



'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (_UpperCAmelCase			):
 __A			  =					(UnCLIPScheduler,)







 def   _a   (    self ,				**_lowerCAmelCase						)    ->    str:
       '''simple docstring'''






       lowercase        =							{
           """num_train_timesteps""": 1000,
           """variance_type""": """fixed_small_log""",
           """clip_sample""": True,
           """clip_sample_range""": 1.0,
           """prediction_type""": """epsilon""",
       }

       config.update(**_lowerCAmelCase						)
       return config







 def   _a   (    self						)    ->    str:
       '''simple docstring'''






       for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)







 def   _a   (    self						)    ->    Optional[Any]:
       '''simple docstring'''






       for variance in ["fixed_small_log", "learned_range"]:
             self.check_over_configs(variance_type=_lowerCAmelCase						)







 def   _a   (    self						)    ->    int:
       '''simple docstring'''






       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_lowerCAmelCase						)







 def   _a   (    self						)    ->    Optional[Any]:
       '''simple docstring'''






       for clip_sample_range in [1, 5, 10, 20]:
             self.check_over_configs(clip_sample_range=_lowerCAmelCase						)







 def   _a   (    self						)    ->    Tuple:
       '''simple docstring'''






       for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(prediction_type=_lowerCAmelCase						)







 def   _a   (    self						)    ->    Dict:
       '''simple docstring'''






       for time_step in [0, 500, 999]:
             for prev_timestep in [None, 5, 100, 250, 500, 750]:
                   if prev_timestep is not None and prev_timestep >= time_step:
                         continue

                   self.check_over_forward(time_step=_lowerCAmelCase ,				prev_timestep=_lowerCAmelCase						)







 def   _a   (    self						)    ->    Optional[Any]:
       '''simple docstring'''






       lowercase        =							self.scheduler_classes[0]
       lowercase        =							self.get_scheduler_config(variance_type="""fixed_small_log"""						)
       lowercase        =							scheduler_class(**_lowerCAmelCase						)

       assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.00_00E-10						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.054_9625						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.999_4987						)						) < 1E-5







 def   _a   (    self						)    ->    Any:
       '''simple docstring'''






       lowercase        =							self.scheduler_classes[0]
       lowercase        =							self.get_scheduler_config(variance_type="""learned_range"""						)
       lowercase        =							scheduler_class(**_lowerCAmelCase						)

       lowercase        =							0.5

       assert scheduler._get_variance(1 ,				predicted_variance=_lowerCAmelCase						) - -10.171_2790 < 1E-5
       assert scheduler._get_variance(487 ,				predicted_variance=_lowerCAmelCase						) - -5.799_8052 < 1E-5
       assert scheduler._get_variance(999 ,				predicted_variance=_lowerCAmelCase						) - -0.001_0011 < 1E-5







 def   _a   (    self						)    ->    Dict:
       '''simple docstring'''






       lowercase        =							self.scheduler_classes[0]
       lowercase        =							self.get_scheduler_config()
       lowercase        =							scheduler_class(**_lowerCAmelCase						)

       lowercase        =							scheduler.timesteps

       lowercase        =							self.dummy_model()
       lowercase        =							self.dummy_sample_deter
       lowercase        =							torch.manual_seed(0						)

       for i, t in enumerate(_lowerCAmelCase						):
             # 1. predict noise residual
             lowercase        =							model(_lowerCAmelCase ,				_lowerCAmelCase						)

             # 2. predict previous mean of sample x_t-1
             lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				generator=_lowerCAmelCase						).prev_sample

             lowercase        =							pred_prev_sample

       lowercase        =							torch.sum(torch.abs(_lowerCAmelCase						)						)
       lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_sum.item() - 252.268_2495						) < 1E-2
       assert abs(result_mean.item() - 0.328_4743						) < 1E-3







 def   _a   (    self						)    ->    Union[str, Any]:
       '''simple docstring'''






       lowercase        =							self.scheduler_classes[0]
       lowercase        =							self.get_scheduler_config()
       lowercase        =							scheduler_class(**_lowerCAmelCase						)

       scheduler.set_timesteps(25						)

       lowercase        =							scheduler.timesteps

       lowercase        =							self.dummy_model()
       lowercase        =							self.dummy_sample_deter
       lowercase        =							torch.manual_seed(0						)

       for i, t in enumerate(_lowerCAmelCase						):
             # 1. predict noise residual
             lowercase        =							model(_lowerCAmelCase ,				_lowerCAmelCase						)

             if i + 1 == timesteps.shape[0]:
                   lowercase        =							None
             else:
                   lowercase        =							timesteps[i + 1]

             # 2. predict previous mean of sample x_t-1
             lowercase        =							scheduler.step(
                 _lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				prev_timestep=_lowerCAmelCase ,				generator=_lowerCAmelCase						).prev_sample

             lowercase        =							pred_prev_sample

       lowercase        =							torch.sum(torch.abs(_lowerCAmelCase						)						)
       lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_sum.item() - 258.204_4983						) < 1E-2
       assert abs(result_mean.item() - 0.336_2038						) < 1E-3







 def   _a   (    self						)    ->    Tuple:
       '''simple docstring'''






       pass







 def   _a   (    self						)    ->    Optional[int]:
       '''simple docstring'''






       pass







'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_      :				List[str]   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												lowercase        =							[144, 192, 240]
												lowercase        =							[16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												lowercase        =							[96, 120, 144]
												lowercase        =							[16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												lowercase        =							[64, 80, 96]
												lowercase        =							[16, 16, 24, 48, 64, 80, 320]
												lowercase        =							0.05
												lowercase        =							2.0

						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							512
												lowercase        =							16
												lowercase        =							21
												lowercase        =							"""pascal-voc-id2label.json"""
						else:
												lowercase        =							1000
												lowercase        =							"""imagenet-1k-id2label.json"""

						lowercase        =							"""huggingface/label-files"""
						lowercase        =							json.load(open(hf_hub_download(lowercase_ ,  lowercase_ ,  repo_type="""dataset"""						) ,  """r"""						)						)
						lowercase        =							{int(lowercase_						): v for k, v in idalabel.items()}
						lowercase        =							idalabel
						lowercase        =							{v: k for k, v in idalabel.items()}

						return config







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      Any=False						):
						for i in range(1 ,  6						):
												if F"""layer_{i}.""" in name:
																		lowercase        =							name.replace(F"""layer_{i}.""" ,  F"""encoder.layer.{i - 1}."""						)

						if "conv_1." in name:
												lowercase        =							name.replace("""conv_1.""" ,  """conv_stem."""						)
						if ".block." in name:
												lowercase        =							name.replace(""".block.""" ,  """."""						)

						if "exp_1x1" in name:
												lowercase        =							name.replace("""exp_1x1""" ,  """expand_1x1"""						)
						if "red_1x1" in name:
												lowercase        =							name.replace("""red_1x1""" ,  """reduce_1x1"""						)
						if ".local_rep.conv_3x3." in name:
												lowercase        =							name.replace(""".local_rep.conv_3x3.""" ,  """.conv_kxk."""						)
						if ".local_rep.conv_1x1." in name:
												lowercase        =							name.replace(""".local_rep.conv_1x1.""" ,  """.conv_1x1."""						)
						if ".norm." in name:
												lowercase        =							name.replace(""".norm.""" ,  """.normalization."""						)
						if ".conv." in name:
												lowercase        =							name.replace(""".conv.""" ,  """.convolution."""						)
						if ".conv_proj." in name:
												lowercase        =							name.replace(""".conv_proj.""" ,  """.conv_projection."""						)

						for i in range(0 ,  2						):
												for j in range(0 ,  4						):
																		if F""".{i}.{j}.""" in name:
																								lowercase        =							name.replace(F""".{i}.{j}.""" ,  F""".{i}.layer.{j}."""						)

						for i in range(2 ,  6						):
												for j in range(0 ,  4						):
																		if F""".{i}.{j}.""" in name:
																								lowercase        =							name.replace(F""".{i}.{j}.""" ,  F""".{i}."""						)
																								if "expand_1x1" in name:
																														lowercase        =							name.replace("""expand_1x1""" ,  """downsampling_layer.expand_1x1"""						)
																								if "conv_3x3" in name:
																														lowercase        =							name.replace("""conv_3x3""" ,  """downsampling_layer.conv_3x3"""						)
																								if "reduce_1x1" in name:
																														lowercase        =							name.replace("""reduce_1x1""" ,  """downsampling_layer.reduce_1x1"""						)

						for i in range(2 ,  5						):
												if F""".global_rep.{i}.weight""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.weight""" ,  """.layernorm.weight"""						)
												if F""".global_rep.{i}.bias""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.bias""" ,  """.layernorm.bias"""						)

						if ".global_rep." in name:
												lowercase        =							name.replace(""".global_rep.""" ,  """.transformer."""						)
						if ".pre_norm_mha.0." in name:
												lowercase        =							name.replace(""".pre_norm_mha.0.""" ,  """.layernorm_before."""						)
						if ".pre_norm_mha.1.out_proj." in name:
												lowercase        =							name.replace(""".pre_norm_mha.1.out_proj.""" ,  """.attention.output.dense."""						)
						if ".pre_norm_ffn.0." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.0.""" ,  """.layernorm_after."""						)
						if ".pre_norm_ffn.1." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.1.""" ,  """.intermediate.dense."""						)
						if ".pre_norm_ffn.4." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.4.""" ,  """.output.dense."""						)
						if ".transformer." in name:
												lowercase        =							name.replace(""".transformer.""" ,  """.transformer.layer."""						)

						if ".aspp_layer." in name:
												lowercase        =							name.replace(""".aspp_layer.""" ,  """."""						)
						if ".aspp_pool." in name:
												lowercase        =							name.replace(""".aspp_pool.""" ,  """."""						)
						if "seg_head." in name:
												lowercase        =							name.replace("""seg_head.""" ,  """segmentation_head."""						)
						if "segmentation_head.classifier.classifier." in name:
												lowercase        =							name.replace("""segmentation_head.classifier.classifier.""" ,  """segmentation_head.classifier."""						)

						if "classifier.fc." in name:
												lowercase        =							name.replace("""classifier.fc.""" ,  """classifier."""						)
						elif (not base_model) and ("segmentation_head." not in name):
												lowercase        =							"""mobilevit.""" + name

						return name







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      List[Any] ,  lowercase_    :      str=False						):
						if base_model:
												lowercase        =							""""""
						else:
												lowercase        =							"""mobilevit."""

						for key in orig_state_dict.copy().keys():
												lowercase        =							orig_state_dict.pop(lowercase_						)

												if key[:8] == "encoder.":
																		lowercase        =							key[8:]

												if "qkv" in key:
																		lowercase        =							key.split("""."""						)
																		lowercase        =							int(key_split[0][6:]						) - 1
																		lowercase        =							int(key_split[3]						)
																		lowercase        =							model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""						)
																		lowercase        =							layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		lowercase        =							(
																		    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
																		)
																		if "weight" in key:
																								lowercase        =							val[:dim, :]
																								lowercase        =							val[dim : dim * 2, :]
																								lowercase        =							val[-dim:, :]
																		else:
																								lowercase        =							val[:dim]
																								lowercase        =							val[dim : dim * 2]
																								lowercase        =							val[-dim:]
												else:
																		lowercase        =							val

						return orig_state_dict







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowercase        =							Image.open(requests.get(lowercase_ ,  stream=lowercase_						).raw						)
						return im







@torch.no_grad()
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      List[Any] ,  lowercase_    :      Any ,  lowercase_    :      List[str]=False						):
						lowercase        =							get_mobilevit_config(lowercase_						)

						# load original state_dict
						lowercase        =							torch.load(lowercase_ ,  map_location="""cpu"""						)

						# load 🤗 model
						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							MobileViTForSemanticSegmentation(lowercase_						).eval()
						else:
												lowercase        =							MobileViTForImageClassification(lowercase_						).eval()

						lowercase        =							convert_state_dict(lowercase_ ,  lowercase_						)
						model.load_state_dict(lowercase_						)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						lowercase        =							MobileViTImageProcessor(crop_size=config.image_size ,  size=config.image_size + 32						)
						lowercase        =							image_processor(images=prepare_img() ,  return_tensors="""pt"""						)
						lowercase        =							model(**lowercase_						)
						lowercase        =							outputs.logits

						if mobilevit_name.startswith("""deeplabv3_"""						):
												assert logits.shape == (1, 21, 32, 32)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																		        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																		        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																		        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																		        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																		    ]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

												assert torch.allclose(logits[0, :3, :3, :3] ,  lowercase_ ,  atol=1E-4						)
						else:
												assert logits.shape == (1, 1000)

												if mobilevit_name == "mobilevit_s":
																		lowercase        =							torch.tensor([-0.9_866, 0.2_392, -1.1_241]						)
												elif mobilevit_name == "mobilevit_xs":
																		lowercase        =							torch.tensor([-2.4_761, -0.9_399, -1.9_587]						)
												elif mobilevit_name == "mobilevit_xxs":
																		lowercase        =							torch.tensor([-1.9_364, -1.2_327, -0.4_653]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

												assert torch.allclose(logits[0, :3] ,  lowercase_ ,  atol=1E-4						)

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

						if push_to_hub:
												lowercase        =							{
												    """mobilevit_s""": """mobilevit-small""",
												    """mobilevit_xs""": """mobilevit-x-small""",
												    """mobilevit_xxs""": """mobilevit-xx-small""",
												    """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
												    """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
												    """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
												}

												print("""Pushing to the hub..."""						)
												lowercase        =							model_mapping[mobilevit_name]
												image_processor.push_to_hub(lowercase_ ,  organization="""apple"""						)
												model.push_to_hub(lowercase_ ,  organization="""apple"""						)


if __name__ == "__main__":
						lowercase_      :				List[str]   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--mobilevit_name''',
						    default='''mobilevit_s''',
						    type=str,
						    help=(
						        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
						        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt 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.'''
						)

						lowercase_      :				List[str]   					=    parser.parse_args()
						convert_movilevit_checkpoint(
						    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)


