

import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						VideoToVideoSDPipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}      ) - {'image', 'width', 'height'}
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}      ) - {'image'}
			SCREAMING_SNAKE_CASE							   =						PipelineTesterMixin.required_optional_params - {'latents'}
			SCREAMING_SNAKE_CASE							   =						False

			# No `output_type`.
			SCREAMING_SNAKE_CASE							   =						frozenset(
			    [
			        'num_inference_steps',
			        'generator',
			        'latents',
			        'return_dict',
			        'callback',
			        'callback_steps',
			    ]      )





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =UNetaDConditionModel(
						    block_out_channels=(32, 64, 64, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')					,  up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')					,  cross_attention_dim=32					,  attention_head_dim=4					,  )
						__a        =DDIMScheduler(
						    beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='scaled_linear'					,  clip_sample=__snake_case					,  set_alpha_to_one=__snake_case					,  )
						torch.manual_seed(0					)
						__a        =AutoencoderKL(
						    block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,  latent_channels=4					,  sample_size=128					,  )
						torch.manual_seed(0					)
						__a        =CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  hidden_act='gelu'					,  projection_dim=512					,  )
						__a        =CLIPTextModel(__snake_case					)
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						__a        ={
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						}
						return components





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Union[str, Any]:




						'''simple docstring'''
						# 3 frames
						__a        =floats_tensor((1, 3, 3, 32, 32)					,  rng=random.Random(__snake_case					)					).to(__snake_case					)

						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__a        ={
						    'prompt': 'A painting of a squirrel eating a burger',
						    'video': video,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'pt',
						}
						return inputs





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        ='cpu'  # ensure determinism for the device-dependent torch.Generator
						__a        =self.get_dummy_components()
						__a        =VideoToVideoSDPipeline(**__snake_case					)
						__a        =sd_pipe.to(__snake_case					)
						sd_pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					)
						__a        ='np'
						__a        =sd_pipe(**__snake_case					).frames
						__a        =frames[0][-3:, -3:, -1]

						assert frames[0].shape == (32, 32, 3)
						__a        =np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

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





			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()					,  reason='XFormers attention is only available with CUDA and `xformers` installed'					,  )
			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__snake_case					,  expected_max_diff=5e-3					)





			@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						pass





			@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						pass





			@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'					)
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						pass





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						return super().test_progress_bar()






@slow
@skip_mps
class 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'					,  torch_dtype=torch.floataa					)
						pipe.enable_model_cpu_offload()

						# 10 frames
						__a        =torch.Generator(device='cpu'					).manual_seed(0					)
						__a        =torch.randn((1, 10, 3, 1024, 576)					,  generator=__snake_case					)
						__a        =video.to('cuda'					)

						__a        ='Spiderman is surfing'

						__a        =pipe(__snake_case					,  video=__snake_case					,  generator=__snake_case					,  num_inference_steps=3					,  output_type='pt'					).frames

						__a        =np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]					)
						assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1e-2









import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  int	):




			"""simple docstring"""






			__a        =old_name

			if "patch_embed" in old_name:
						__a      ,    __a      ,    __a        =old_name.split('.'	)

						if layer == "0":
									__a        =old_name.replace('0'       ,     'convolution1'	)
						elif layer == "1":
									__a        =old_name.replace('1'       ,     'batchnorm_before'	)
						elif layer == "3":
									__a        =old_name.replace('3'       ,     'convolution2'	)
						else:
									__a        =old_name.replace('4'       ,     'batchnorm_after'	)

			if "network" in old_name and re.search(r'\d\.\d'       ,     _snake_case	):
						__a        =r'\b\d{2}\b'
						if bool(re.search(_snake_case       ,     _snake_case	)	):
									__a        =re.search(r'\d\.\d\d.'       ,     _snake_case	).group()
						else:
									__a        =re.search(r'\d\.\d.'       ,     _snake_case	).group()
						if int(match[0]	) < 6:
									__a        =old_name.replace(_snake_case       ,     ''	)
									__a        =trimmed_name.replace('network'       ,     match[0] + '.meta4D_layers.blocks.' + match[2:-1]	)
									__a        ='intermediate_stages.' + trimmed_name
						else:
									__a        =old_name.replace(_snake_case       ,     ''	)
									if int(match[2]	) < num_meta4D_last_stage:
												__a        =trimmed_name.replace('network'       ,     'meta4D_layers.blocks.' + match[2]	)
									else:
												__a        =str(int(match[2]	) - num_meta4D_last_stage	)
												__a        =trimmed_name.replace('network'       ,     'meta3D_layers.blocks.' + layer_index	)
												if "norm1" in old_name:
															__a        =trimmed_name.replace('norm1'       ,     'layernorm1'	)
												elif "norm2" in old_name:
															__a        =trimmed_name.replace('norm2'       ,     'layernorm2'	)
												elif "fc1" in old_name:
															__a        =trimmed_name.replace('fc1'       ,     'linear_in'	)
												elif "fc2" in old_name:
															__a        =trimmed_name.replace('fc2'       ,     'linear_out'	)

									__a        ='last_stage.' + trimmed_name

			elif "network" in old_name and re.search(r'.\d.'       ,     _snake_case	):
						__a        =old_name.replace('network'       ,     'intermediate_stages'	)

			if "fc" in new_name:
						__a        =new_name.replace('fc'       ,     'convolution'	)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						__a        =new_name.replace('norm1'       ,     'batchnorm_before'	)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						__a        =new_name.replace('norm2'       ,     'batchnorm_after'	)
			if "proj" in new_name:
						__a        =new_name.replace('proj'       ,     'projection'	)
			if "dist_head" in new_name:
						__a        =new_name.replace('dist_head'       ,     'distillation_classifier'	)
			elif "head" in new_name:
						__a        =new_name.replace('head'       ,     'classifier'	)
			elif "patch_embed" in new_name:
						__a        ='efficientformer.' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						__a        =new_name.replace('norm'       ,     'layernorm'	)
						__a        ='efficientformer.' + new_name
			else:
						__a        ='efficientformer.encoder.' + new_name

			return new_name






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Dict	):




			"""simple docstring"""






			for key in checkpoint.copy().keys():
						__a        =checkpoint.pop(_snake_case	)
						__a        =val

			return checkpoint






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        ='http://images.cocodataset.org/val2017/000000039769.jpg'
			__a        =Image.open(requests.get(_snake_case       ,     stream=_snake_case	).raw	)

			return image






def        UpperCamelCase_(							_snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  bool	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)['model']
			__a        =EfficientFormerConfig.from_json_file(_snake_case	)
			__a        =EfficientFormerForImageClassificationWithTeacher(_snake_case	)
			__a        ='_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

			__a        =config.depths[-1] - config.num_metaad_blocks + 1
			__a        =convert_torch_checkpoint(_snake_case       ,     _snake_case	)

			model.load_state_dict(_snake_case	)
			model.eval()

			__a        ={
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

			# prepare image
			__a        =prepare_img()
			__a        =256
			__a        =224
			__a        =EfficientFormerImageProcessor(
			    size={'shortest_edge': image_size}       ,     crop_size={'height': crop_size, 'width': crop_size}       ,     resample=pillow_resamplings['bicubic']       ,     )
			__a        =processor(images=_snake_case       ,     return_tensors='pt'	).pixel_values

			# original processing pipeline
			__a        =Compose(
			    [
			        Resize(_snake_case       ,     interpolation=pillow_resamplings['bicubic']	),
			        CenterCrop(_snake_case	),
			        ToTensor(),
			        Normalize(_snake_case       ,     _snake_case	),
			    ]	)
			__a        =image_transforms(_snake_case	).unsqueeze(0	)

			assert torch.allclose(_snake_case       ,     _snake_case	)

			__a        =model(_snake_case	)
			__a        =outputs.logits

			__a        =(1, 1000)

			if "l1" in model_name:
						__a        =torch.Tensor(
						    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
						assert torch.allclose(logits[0, :10]       ,     _snake_case       ,     atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__a        =torch.Tensor(
						    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
						assert torch.allclose(logits[0, :10]       ,     _snake_case       ,     atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__a        =torch.Tensor(
						    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'	)

			# Save Checkpoints
			Path(_snake_case	).mkdir(exist_ok=_snake_case	)
			model.save_pretrained(_snake_case	)
			print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'	)
			processor.save_pretrained(_snake_case	)
			print(F'Processor successfuly saved at {pytorch_dump_path}'	)

			if push_to_hub:
						print('Pushing model to the hub...'	)

						model.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'       ,     commit_message='Add model'       ,     use_temp_dir=_snake_case       ,     )
						processor.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'       ,     commit_message='Add image processor'       ,     use_temp_dir=_snake_case       ,     )


if __name__ == "__main__":
						_lowerCAmelCase					:			int  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--pytorch_model_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to EfficientFormer pytorch checkpoint.",
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The json file for EfficientFormer model config.",
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)

						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
						parser.add_argument(
						    "--no-push_to_hub",
						    dest="push_to_hub",
						    action="store_false",
						    help="Do not push model and image processor to the hub",
						)
						parser.set_defaults(push_to_hub=True)

						_lowerCAmelCase					:			Union[str, Any]  				=			parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=args.push_to_hub,
						)






