

'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			List[str]:
     """simple docstring"""




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

     return name


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[str]				)      ->			Union[str, Any]:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE							    =orig_state_dict.pop(snake_case_				)

          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
               _SCREAMING_SNAKE_CASE							    =key.split('.'				)
               _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =int(key_split[2]				), int(key_split[4]				)
               _SCREAMING_SNAKE_CASE							    =config.vision_config.hidden_size
               if "weight" in key:
                    _SCREAMING_SNAKE_CASE							    =val[:dim, :]
                    _SCREAMING_SNAKE_CASE							    =val[dim : dim * 2, :]
                    _SCREAMING_SNAKE_CASE							    =val[-dim:, :]
               else:
                    _SCREAMING_SNAKE_CASE							    =val[:dim]
                    _SCREAMING_SNAKE_CASE							    =val[dim : dim * 2]
                    _SCREAMING_SNAKE_CASE							    =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
               _SCREAMING_SNAKE_CASE							    =key.split('.'				)
               _SCREAMING_SNAKE_CASE							    =int(key_split[3]				)
               _SCREAMING_SNAKE_CASE							    =config.text_config.hidden_size
               if "weight" in key:
                    _SCREAMING_SNAKE_CASE							    =val[:dim, :]
                    _SCREAMING_SNAKE_CASE							    =val[
                        dim : dim * 2, :
                    ]
                    _SCREAMING_SNAKE_CASE							    =val[-dim:, :]
               else:
                    _SCREAMING_SNAKE_CASE							    =val[:dim]
                    _SCREAMING_SNAKE_CASE							    =val[dim : dim * 2]
                    _SCREAMING_SNAKE_CASE							    =val[-dim:]
          else:
               _SCREAMING_SNAKE_CASE							    =rename_key(snake_case_				)
               # 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
               ):
                    _SCREAMING_SNAKE_CASE							    =val.squeeze_()
               else:
                    _SCREAMING_SNAKE_CASE							    =val

     return orig_state_dict


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ='http://images.cocodataset.org/val2017/000000039769.jpg'
     _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(snake_case_							, stream=snake_case_				).raw				)
     return im


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Optional[Any]="groupvit-gcc-yfcc"							, _UpperCamelCase						:     List[str]=False				)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =GroupViTConfig()
     _SCREAMING_SNAKE_CASE							    =GroupViTModel(snake_case_				).eval()

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

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

     with torch.no_grad():
          _SCREAMING_SNAKE_CASE							    =model(**snake_case_				)

     if model_name == "groupvit-gcc-yfcc":
          _SCREAMING_SNAKE_CASE							    =torch.tensor([[13.35_23, 6.36_29]]				)
     elif model_name == "groupvit-gcc-redcaps":
          _SCREAMING_SNAKE_CASE							    =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							, snake_case_							, atol=1E-3				)

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

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


if __name__ == "__main__":
     lowerCamelCase :      Dict					 =     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 :      Optional[int]					 =     parser.parse_args()

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

'''simple docstring'''





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger()

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str ):
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   {"source": "What is love ?", "target": "life"}
         UpperCAmelCase_           =   {"train": 12, "val": 2, "test": 2}
         for split in ["train", "test", "val"]:
              for field in ["source", "target"]:
                   UpperCAmelCase_           =   "\n".join([contents[field]] * n_lines[split] )
                   with open(os.path.join(__a   ,			f"""{split}.{field}""" )   ,			"w" ) as f:
                        f.write(__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    str = "pytorch" ):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir()
         UpperCAmelCase_           =   os.path.join(__a   ,			"output" )
         UpperCAmelCase_           =   os.path.join(__a   ,			"data" )
         self._create_dummy_data(data_dir=__a )

         UpperCAmelCase_           =   f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

         if gpus > 0:
              testargs.append(f"""--gpus={gpus}""" )
              if is_apex_available():
                   testargs.append("--fp16" )
         else:
              testargs.append("--gpus=0" )
              testargs.append("--distributed_backend=ddp_cpu" )
              testargs.append("--num_processes=2" )

         UpperCAmelCase_           =   [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
         execute_subprocess_async(__a   ,			env=self.get_env() )

         UpperCAmelCase_           =   os.path.join(__a   ,			"metrics.json" )
         with open(__a ) as f:
              UpperCAmelCase_           =   json.load(__a )
         return result



    @require_torch_gpu
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_multi_gpu
    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self._run_finetune(gpus=2 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_gpu
    @require_ray
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )






    @require_torch_multi_gpu
    @require_ray
    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )




