import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def  __UpperCamelCase    (						A							):
     # vision encoder
     if "img_encoder.pos_embed" in name:
          UpperCamelCase__										=      name.replace('''img_encoder.pos_embed''' ,							'''vision_model.embeddings.position_embeddings'''							)
     if "img_encoder.patch_embed.proj" in name:
          UpperCamelCase__										=      name.replace('''img_encoder.patch_embed.proj''' ,							'''vision_model.embeddings.patch_embeddings.projection'''							)
     if "img_encoder.patch_embed.norm" in name:
          UpperCamelCase__										=      name.replace('''img_encoder.patch_embed.norm''' ,							'''vision_model.embeddings.layernorm'''							)
     if "img_encoder.layers" in name:
          UpperCamelCase__										=      name.replace('''img_encoder.layers''' ,							'''vision_model.encoder.stages'''							)
     if "blocks" in name and "res" not in name:
          UpperCamelCase__										=      name.replace('''blocks''' ,							'''layers'''							)
     if "attn" in name and "pre_assign" not in name:
          UpperCamelCase__										=      name.replace('''attn''' ,							'''self_attn'''							)
     if "proj" in name and "self_attn" in name and "text" not in name:
          UpperCamelCase__										=      name.replace('''proj''' ,							'''out_proj'''							)
     if "pre_assign_attn.attn.proj" in name:
          UpperCamelCase__										=      name.replace('''pre_assign_attn.attn.proj''' ,							'''pre_assign_attn.attn.out_proj'''							)
     if "norm1" in name:
          UpperCamelCase__										=      name.replace('''norm1''' ,							'''layer_norm1'''							)
     if "norm2" in name and "pre_assign" not in name:
          UpperCamelCase__										=      name.replace('''norm2''' ,							'''layer_norm2'''							)
     if "img_encoder.norm" in name:
          UpperCamelCase__										=      name.replace('''img_encoder.norm''' ,							'''vision_model.layernorm'''							)
     # text encoder
     if "text_encoder.token_embedding" in name:
          UpperCamelCase__										=      name.replace('''text_encoder.token_embedding''' ,							'''text_model.embeddings.token_embedding'''							)
     if "text_encoder.positional_embedding" in name:
          UpperCamelCase__										=      name.replace('''text_encoder.positional_embedding''' ,							'''text_model.embeddings.position_embedding.weight'''							)
     if "text_encoder.transformer.resblocks." in name:
          UpperCamelCase__										=      name.replace('''text_encoder.transformer.resblocks.''' ,							'''text_model.encoder.layers.'''							)
     if "ln_1" in name:
          UpperCamelCase__										=      name.replace('''ln_1''' ,							'''layer_norm1'''							)
     if "ln_2" in name:
          UpperCamelCase__										=      name.replace('''ln_2''' ,							'''layer_norm2'''							)
     if "c_fc" in name:
          UpperCamelCase__										=      name.replace('''c_fc''' ,							'''fc1'''							)
     if "c_proj" in name:
          UpperCamelCase__										=      name.replace('''c_proj''' ,							'''fc2'''							)
     if "text_encoder" in name:
          UpperCamelCase__										=      name.replace('''text_encoder''' ,							'''text_model'''							)
     if "ln_final" in name:
          UpperCamelCase__										=      name.replace('''ln_final''' ,							'''final_layer_norm'''							)
     # projection layers
     if "img_projector.linear_hidden." in name:
          UpperCamelCase__										=      name.replace('''img_projector.linear_hidden.''' ,							'''visual_projection.'''							)
     if "img_projector.linear_out." in name:
          UpperCamelCase__										=      name.replace('''img_projector.linear_out.''' ,							'''visual_projection.3.'''							)
     if "text_projector.linear_hidden" in name:
          UpperCamelCase__										=      name.replace('''text_projector.linear_hidden''' ,							'''text_projection'''							)
     if "text_projector.linear_out" in name:
          UpperCamelCase__										=      name.replace('''text_projector.linear_out''' ,							'''text_projection.3'''							)

     return name







def  __UpperCamelCase    (						A ,							A							):
     for key in orig_state_dict.copy().keys():
          UpperCamelCase__										=      orig_state_dict.pop(A							)

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               UpperCamelCase__										=      key.split('''.'''							)
               UpperCamelCase__						,       UpperCamelCase__										=      int(key_split[2]							), int(key_split[4]							)
               UpperCamelCase__										=      config.vision_config.hidden_size
               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:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               UpperCamelCase__										=      key.split('''.'''							)
               UpperCamelCase__										=      int(key_split[3]							)
               UpperCamelCase__										=      config.text_config.hidden_size
               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:]
          else:
               UpperCamelCase__										=      rename_key(A							)
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    UpperCamelCase__										=      val.squeeze_()
               else:
                    UpperCamelCase__										=      val

     return orig_state_dict







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A							).raw							)
     return im







@torch.no_grad()
def  __UpperCamelCase    (						A ,							A ,							A="groupvit-gcc-yfcc" ,							A=False							):
     UpperCamelCase__										=      GroupViTConfig()
     UpperCamelCase__										=      GroupViTModel(A							).eval()

     UpperCamelCase__										=      torch.load(A ,							map_location='''cpu'''							)['''model''']
     UpperCamelCase__										=      convert_state_dict(A ,							A							)
     UpperCamelCase__						,       UpperCamelCase__										=      model.load_state_dict(A ,							strict=A							)
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(A							) == 0)

     # verify result
     UpperCamelCase__										=      CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''							)
     UpperCamelCase__										=      prepare_img()
     UpperCamelCase__										=      processor(text=['''a photo of a cat''', '''a photo of a dog'''] ,							images=A ,							padding=A ,							return_tensors='''pt'''							)

     with torch.no_grad():
          UpperCamelCase__										=      model(**A							)

     if model_name == "groupvit-gcc-yfcc":
          UpperCamelCase__										=      torch.tensor([[13.35_23, 6.36_29]]							)
     elif model_name == "groupvit-gcc-redcaps":
          UpperCamelCase__										=      torch.tensor([[16.18_73, 8.62_30]]							)
     else:
          raise ValueError(f"Model name {model_name} not supported."							)
     assert torch.allclose(outputs.logits_per_image ,							A ,							atol=1e-3							)

     processor.save_pretrained(A							)
     model.save_pretrained(A							)
     print('''Successfully saved processor and model to''' ,							A							)

     if push_to_hub:
          print('''Pushing to the hub...'''							)
          processor.push_to_hub(A ,							organization='''nielsr'''							)
          model.push_to_hub(A ,							organization='''nielsr'''							)


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
     )
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
     parser.add_argument(
         '''--model_name''',
         default='''groupvit-gccy-fcc''',
         type=str,
         help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
         help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
     )
     __magic_name__								=parser.parse_args()

     convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__magic_name__								='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
     raise ImportWarning(
         '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
         '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__magic_name__								=concatenate_datasets
__magic_name__								=DownloadConfig
__magic_name__								=DownloadManager
__magic_name__								=DownloadMode
__magic_name__								=DownloadConfig
__magic_name__								=DownloadMode
__magic_name__								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



