





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 		snake_case_   (							UpperCAmelCase_     ,     UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		VQModel
	__UpperCamelCase   =		'''sample'''




	@property
	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     List[str]=(32, 32)   )       -> Any:

			'''simple docstring'''




			__lowercase											=     4
			__lowercase											=     3

			__lowercase											=     floats_tensor((batch_size, num_channels) + sizes   ).to(__lowerCamelCase   )

			return {"sample": image}




	@property
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			return (3, 32, 32)




	@property
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			return (3, 32, 32)




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     {
			    'block_out_channels': [32, 64],
			    'in_channels': 3,
			    'out_channels': 3,
			    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
			    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
			    'latent_channels': 3,
			}
			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict




	def    UpperCAmelCase			(						self							:     Any   )       -> List[Any]:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     Tuple   )       -> int:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Dict:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     VQModel.from_pretrained('fusing/vqgan-dummy'					,      output_loading_info=__lowerCamelCase   )
			self.assertIsNotNone(__lowerCamelCase   )
			self.assertEqual(len(loading_info['missing_keys']   )					,      0   )

			model.to(__lowerCamelCase   )
			__lowercase											=     model(**self.dummy_input   )

			assert image is not None, "Make sure output is not None"






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

			'''simple docstring'''




			__lowercase											=     VQModel.from_pretrained('fusing/vqgan-dummy'   )
			model.to(__lowerCamelCase   ).eval()

			torch.manual_seed(0   )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0   )

			__lowercase											=     torch.randn(1					,      model.config.in_channels					,      model.config.sample_size					,      model.config.sample_size   )
			__lowercase											=     image.to(__lowerCamelCase   )
			with torch.no_grad():
					__lowercase											=     model(__lowerCamelCase   ).sample

			__lowercase											=     output[0, -1, -3:, -3:].flatten().cpu()
			# fmt: off
			__lowercase											=     torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]   )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-3   )   )











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()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

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

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

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

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

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




