


"""simple docstring"""







import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _A						(						lowercase		):







    """simple docstring"""
    a 		=SwinConfig()
    a 		=swin_name.split('''_'''		)

    a 		=name_split[1]
    a 		=int(name_split[4]		)
    a 		=int(name_split[3][-1]		)

    if model_size == "tiny":
        a 		=96
        a 		=(2, 2, 6, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "small":
        a 		=96
        a 		=(2, 2, 18, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "base":
        a 		=1_28
        a 		=(2, 2, 18, 2)
        a 		=(4, 8, 16, 32)
    else:
        a 		=1_92
        a 		=(2, 2, 18, 2)
        a 		=(6, 12, 24, 48)

    if "in22k" in swin_name:
        a 		=2_18_41
    else:
        a 		=10_00
        a 		='''huggingface/label-files'''
        a 		='''imagenet-1k-id2label.json'''
        a 		=json.load(open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		)		)
        a 		={int(lowercase		): v for k, v in idalabel.items()}
        a 		=idalabel
        a 		={v: k for k, v in idalabel.items()}

    a 		=img_size
    a 		=num_classes
    a 		=embed_dim
    a 		=depths
    a 		=num_heads
    a 		=window_size

    return config
def _A						(						lowercase		):







    """simple docstring"""
    if "patch_embed.proj" in name:
        a 		=name.replace('''patch_embed.proj'''       ,    '''embeddings.patch_embeddings.projection'''		)
    if "patch_embed.norm" in name:
        a 		=name.replace('''patch_embed.norm'''       ,    '''embeddings.norm'''		)
    if "layers" in name:
        a 		='''encoder.''' + name
    if "attn.proj" in name:
        a 		=name.replace('''attn.proj'''       ,    '''attention.output.dense'''		)
    if "attn" in name:
        a 		=name.replace('''attn'''       ,    '''attention.self'''		)
    if "norm1" in name:
        a 		=name.replace('''norm1'''       ,    '''layernorm_before'''		)
    if "norm2" in name:
        a 		=name.replace('''norm2'''       ,    '''layernorm_after'''		)
    if "mlp.fc1" in name:
        a 		=name.replace('''mlp.fc1'''       ,    '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a 		=name.replace('''mlp.fc2'''       ,    '''output.dense'''		)

    if name == "norm.weight":
        a 		='''layernorm.weight'''
    if name == "norm.bias":
        a 		='''layernorm.bias'''

    if "head" in name:
        a 		=name.replace('''head'''       ,    '''classifier'''		)
    else:
        a 		='''swin.''' + name

    return name
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a 		=orig_state_dict.pop(lowercase		)

        if "mask" in key:
            continue
        elif "qkv" in key:
            a 		=key.split('''.'''		)
            a 		=int(key_split[1]		)
            a 		=int(key_split[3]		)
            a 		=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_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 		=val

    return orig_state_dict
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=timm.create_model(lowercase       ,    pretrained=lowercase		)
    timm_model.eval()

    a 		=get_swin_config(lowercase		)
    a 		=SwinForImageClassification(lowercase		)
    model.eval()

    a 		=convert_state_dict(timm_model.state_dict()       ,    lowercase		)
    model.load_state_dict(lowercase		)

    a 		='''http://images.cocodataset.org/val2017/000000039769.jpg'''

    a 		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''       ,    '''-'''		)		)		)
    a 		=Image.open(requests.get(lowercase       ,    stream=lowercase		).raw		)
    a 		=image_processor(images=lowercase       ,    return_tensors='''pt'''		)

    a 		=timm_model(inputs['''pixel_values''']		)
    a 		=model(**lowercase		).logits

    assert torch.allclose(lowercase       ,    lowercase       ,    atol=1E-3		)

    print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(lowercase		)

    print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
    image_processor.save_pretrained(lowercase		)


if __name__ == "__main__":
       lowerCamelCase_   :   Tuple									=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swin_name""",
           default="""swin_tiny_patch4_window7_224""",
           type=str,
           help="""Name of the Swin timm model you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )

       lowerCamelCase_   :   str									=  parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=ShapEPipeline
		lowerCamelCase__				=['prompt']
		lowerCamelCase__				=['prompt']
		lowerCamelCase__				=[
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCamelCase__				=False



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim * 4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 8



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
			return tokenizer



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Optional[int]						=				CLIPTextConfig(
			    bos_token_id=0				,       eos_token_id=2				,       hidden_size=self.text_embedder_hidden_size				,       projection_dim=self.text_embedder_hidden_size				,       intermediate_size=37				,       layer_norm_eps=1E-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=10_00				,       )
			return CLIPTextModelWithProjection(a_					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Optional[int]						=				{
			    '''num_attention_heads''': 2,
			    '''attention_head_dim''': 16,
			    '''embedding_dim''': self.time_input_dim,
			    '''num_embeddings''': 32,
			    '''embedding_proj_dim''': self.text_embedder_hidden_size,
			    '''time_embed_dim''': self.time_embed_dim,
			    '''num_layers''': 1,
			    '''clip_embed_dim''': self.time_input_dim * 2,
			    '''additional_embeddings''': 0,
			    '''time_embed_act_fn''': '''gelu''',
			    '''norm_in_type''': '''layer''',
			    '''encoder_hid_proj_type''': None,
			    '''added_emb_type''': None,
			}

			__snake_case	:						Optional[int]						=				PriorTransformer(**a_					)
			return model



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Optional[int]						=				{
			    '''param_shapes''': (
			        (self.renderer_dim, 93),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			    ),
			    '''d_latent''': self.time_input_dim,
			    '''d_hidden''': self.renderer_dim,
			    '''n_output''': 12,
			    '''background''': (
			        0.1,
			        0.1,
			        0.1,
			    ),
			}
			__snake_case	:						List[Any]						=				ShapERenderer(**a_					)
			return model



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.dummy_prior
			__snake_case	:						str						=				self.dummy_text_encoder
			__snake_case	:						str						=				self.dummy_tokenizer
			__snake_case	:						Tuple						=				self.dummy_renderer

			__snake_case	:						int						=				HeunDiscreteScheduler(
			    beta_schedule='''exp'''				,       num_train_timesteps=10_24				,       prediction_type='''sample'''				,       use_karras_sigmas=a_				,       clip_sample=a_				,       clip_sample_range=1.0				,       )
			__snake_case	:						Union[str, Any]						=				{
			    '''prior''': prior,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''renderer''': renderer,
			    '''scheduler''': scheduler,
			}

			return components



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

			if str(a_					).startswith('''mps'''					):
				__snake_case	:						Tuple						=				torch.manual_seed(a_					)
			else:
				__snake_case	:						Union[str, Any]						=				torch.Generator(device=a_					).manual_seed(a_					)
			__snake_case	:						Optional[int]						=				{
			    '''prompt''': '''horse''',
			    '''generator''': generator,
			    '''num_inference_steps''': 1,
			    '''frame_size''': 32,
			    '''output_type''': '''np''',
			}
			return inputs



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''cpu'''

			__snake_case	:						str						=				self.get_dummy_components()

			__snake_case	:						List[Any]						=				self.pipeline_class(**a_					)
			__snake_case	:						str						=				pipe.to(a_					)

			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				pipe(**self.get_dummy_inputs(a_					)					)
			__snake_case	:						List[str]						=				output.images[0]
			__snake_case	:						List[str]						=				image[0, -3:, -3:, -1]

			assert image.shape == (20, 32, 32, 3)

			__snake_case	:						List[Any]						=				np.array(
			    [
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			    ]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_inference_batch_consistent(batch_sizes=[1, 2]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				torch_device == '''cpu'''
			__snake_case	:						Tuple						=				True

			self._test_inference_batch_single_identical(
			    batch_size=2				,       test_max_difference=a_				,       relax_max_difference=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.get_dummy_components()
			__snake_case	:						int						=				self.pipeline_class(**a_					)
			__snake_case	:						int						=				pipe.to(a_					)
			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				1
			__snake_case	:						List[Any]						=				2

			__snake_case	:						int						=				self.get_dummy_inputs(a_					)

			for key in inputs.keys():
				if key in self.batch_params:
					__snake_case	:						Dict						=				batch_size * [inputs[key]]

			__snake_case	:						Union[str, Any]						=				pipe(**a_				,       num_images_per_prompt=a_					)[0]

			assert images.shape[0] == batch_size * num_images_per_prompt



@slow
@require_torch_gpu
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/shap_e/test_shap_e_np_out.npy'''					)
			__snake_case	:						Optional[Any]						=				ShapEPipeline.from_pretrained('''openai/shap-e'''					)
			__snake_case	:						Optional[int]						=				pipe.to(a_					)
			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				torch.Generator(device=a_					).manual_seed(0					)

			__snake_case	:						str						=				pipe(
			    '''a shark'''				,       generator=a_				,       guidance_scale=15.0				,       num_inference_steps=64				,       frame_size=64				,       output_type='''np'''				,       ).images[0]

			assert images.shape == (20, 64, 64, 3)

			assert_mean_pixel_difference(a_				,       a_					)





