



'''simple docstring'''
import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_					:      Tuple    			=    logging.get_logger(__name__)






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						str												=  MobileNetVaConfig(layer_norm_eps=0.001				)

						if "_quant" in model_name:
												raise ValueError('Quantized models are not supported.'				)

						_UpperCamelCase   :						Optional[int]												=  re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'    ,    UpperCAmelCase_				)
						if matches:
												_UpperCamelCase   :						Union[str, Any]												=  float(matches[1]				)
												_UpperCamelCase   :						List[Any]												=  int(matches[2]				)

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						_UpperCamelCase   :						Dict												=  1_0_0_1
						_UpperCamelCase   :						Union[str, Any]												=  'imagenet-1k-id2label.json'
						_UpperCamelCase   :						Optional[Any]												=  'huggingface/label-files'
						_UpperCamelCase   :						List[Any]												=  json.load(open(hf_hub_download(UpperCAmelCase_    ,    UpperCAmelCase_    ,    repo_type='dataset'				)    ,    'r'				)				)
						_UpperCamelCase   :						List[str]												=  {int(UpperCAmelCase_				) + 1: v for k, v in idalabel.items()}
						_UpperCamelCase   :						Tuple												=  'background'
						_UpperCamelCase   :						str												=  idalabel
						_UpperCamelCase   :						Any												=  {v: k for k, v in idalabel.items()}

						return config






def A__       (	):
						_UpperCamelCase   :						List[Any]												=  'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase   :						List[Any]												=  Image.open(requests.get(UpperCAmelCase_    ,    stream=UpperCAmelCase_				).raw				)
						return im






@torch.no_grad()
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=False				):
						_UpperCamelCase   :						Union[str, Any]												=  get_mobilenet_va_config(UpperCAmelCase_				)

						# Load 🤗 model
						_UpperCamelCase   :						List[str]												=  MobileNetVaForImageClassification(UpperCAmelCase_				).eval()

						# Load weights from TensorFlow checkpoint
						load_tf_weights_in_mobilenet_va(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						_UpperCamelCase   :						Tuple												=  MobileNetVaImageProcessor(
						    crop_size={'width': config.image_size, 'height': config.image_size}    ,    size={'shortest_edge': config.image_size + 3_2}    ,    )
						_UpperCamelCase   :						int												=  image_processor(images=prepare_img()    ,    return_tensors='pt'				)
						_UpperCamelCase   :						Union[str, Any]												=  model(**UpperCAmelCase_				)
						_UpperCamelCase   :						Dict												=  outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												_UpperCamelCase   :						Tuple												=  torch.tensor([-4.1_739, -1.1_233, 3.1_205]				)
						elif model_name == "mobilenet_v1_0.75_192":
												_UpperCamelCase   :						int												=  torch.tensor([-3.9_440, -2.3_141, -0.3_333]				)
						else:
												_UpperCamelCase   :						List[str]												=  None

						if expected_logits is not None:
												assert torch.allclose(logits[0, :3]    ,    UpperCAmelCase_    ,    atol=1E-4				)

						Path(UpperCAmelCase_				).mkdir(exist_ok=UpperCAmelCase_				)
						print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
						model.save_pretrained(UpperCAmelCase_				)
						print(f'Saving image processor to {pytorch_dump_folder_path}'				)
						image_processor.save_pretrained(UpperCAmelCase_				)

						if push_to_hub:
												print('Pushing to the hub...'				)
												_UpperCamelCase   :						List[str]												=  'google/' + model_name
												image_processor.push_to_hub(UpperCAmelCase_				)
												model.push_to_hub(UpperCAmelCase_				)


if __name__ == "__main__":
					snake_case_					:      Optional[int]    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='mobilenet_v1_1.0_224',
					    type=str,
					    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
					)
					parser.add_argument(
					    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

					snake_case_					:      Tuple    			=    parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)







'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case_					:      Tuple    			=    False






@skip_mps
class 				lowercase__      (				lowercase		,   lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     StableDiffusionAttendAndExcitePipeline
	lowercase__    							=     False
	lowercase__    							=     TEXT_TO_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}  )
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS





	@classmethod
	def       UpperCamelCase_    (					cls   :							Any					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							List[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Union[str, Any]												=  UNetaDConditionModel(
							    block_out_channels=(32, 64)					,layers_per_block=1					,sample_size=32					,in_channels=4					,out_channels=4					,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,cross_attention_dim=32					,attention_head_dim=(2, 4)					,use_linear_projection=lowerCamelCase__					,)
							_UpperCamelCase   :						List[str]												=  DDIMScheduler(
							    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  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					)
							_UpperCamelCase   :						List[str]												=  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					,)
							_UpperCamelCase   :						List[str]												=  CLIPTextModel(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

							_UpperCamelCase   :						str												=  {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							str=0					):







							'''simple docstring'''
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						Union[str, Any]												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						int												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  'cpu'

							_UpperCamelCase   :						List[Any]												=  self.get_dummy_components()
							_UpperCamelCase   :						Union[str, Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  pipe(**lowerCamelCase__					).images
							_UpperCamelCase   :						Union[str, Any]												=  image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,(1, 64, 64, 3)					)
							_UpperCamelCase   :						Union[str, Any]												=  np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]					)
							_UpperCamelCase   :						Tuple												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							self._test_inference_batch_single_identical(batch_size=2					,expected_max_diff=7E-4					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							super().test_save_load_local(expected_max_difference=5E-4					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							super().test_save_load_optional_components(expected_max_difference=4E-4					)






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





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							Optional[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(51					)

							_UpperCamelCase   :						Optional[Any]												=  StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa					)
							pipe.to('cuda'					)

							_UpperCamelCase   :						Any												=  'a painting of an elephant with glasses'
							_UpperCamelCase   :						str												=  [5, 7]

							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,token_indices=lowerCamelCase__					,guidance_scale=7.5					,generator=lowerCamelCase__					,num_inference_steps=5					,max_iter_to_alter=5					,output_type='numpy'					,).images[0]

							_UpperCamelCase   :						Tuple												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'					)
							assert np.abs((expected_image - image).max()					) < 5E-1


