "layernorm.bias"

  if "conv_first" in name:
    __UpperCAmelCase						    =       name.replace("conv_first"						,	"first_convolution"					)

  if (
      "upsample" in name
      or "conv_before_upsample" in name
      or "conv_bicubic" in name
      or "conv_up" in name
      or "conv_hr" in name
      or "conv_last" in name
      or "aux" in name
  ):
    # heads
    if "conv_last" in name:
      __UpperCAmelCase						    =       name.replace("conv_last"						,	"final_convolution"					)
    if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
      if "conv_before_upsample.0" in name:
        __UpperCAmelCase						    =       name.replace("conv_before_upsample.0"						,	"conv_before_upsample"					)
      if "upsample.0" in name:
        __UpperCAmelCase						    =       name.replace("upsample.0"						,	"upsample.convolution_0"					)
      if "upsample.2" in name:
        __UpperCAmelCase						    =       name.replace("upsample.2"						,	"upsample.convolution_1"					)
      __UpperCAmelCase						    =       "upsample." + name
    elif config.upsampler == "pixelshuffledirect":
      __UpperCAmelCase						    =       name.replace("upsample.0.weight"						,	"upsample.conv.weight"					)
      __UpperCAmelCase						    =       name.replace("upsample.0.bias"						,	"upsample.conv.bias"					)
    else:
      pass
  else:
    __UpperCAmelCase						    =       "swin2sr." + name

  return name




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Tuple					)    ->  Optional[Any]:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

    if "qkv" in key:
      __UpperCAmelCase						    =       key.split("."					)
      __UpperCAmelCase						    =       int(key_split[1]					)
      __UpperCAmelCase						    =       int(key_split[4]					)
      __UpperCAmelCase						    =       config.embed_dim

      if "weight" in key:
        __UpperCAmelCase						    =       val[:dim, :]
        __UpperCAmelCase						    =       val[dim : dim * 2, :]
        __UpperCAmelCase						    =       val[-dim:, :]
      else:
        __UpperCAmelCase						    =       val[:dim]
        __UpperCAmelCase						    =       val[dim : dim * 2]
        __UpperCAmelCase						    =       val[-dim:]
      pass
    else:
      __UpperCAmelCase						    =       val

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Optional[int]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       get_config(A_					)
  __UpperCAmelCase						    =       SwinaSRForImageSuperResolution(A_					)
  model.eval()

  __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	map_location="cpu"					)
  __UpperCAmelCase						    =       convert_state_dict(A_						,	A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       model.load_state_dict(A_						,	strict=A_					)

  if len(A_					) > 0:
    raise ValueError("Missing keys when converting: {}".format(A_					)					)
  for key in unexpected_keys:
    if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
      raise ValueError(F'''Unexpected key {key} in state_dict'''					)

    # verify values
  __UpperCAmelCase						    =       "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					).convert("RGB"					)
  __UpperCAmelCase						    =       SwinaSRImageProcessor()
  # pixel_values = processor(image, return_tensors="pt").pixel_values

  __UpperCAmelCase						    =       1_26 if "Jpeg" in checkpoint_url else 2_56
  __UpperCAmelCase						    =       Compose(
      [
          Resize((image_size, image_size)					),
          ToTensor(),
          Normalize(mean=[0.4_85, 0.4_56, 0.4_06]						,	std=[0.2_29, 0.2_24, 0.2_25]					),
      ]					)
  __UpperCAmelCase						    =       transforms(A_					).unsqueeze(0					)

  if config.num_channels == 1:
    __UpperCAmelCase						    =       pixel_values[:, 0, :, :].unsqueeze(1					)

  __UpperCAmelCase						    =       model(A_					)

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 5_12, 5_12]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]					)
  elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]					)
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    # TODO values didn't match exactly here
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]					)
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 5_12, 5_12]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]					)
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]					)

  assert (
      outputs.reconstruction.shape == expected_shape
  ), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
  assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]						,	A_						,	atol=1e-3					)
  print("Looks ok!"					)

  __UpperCAmelCase						    =       {
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
          "swin2SR-classical-sr-x2-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
          "swin2SR-classical-sr-x4-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
          "swin2SR-compressed-sr-x4-48"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
          "swin2SR-lightweight-x2-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
          "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
      ),
  }
  __UpperCAmelCase						    =       url_to_name[checkpoint_url]

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(A_					)
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
    processor.save_pretrained(A_					)

  if push_to_hub:
    model.push_to_hub(F'''caidas/{model_name}'''					)
    processor.push_to_hub(F'''caidas/{model_name}'''					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--checkpoint_url""",
           default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
           type=str,
           help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )
       parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")

       a_							=      parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



