

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : bool = False							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   10

     if only_cross_attention:
       SCREAMING_SNAKE_CASE:int   =   12
     else:
       # when only cross attention is not set, the cross attention dim must be the same as the query dim
       SCREAMING_SNAKE_CASE:List[Any]   =   query_dim

     return {
         "query_dim": query_dim,
         "cross_attention_dim": cross_attention_dim,
         "heads": 2,
         "dim_head": 4,
         "added_kv_proj_dim": 6,
         "norm_num_groups": 1,
         "only_cross_attention": only_cross_attention,
         "processor": AttnAddedKVProcessor(),
     }
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : str       ,SCREAMING_SNAKE_CASE__     : Optional[int]							):
     SCREAMING_SNAKE_CASE:int   =   2

     SCREAMING_SNAKE_CASE:int   =   torch.rand(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,3       ,2							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   torch.rand(SCREAMING_SNAKE_CASE__       ,4       ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   None

     return {
         "hidden_states": hidden_states,
         "encoder_hidden_states": encoder_hidden_states,
         "attention_mask": attention_mask,
     }







   def       __UpperCamelCase  (	self     : str							):
     # self and cross attention

     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Dict   =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]   =   Attention(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue(attn.to_k is not None							)
     self.assertTrue(attn.to_v is not None							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.get_forward_arguments(
         query_dim=constructor_args["query_dim"]       ,added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

     SCREAMING_SNAKE_CASE:List[str]   =   attn(**SCREAMING_SNAKE_CASE__							)

     # only self attention

     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Tuple   =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   Attention(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue(attn.to_k is None							)
     self.assertTrue(attn.to_v is None							)

     SCREAMING_SNAKE_CASE:str   =   self.get_forward_arguments(
         query_dim=constructor_args["query_dim"]       ,added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

     SCREAMING_SNAKE_CASE:str   =   attn(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)





class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     # the initial conversion succeeds
     SCREAMING_SNAKE_CASE:Tuple   =   DiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-pipe"       ,device_map="sequential"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     with tempfile.TemporaryDirectory() as tmpdir:
       # save the converted model
       pipe.save_pretrained(SCREAMING_SNAKE_CASE__							)

       # can also load the converted weights
       SCREAMING_SNAKE_CASE:Tuple   =   DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__       ,device_map="sequential"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,atol=1e-5							)							)
     self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,atol=1e-5							)							)









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


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





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   MobileNetVaConfig(layer_norm_eps=0.001	)

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

  SCREAMING_SNAKE_CASE:Any   =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$" ,    snake_case	)
  if matches:
    SCREAMING_SNAKE_CASE:str   =   float(matches[1]	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   int(matches[2]	)

  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:str   =   8
    SCREAMING_SNAKE_CASE:Optional[int]   =   21
    SCREAMING_SNAKE_CASE:Dict   =   "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:Optional[int]   =   1001
    SCREAMING_SNAKE_CASE:Tuple   =   "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]   =   "huggingface/label-files"
  SCREAMING_SNAKE_CASE:List[Any]   =   json.load(open(hf_hub_download(snake_case ,    snake_case ,    repo_type="dataset"	) ,    "r"	)	)

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

  SCREAMING_SNAKE_CASE:str   =   idalabel
  SCREAMING_SNAKE_CASE:int   =   {v: k for k, v in idalabel.items()}

  return config





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str   =   Image.open(requests.get(snake_case ,    stream=snake_case	).raw	)
  return im





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case=False	):
  SCREAMING_SNAKE_CASE:Any   =   get_mobilenet_va_config(snake_case	)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:Tuple   =   MobileNetVaForSemanticSegmentation(snake_case	).eval()
  else:
    SCREAMING_SNAKE_CASE:List[str]   =   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 MobileNetV2ImageProcessor
  SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size} ,    size={"shortest_edge": config.image_size + 32} ,    )
  SCREAMING_SNAKE_CASE:List[Any]   =   image_processor(images=prepare_img() ,    return_tensors="pt"	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   model(**snake_case	)
  SCREAMING_SNAKE_CASE:str   =   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:int   =   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] ,    snake_case ,    atol=1e-4	)
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      SCREAMING_SNAKE_CASE:List[str]   =   torch.tensor([0.0181, -1.0015, 0.4688]	)
    elif model_name == "mobilenet_v2_1.0_224":
      SCREAMING_SNAKE_CASE:Optional[int]   =   torch.tensor([0.2445, -1.1993, 0.1905]	)
    elif model_name == "mobilenet_v2_0.75_160":
      SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.tensor([0.2482, 0.4136, 0.6669]	)
    elif model_name == "mobilenet_v2_0.35_96":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor([0.1451, -0.4624, 0.7192]	)
    else:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   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..."	)
    SCREAMING_SNAKE_CASE:Tuple   =   "google/" + model_name
    image_processor.push_to_hub(snake_case	)
    model.push_to_hub(snake_case	)


if __name__ == "__main__":
    A_      =							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."
    )

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






