



"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

    return name





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		str							):
    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase							:  Union[str, Any]											=    orig_state_dict.pop(UpperCamelCase__							)

        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							:  int											=    key.split('''.'''							)
            _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    int(key_split[2]							), int(key_split[4]							)
            _UpperCAmelCase							:  Tuple											=    config.vision_config.hidden_size
            if "weight" in key:
                _UpperCAmelCase							:  Optional[Any]											=    val[:dim, :]
                _UpperCAmelCase							:  List[Any]											=    val[dim : dim * 2, :]
                _UpperCAmelCase							:  str											=    val[-dim:, :]
            else:
                _UpperCAmelCase							:  Dict											=    val[:dim]
                _UpperCAmelCase							:  List[Any]											=    val[dim : dim * 2]
                _UpperCAmelCase							:  List[Any]											=    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							:  Optional[int]											=    key.split('''.'''							)
            _UpperCAmelCase							:  int											=    int(key_split[3]							)
            _UpperCAmelCase							:  int											=    config.text_config.hidden_size
            if "weight" in key:
                _UpperCAmelCase							:  Union[str, Any]											=    val[:dim, :]
                _UpperCAmelCase							:  Any											=    val[
                    dim : dim * 2, :
                ]
                _UpperCAmelCase							:  Optional[Any]											=    val[-dim:, :]
            else:
                _UpperCAmelCase							:  Optional[int]											=    val[:dim]
                _UpperCAmelCase							:  List[str]											=    val[dim : dim * 2]
                _UpperCAmelCase							:  List[str]											=    val[-dim:]
        else:
            _UpperCAmelCase							:  Optional[Any]											=    rename_key(UpperCamelCase__							)
            # 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							:  Union[str, Any]											=    val.squeeze_()
            else:
                _UpperCAmelCase							:  str											=    val

    return orig_state_dict





def  lowerCamelCase_						():
    _UpperCAmelCase							:  List[str]											=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase							:  int											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							)
    return im





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Optional[Any]="groupvit-gcc-yfcc"     ,       UpperCamelCase__		:		Any=False							):
    _UpperCAmelCase							:  Any											=    GroupViTConfig()
    _UpperCAmelCase							:  int											=    GroupViTModel(UpperCamelCase__							).eval()

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

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

    with torch.no_grad():
        _UpperCAmelCase							:  Dict											=    model(**UpperCamelCase__							)

    if model_name == "groupvit-gcc-yfcc":
        _UpperCAmelCase							:  Tuple											=    torch.tensor([[13.3523, 6.3629]]							)
    elif model_name == "groupvit-gcc-redcaps":
        _UpperCAmelCase							:  str											=    torch.tensor([[16.1873, 8.6230]]							)
    else:
        raise ValueError(F'Model name {model_name} not supported.'							)
    assert torch.allclose(outputs.logits_per_image     ,       UpperCamelCase__     ,       atol=1E-3							)

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

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


if __name__ == "__main__":
  _lowerCAmelCase    :Optional[int]     =					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`.',
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()

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







"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


