
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    _a (						lowerCamelCase							):
 lowerCamelCase		:     int          =				MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

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

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

 if model_name.startswith("""deeplabv3_"""							):
  lowerCamelCase		:     List[Any]          =				8
  lowerCamelCase		:     Any          =				21
  lowerCamelCase		:     Optional[Any]          =				"""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".
  lowerCamelCase		:     Tuple          =				1001
  lowerCamelCase		:     Any          =				"""imagenet-1k-id2label.json"""

 lowerCamelCase		:     Any          =				"""huggingface/label-files"""
 lowerCamelCase		:     int          =				json.load(open(hf_hub_download(lowerCamelCase,     lowerCamelCase,     repo_type="""dataset"""							),     """r"""							)							)

 if config.num_labels == 1001:
  lowerCamelCase		:     Dict          =				{int(lowerCamelCase							) + 1: v for k, v in idalabel.items()}
  lowerCamelCase		:     int          =				"""background"""
 else:
  lowerCamelCase		:     Union[str, Any]          =				{int(lowerCamelCase							): v for k, v in idalabel.items()}

 lowerCamelCase		:     Union[str, Any]          =				idalabel
 lowerCamelCase		:     List[str]          =				{v: k for k, v in idalabel.items()}

 return config





def    _a (						):
 lowerCamelCase		:     Union[str, Any]          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCamelCase		:     List[str]          =				Image.open(requests.get(lowerCamelCase,     stream=lowerCamelCase							).raw							)
 return im





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase=False							):
 lowerCamelCase		:     str          =				get_mobilenet_va_config(lowerCamelCase							)

 # Load 🤗 model
 if model_name.startswith("""deeplabv3_"""							):
  lowerCamelCase		:     Tuple          =				MobileNetVaForSemanticSegmentation(lowerCamelCase							).eval()
 else:
  lowerCamelCase		:     Any          =				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
 lowerCamelCase		:     Optional[int]          =				MobileNetVaImageProcessor(
     crop_size={"""width""": config.image_size, """height""": config.image_size},     size={"""shortest_edge""": config.image_size + 32},     )
 lowerCamelCase		:     Tuple          =				image_processor(images=prepare_img(),     return_tensors="""pt"""							)
 lowerCamelCase		:     Optional[int]          =				model(**lowerCamelCase							)
 lowerCamelCase		:     List[Any]          =				outputs.logits

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

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   lowerCamelCase		:     Optional[Any]          =				torch.tensor(
       [
           [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
           [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
           [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
       ]							)

  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":
   lowerCamelCase		:     Any          =				torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]							)
  elif model_name == "mobilenet_v2_1.0_224":
   lowerCamelCase		:     Union[str, Any]          =				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]							)
  elif model_name == "mobilenet_v2_0.75_160":
   lowerCamelCase		:     str          =				torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]							)
  elif model_name == "mobilenet_v2_0.35_96":
   lowerCamelCase		:     List[Any]          =				torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]							)
  else:
   lowerCamelCase		:     Union[str, Any]          =				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..."""							)
  lowerCamelCase		:     List[Any]          =				"""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
      )






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   Any   	=      KandinskyPipeline
      _UpperCAmelCase    :   Any   	=      [
          """prompt""",
          """image_embeds""",
          """negative_image_embeds""",
      ]
      _UpperCAmelCase    :   Union[str, Any]   	=      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
      _UpperCAmelCase    :   Dict   	=      [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _UpperCAmelCase    :   List[Any]   	=      False
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim * 4
      @property
      def 						UpperCamelCase__							(				self				):
       return 1_0_0
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
       return tokenizer
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Union[str, Any]          =				MCLIPConfig(
           numDims=self.cross_attention_dim   ,  transformerDimensions=self.text_embedder_hidden_size   ,  hidden_size=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  vocab_size=1_0_0_5   ,  )

       lowerCamelCase		:     Dict          =				MultilingualCLIP(__magic_name__				)
       lowerCamelCase		:     Optional[Any]          =				text_encoder.eval()

       return text_encoder
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)

       lowerCamelCase		:     Any          =				{
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """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,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       lowerCamelCase		:     Optional[int]          =				UNetaDConditionModel(**__magic_name__				)
       return model
      @property
      def 						UpperCamelCase__							(				self				):
       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Dict          =				VQModel(**self.dummy_movq_kwargs				)
       return model
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.dummy_text_encoder
       lowerCamelCase		:     int          =				self.dummy_tokenizer
       lowerCamelCase		:     Optional[int]          =				self.dummy_unet
       lowerCamelCase		:     str          =				self.dummy_movq

       lowerCamelCase		:     Optional[int]          =				DDIMScheduler(
           num_train_timesteps=1_0_0_0   ,  beta_schedule="""linear"""   ,  beta_start=0.00_085   ,  beta_end=0.012   ,  clip_sample=__magic_name__   ,  set_alpha_to_one=__magic_name__   ,  steps_offset=1   ,  prediction_type="""epsilon"""   ,  thresholding=__magic_name__   ,  )

       lowerCamelCase		:     Any          =				{
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }
       return components
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
       lowerCamelCase		:     str          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(seed + 1				)				).to(__magic_name__				)
       if str(__magic_name__				).startswith("""mps"""				):
        lowerCamelCase		:     Union[str, Any]          =				torch.manual_seed(__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				{
           """prompt""": """horse""",
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 6_4,
           """width""": 6_4,
           """guidance_scale""": 4.0,
           """num_inference_steps""": 2,
           """output_type""": """np""",
       }
       return inputs






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				"""cpu"""

       lowerCamelCase		:     str          =				self.get_dummy_components()

       lowerCamelCase		:     Optional[Any]          =				self.pipeline_class(**__magic_name__				)
       lowerCamelCase		:     str          =				pipe.to(__magic_name__				)

       pipe.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     str          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
       lowerCamelCase		:     str          =				output.images

       lowerCamelCase		:     Dict          =				pipe(
           **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

       lowerCamelCase		:     int          =				image[0, -3:, -3:, -1]
       lowerCamelCase		:     Tuple          =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       lowerCamelCase		:     int          =				np.array(
           [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]				)

       assert (
           np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 							A__		(     unittest.TestCase):
      def 						UpperCamelCase__							(				self				):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_text2img_cat_fp16.npy"""				)

       lowerCamelCase		:     List[Any]          =				KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""   ,  torch_dtype=torch.floataa				)
       pipe_prior.to(__magic_name__				)

       lowerCamelCase		:     List[str]          =				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,  torch_dtype=torch.floataa				)
       lowerCamelCase		:     str          =				pipeline.to(__magic_name__				)
       pipeline.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     Dict          =				"""red cat, 4k photo"""

       lowerCamelCase		:     Tuple          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase      ,   lowerCamelCase		:     Any          =				pipe_prior(
           __magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=5   ,  negative_prompt=""""""   ,  ).to_tuple()

       lowerCamelCase		:     Dict          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase		:     Optional[Any]          =				pipeline(
           __magic_name__   ,  image_embeds=__magic_name__   ,  negative_image_embeds=__magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=1_0_0   ,  output_type="""np"""   ,  )

       lowerCamelCase		:     str          =				output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       assert_mean_pixel_difference(__magic_name__   ,  __magic_name__				)




