

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any				)							->				int:





    '''simple docstring'''






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

    return name
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Tuple:





    '''simple docstring'''






    for key in orig_state_dict.copy().keys():
        A__   				=       orig_state_dict.pop(SCREAMING_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
            A__   				=       key.split('.'				)
            A__				,						A__   				=       int(key_split[2]				), int(key_split[4]				)
            A__   				=       config.vision_config.hidden_size
            if "weight" in key:
                A__   				=       val[:dim, :]
                A__   				=       val[dim : dim * 2, :]
                A__   				=       val[-dim:, :]
            else:
                A__   				=       val[:dim]
                A__   				=       val[dim : dim * 2]
                A__   				=       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
            A__   				=       key.split('.'				)
            A__   				=       int(key_split[3]				)
            A__   				=       config.text_config.hidden_size
            if "weight" in key:
                A__   				=       val[:dim, :]
                A__   				=       val[
                    dim : dim * 2, :
                ]
                A__   				=       val[-dim:, :]
            else:
                A__   				=       val[:dim]
                A__   				=       val[dim : dim * 2]
                A__   				=       val[-dim:]
        else:
            A__   				=       rename_key(SCREAMING_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
            ):
                A__   				=       val.squeeze_()
            else:
                A__   				=       val

    return orig_state_dict
def 						_snake_case(   )							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__   				=       Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,      stream=SCREAMING_SNAKE_CASE__				).raw				)
    return im
@torch.no_grad()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     str="groupvit-gcc-yfcc"   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]=False				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       GroupViTConfig()
    A__   				=       GroupViTModel(SCREAMING_SNAKE_CASE__				).eval()

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

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

    with torch.no_grad():
        A__   				=       model(**SCREAMING_SNAKE_CASE__				)

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

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

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


if __name__ == "__main__":
     lowercase_			=  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`.",
     )
     lowercase_			=  parser.parse_args()

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




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

