

import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    unittest.TestCase):
						_UpperCamelCase:Dict	=		UnCLIPPipeline
						_UpperCamelCase:Any	=		TEXT_TO_IMAGE_PARAMS - {
						    "negative_prompt",
						    "height",
						    "width",
						    "negative_prompt_embeds",
						    "guidance_scale",
						    "prompt_embeds",
						    "cross_attention_kwargs",
						}
						_UpperCamelCase:Union[str, Any]	=		TEXT_TO_IMAGE_BATCH_PARAMS
						_UpperCamelCase:int	=		[
						    "generator",
						    "return_dict",
						    "prior_num_inference_steps",
						    "decoder_num_inference_steps",
						    "super_res_num_inference_steps",
						]
						_UpperCamelCase:List[str]	=		False







						@property
						def        _snake_case				(			self							)->							Optional[Any]:
													return 32







						@property
						def        _snake_case				(			self							)->							List[Any]:
													return 32







						@property
						def        _snake_case				(			self							)->							Dict:
													return self.time_input_dim







						@property
						def        _snake_case				(			self							)->							Optional[Any]:
													return self.time_input_dim * 4







						@property
						def        _snake_case				(			self							)->							Any:
													return 100







						@property
						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer







						@property
						def        _snake_case				(			self							)->							Any:
													torch.manual_seed(0							)
													lowerCamelCase_								=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=1000     ,						)
													return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE							)







						@property
						def        _snake_case				(			self							)->							List[str]:
													torch.manual_seed(0							)

													lowerCamelCase_								={
													    "num_attention_heads": 2,
													    "attention_head_dim": 12,
													    "embedding_dim": self.text_embedder_hidden_size,
													    "num_layers": 1,
													}

													lowerCamelCase_								=PriorTransformer(**_SCREAMING_SNAKE_CASE							)
													return model







						@property
						def        _snake_case				(			self							)->							List[Any]:
													torch.manual_seed(0							)

													lowerCamelCase_								={
													    "clip_embeddings_dim": self.text_embedder_hidden_size,
													    "time_embed_dim": self.time_embed_dim,
													    "cross_attention_dim": self.cross_attention_dim,
													}

													lowerCamelCase_								=UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE							)
													return model







						@property
						def        _snake_case				(			self							)->							List[Any]:
													torch.manual_seed(0							)

													lowerCamelCase_								={
													    "sample_size": 32,
													    # RGB in channels
													    "in_channels": 3,
													    # Out channels is double in channels because predicts mean and variance
													    "out_channels": 6,
													    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
													    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
													    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
													    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    "layers_per_block": 1,
													    "cross_attention_dim": self.cross_attention_dim,
													    "attention_head_dim": 4,
													    "resnet_time_scale_shift": "scale_shift",
													    "class_embed_type": "identity",
													}

													lowerCamelCase_								=UNetaDConditionModel(**_SCREAMING_SNAKE_CASE							)
													return model







						@property
						def        _snake_case				(			self							)->							Optional[Any]:
													return {
													    "sample_size": 64,
													    "layers_per_block": 1,
													    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
													    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
													    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    "in_channels": 6,
													    "out_channels": 3,
													}







						@property
						def        _snake_case				(			self							)->							Dict:
													torch.manual_seed(0							)

													lowerCamelCase_								=UNetaDModel(**self.dummy_super_res_kwargs							)
													return model







						@property
						def        _snake_case				(			self							)->							int:
													# seeded differently to get different unet than `self.dummy_super_res_first`
													torch.manual_seed(1							)

													lowerCamelCase_								=UNetaDModel(**self.dummy_super_res_kwargs							)
													return model







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=self.dummy_prior
													lowerCamelCase_								=self.dummy_decoder
													lowerCamelCase_								=self.dummy_text_proj
													lowerCamelCase_								=self.dummy_text_encoder
													lowerCamelCase_								=self.dummy_tokenizer
													lowerCamelCase_								=self.dummy_super_res_first
													lowerCamelCase_								=self.dummy_super_res_last

													lowerCamelCase_								=UnCLIPScheduler(
													    variance_type="""fixed_small_log"""     ,						prediction_type="""sample"""     ,						num_train_timesteps=1000     ,						clip_sample_range=5.0     ,						)

													lowerCamelCase_								=UnCLIPScheduler(
													    variance_type="""learned_range"""     ,						prediction_type="""epsilon"""     ,						num_train_timesteps=1000     ,						)

													lowerCamelCase_								=UnCLIPScheduler(
													    variance_type="""fixed_small_log"""     ,						prediction_type="""epsilon"""     ,						num_train_timesteps=1000     ,						)

													lowerCamelCase_								={
													    "prior": prior,
													    "decoder": decoder,
													    "text_proj": text_proj,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "super_res_first": super_res_first,
													    "super_res_last": super_res_last,
													    "prior_scheduler": prior_scheduler,
													    "decoder_scheduler": decoder_scheduler,
													    "super_res_scheduler": super_res_scheduler,
													}

													return components







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=0							)->							List[str]:
													if str(_SCREAMING_SNAKE_CASE							).startswith("""mps"""							):
																				lowerCamelCase_								=torch.manual_seed(_SCREAMING_SNAKE_CASE							)
													else:
																				lowerCamelCase_								=torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								={
													    "prompt": "horse",
													    "generator": generator,
													    "prior_num_inference_steps": 2,
													    "decoder_num_inference_steps": 2,
													    "super_res_num_inference_steps": 2,
													    "output_type": "numpy",
													}
													return inputs







						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								="cpu"

													lowerCamelCase_								=self.get_dummy_components()

													lowerCamelCase_								=self.pipeline_class(**_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=pipe.to(_SCREAMING_SNAKE_CASE							)

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)							)
													lowerCamelCase_								=output.images

													lowerCamelCase_								=pipe(
													    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)     ,						return_dict=_SCREAMING_SNAKE_CASE     ,						)[0]

													lowerCamelCase_								=image[0, -3:, -3:, -1]
													lowerCamelCase_								=image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													lowerCamelCase_								=np.array(
													    [
													        0.9_9_9_7,
													        0.9_9_8_8,
													        0.0_0_2_8,
													        0.9_9_9_7,
													        0.9_9_8_4,
													        0.9_9_6_5,
													        0.0_0_2_9,
													        0.9_9_8_6,
													        0.0_0_2_5,
													    ]							)

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







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=torch.device("""cpu"""							)


													class 			_SCREAMING_SNAKE_CASE      :
																			_UpperCamelCase:Any	=		1

													lowerCamelCase_								=self.get_dummy_components()

													lowerCamelCase_								=self.pipeline_class(**_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=pipe.to(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=components["prior"]
													lowerCamelCase_								=components["decoder"]
													lowerCamelCase_								=components["super_res_first"]
													lowerCamelCase_								=components["tokenizer"]
													lowerCamelCase_								=components["text_encoder"]

													lowerCamelCase_								=torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
													lowerCamelCase_								=prior.dtype
													lowerCamelCase_								=1

													lowerCamelCase_								=(batch_size, prior.config.embedding_dim)
													lowerCamelCase_								=pipe.prepare_latents(
													    _SCREAMING_SNAKE_CASE     ,						dtype=_SCREAMING_SNAKE_CASE     ,						device=_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE     ,						latents=_SCREAMING_SNAKE_CASE     ,						scheduler=DummyScheduler()							)
													lowerCamelCase_								=(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
													lowerCamelCase_								=pipe.prepare_latents(
													    _SCREAMING_SNAKE_CASE     ,						dtype=_SCREAMING_SNAKE_CASE     ,						device=_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE     ,						latents=_SCREAMING_SNAKE_CASE     ,						scheduler=DummyScheduler()							)

													lowerCamelCase_								=(
													    batch_size,
													    super_res_first.config.in_channels // 2,
													    super_res_first.config.sample_size,
													    super_res_first.config.sample_size,
													)
													lowerCamelCase_								=pipe.prepare_latents(
													    _SCREAMING_SNAKE_CASE     ,						dtype=_SCREAMING_SNAKE_CASE     ,						device=_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE     ,						latents=_SCREAMING_SNAKE_CASE     ,						scheduler=DummyScheduler()							)

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								="this is a prompt example"

													lowerCamelCase_								=torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
													lowerCamelCase_								=pipe(
													    [prompt]     ,						generator=_SCREAMING_SNAKE_CASE     ,						prior_num_inference_steps=2     ,						decoder_num_inference_steps=2     ,						super_res_num_inference_steps=2     ,						prior_latents=_SCREAMING_SNAKE_CASE     ,						decoder_latents=_SCREAMING_SNAKE_CASE     ,						super_res_latents=_SCREAMING_SNAKE_CASE     ,						output_type="""np"""     ,						)
													lowerCamelCase_								=output.images

													lowerCamelCase_								=tokenizer(
													    _SCREAMING_SNAKE_CASE     ,						padding="""max_length"""     ,						max_length=tokenizer.model_max_length     ,						return_tensors="""pt"""     ,						)
													lowerCamelCase_								=text_encoder(text_inputs.input_ids							)
													lowerCamelCase_								=text_inputs.attention_mask

													lowerCamelCase_								=torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
													lowerCamelCase_								=pipe(
													    generator=_SCREAMING_SNAKE_CASE     ,						prior_num_inference_steps=2     ,						decoder_num_inference_steps=2     ,						super_res_num_inference_steps=2     ,						prior_latents=_SCREAMING_SNAKE_CASE     ,						decoder_latents=_SCREAMING_SNAKE_CASE     ,						super_res_latents=_SCREAMING_SNAKE_CASE     ,						text_model_output=_SCREAMING_SNAKE_CASE     ,						text_attention_mask=_SCREAMING_SNAKE_CASE     ,						output_type="""np"""     ,						)[0]

													# make sure passing text embeddings manually is identical
													assert np.abs(image - image_from_text							).max() < 1E-4







						@skip_mps
						def        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=torch_device == "cpu"

													self._test_attention_slicing_forward_pass(test_max_difference=_SCREAMING_SNAKE_CASE     ,						expected_max_diff=0.0_1							)







						@skip_mps
						def        _snake_case				(			self							)->							Dict:
													lowerCamelCase_								=torch_device == "cpu"
													lowerCamelCase_								=True
													lowerCamelCase_								=[
													    "prior_num_inference_steps",
													    "decoder_num_inference_steps",
													    "super_res_num_inference_steps",
													]

													self._test_inference_batch_single_identical(
													    test_max_difference=_SCREAMING_SNAKE_CASE     ,						relax_max_difference=_SCREAMING_SNAKE_CASE     ,						additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE     ,						)







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=[
													    "prior_num_inference_steps",
													    "decoder_num_inference_steps",
													    "super_res_num_inference_steps",
													]

													if torch_device == "mps":
																				# TODO: MPS errors with larger batch sizes
																				lowerCamelCase_								=[2, 3]
																				self._test_inference_batch_consistent(
																				    batch_sizes=_SCREAMING_SNAKE_CASE     ,						additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE     ,						)
													else:
																				self._test_inference_batch_consistent(
																				    additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE							)







						@skip_mps
						def        _snake_case				(			self							)->							List[Any]:
													return super().test_dict_tuple_outputs_equivalent()







						@skip_mps
						def        _snake_case				(			self							)->							str:
													return super().test_save_load_local()







						@skip_mps
						def        _snake_case				(			self							)->							List[Any]:
													return super().test_save_load_optional_components()


@nightly
class 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						def        _snake_case				(			self							)->							Any:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/unclip/karlo_v1_alpha_horse_cpu.npy"""							)

													lowerCamelCase_								=UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							)
													pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=torch.manual_seed(0							)
													lowerCamelCase_								=pipeline(
													    """horse"""     ,						num_images_per_prompt=1     ,						generator=_SCREAMING_SNAKE_CASE     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=output.images[0]

													assert image.shape == (256, 256, 3)
													assert np.abs(expected_image - image							).max() < 1E-1


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







						def        _snake_case				(			self							)->							int:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/unclip/karlo_v1_alpha_horse_fp16.npy"""							)

													lowerCamelCase_								=UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     ,						torch_dtype=torch.floataa							)
													lowerCamelCase_								=pipeline.to(_SCREAMING_SNAKE_CASE							)
													pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=torch.Generator(device="""cpu"""							).manual_seed(0							)
													lowerCamelCase_								=pipeline(
													    """horse"""     ,						generator=_SCREAMING_SNAKE_CASE     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=output.images[0]

													assert image.shape == (256, 256, 3)

													assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							List[Any]:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													lowerCamelCase_								=UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     ,						torch_dtype=torch.floataa							)
													lowerCamelCase_								=pipe.to(_SCREAMING_SNAKE_CASE							)
													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													lowerCamelCase_								=pipe(
													    """horse"""     ,						num_images_per_prompt=1     ,						prior_num_inference_steps=2     ,						decoder_num_inference_steps=2     ,						super_res_num_inference_steps=2     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=torch.cuda.max_memory_allocated()
													# make sure that less than 7 GB is allocated
													assert mem_bytes < 7 * 10**9





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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

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

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"pascal-voc-id2label.json"
 else:
  # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
  # of the usual 1000. The first class (index 0) is "background".
  __SCREAMING_SNAKE_CASE							:     int							=						1001
  __SCREAMING_SNAKE_CASE							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     str							=						MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

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

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

 if model_name.startswith("deeplabv3_"  ):
  assert logits.shape == (1, 21, 65, 65)

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.tensor(
       [
           [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
           [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
           [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
       ]  )

  else:
   raise ValueError(F"""Unknown model name: {model_name}"""  )

  assert torch.allclose(logits[0, :3, :3, :3]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v2_1.4_224":
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.tensor([0.0181, -1.0015, 0.4688]  )
  elif model_name == "mobilenet_v2_1.0_224":
   __SCREAMING_SNAKE_CASE							:     str							=						torch.tensor([0.2445, -1.1993, 0.1905]  )
  elif model_name == "mobilenet_v2_0.75_160":
   __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor([0.2482, 0.4136, 0.6669]  )
  elif model_name == "mobilenet_v2_0.35_96":
   __SCREAMING_SNAKE_CASE							:     Any							=						torch.tensor([0.1451, -0.4624, 0.7192]  )
  else:
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						None

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

 Path(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
 print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
 model.save_pretrained(__lowerCamelCase  )
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
 image_processor.save_pretrained(__lowerCamelCase  )

 if push_to_hub:
  print("Pushing to the hub..."  )
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"google/" + model_name
  image_processor.push_to_hub(__lowerCamelCase  )
  model.push_to_hub(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v2_1.0_224""",
          type=str,
          help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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."""
      )

      _lowerCamelCase         =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






