

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()
_UpperCamelCase 			=		logging.get_logger(__name__)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						List[str]					=					MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            __lowerCAmelCase					:						Optional[Any]					=					[1_4_4, 1_9_2, 2_4_0]
            __lowerCAmelCase					:						int					=					[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
      elif "mobilevit_xs" in mobilevit_name:
            __lowerCAmelCase					:						Any					=					[9_6, 1_2_0, 1_4_4]
            __lowerCAmelCase					:						Optional[Any]					=					[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
      elif "mobilevit_xxs" in mobilevit_name:
            __lowerCAmelCase					:						str					=					[6_4, 8_0, 9_6]
            __lowerCAmelCase					:						Tuple					=					[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
            __lowerCAmelCase					:						Optional[Any]					=					0.0_5
            __lowerCAmelCase					:						Tuple					=					2.0

      if mobilevit_name.startswith('''deeplabv3_'''						):
            __lowerCAmelCase					:						List[str]					=					5_1_2
            __lowerCAmelCase					:						List[str]					=					1_6
            __lowerCAmelCase					:						Union[str, Any]					=					2_1
            __lowerCAmelCase					:						Tuple					=					"""pascal-voc-id2label.json"""
      else:
            __lowerCAmelCase					:						int					=					1_0_0_0
            __lowerCAmelCase					:						Dict					=					"""imagenet-1k-id2label.json"""

      __lowerCAmelCase					:						Optional[int]					=					"""huggingface/label-files"""
      __lowerCAmelCase					:						List[Any]					=					json.load(open(hf_hub_download(UpperCAmelCase__   ,	UpperCAmelCase__   ,	repo_type='''dataset'''						)   ,	'''r'''						)						)
      __lowerCAmelCase					:						Optional[int]					=					{int(UpperCAmelCase__						): v for k, v in idalabel.items()}
      __lowerCAmelCase					:						Optional[int]					=					idalabel
      __lowerCAmelCase					:						Any					=					{v: k for k, v in idalabel.items()}

      return config







def 		_lowercase (     lowercase__   ,	lowercase__=False						):
      for i in range(1   ,	6						):
            if f"""layer_{i}.""" in name:
                  __lowerCAmelCase					:						Optional[Any]					=					name.replace(f"""layer_{i}."""   ,	f"""encoder.layer.{i - 1}."""						)

      if "conv_1." in name:
            __lowerCAmelCase					:						Optional[Any]					=					name.replace('''conv_1.'''   ,	'''conv_stem.'''						)
      if ".block." in name:
            __lowerCAmelCase					:						Tuple					=					name.replace('''.block.'''   ,	'''.'''						)

      if "exp_1x1" in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''exp_1x1'''   ,	'''expand_1x1'''						)
      if "red_1x1" in name:
            __lowerCAmelCase					:						int					=					name.replace('''red_1x1'''   ,	'''reduce_1x1'''						)
      if ".local_rep.conv_3x3." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.local_rep.conv_3x3.'''   ,	'''.conv_kxk.'''						)
      if ".local_rep.conv_1x1." in name:
            __lowerCAmelCase					:						List[str]					=					name.replace('''.local_rep.conv_1x1.'''   ,	'''.conv_1x1.'''						)
      if ".norm." in name:
            __lowerCAmelCase					:						List[Any]					=					name.replace('''.norm.'''   ,	'''.normalization.'''						)
      if ".conv." in name:
            __lowerCAmelCase					:						Optional[int]					=					name.replace('''.conv.'''   ,	'''.convolution.'''						)
      if ".conv_proj." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.conv_proj.'''   ,	'''.conv_projection.'''						)

      for i in range(0   ,	2						):
            for j in range(0   ,	4						):
                  if f""".{i}.{j}.""" in name:
                        __lowerCAmelCase					:						Tuple					=					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:
                        __lowerCAmelCase					:						Union[str, Any]					=					name.replace(f""".{i}.{j}."""   ,	f""".{i}."""						)
                        if "expand_1x1" in name:
                              __lowerCAmelCase					:						int					=					name.replace('''expand_1x1'''   ,	'''downsampling_layer.expand_1x1'''						)
                        if "conv_3x3" in name:
                              __lowerCAmelCase					:						Optional[int]					=					name.replace('''conv_3x3'''   ,	'''downsampling_layer.conv_3x3'''						)
                        if "reduce_1x1" in name:
                              __lowerCAmelCase					:						str					=					name.replace('''reduce_1x1'''   ,	'''downsampling_layer.reduce_1x1'''						)

      for i in range(2   ,	5						):
            if f""".global_rep.{i}.weight""" in name:
                  __lowerCAmelCase					:						Dict					=					name.replace(f""".global_rep.{i}.weight"""   ,	'''.layernorm.weight'''						)
            if f""".global_rep.{i}.bias""" in name:
                  __lowerCAmelCase					:						Tuple					=					name.replace(f""".global_rep.{i}.bias"""   ,	'''.layernorm.bias'''						)

      if ".global_rep." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.global_rep.'''   ,	'''.transformer.'''						)
      if ".pre_norm_mha.0." in name:
            __lowerCAmelCase					:						Tuple					=					name.replace('''.pre_norm_mha.0.'''   ,	'''.layernorm_before.'''						)
      if ".pre_norm_mha.1.out_proj." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.pre_norm_mha.1.out_proj.'''   ,	'''.attention.output.dense.'''						)
      if ".pre_norm_ffn.0." in name:
            __lowerCAmelCase					:						Optional[int]					=					name.replace('''.pre_norm_ffn.0.'''   ,	'''.layernorm_after.'''						)
      if ".pre_norm_ffn.1." in name:
            __lowerCAmelCase					:						List[Any]					=					name.replace('''.pre_norm_ffn.1.'''   ,	'''.intermediate.dense.'''						)
      if ".pre_norm_ffn.4." in name:
            __lowerCAmelCase					:						int					=					name.replace('''.pre_norm_ffn.4.'''   ,	'''.output.dense.'''						)
      if ".transformer." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.transformer.'''   ,	'''.transformer.layer.'''						)

      if ".aspp_layer." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''.aspp_layer.'''   ,	'''.'''						)
      if ".aspp_pool." in name:
            __lowerCAmelCase					:						List[Any]					=					name.replace('''.aspp_pool.'''   ,	'''.'''						)
      if "seg_head." in name:
            __lowerCAmelCase					:						str					=					name.replace('''seg_head.'''   ,	'''segmentation_head.'''						)
      if "segmentation_head.classifier.classifier." in name:
            __lowerCAmelCase					:						str					=					name.replace('''segmentation_head.classifier.classifier.'''   ,	'''segmentation_head.classifier.'''						)

      if "classifier.fc." in name:
            __lowerCAmelCase					:						Union[str, Any]					=					name.replace('''classifier.fc.'''   ,	'''classifier.'''						)
      elif (not base_model) and ("segmentation_head." not in name):
            __lowerCAmelCase					:						Optional[int]					=					"""mobilevit.""" + name

      return name







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=False						):
      if base_model:
            __lowerCAmelCase					:						List[Any]					=					""""""
      else:
            __lowerCAmelCase					:						List[Any]					=					"""mobilevit."""

      for key in orig_state_dict.copy().keys():
            __lowerCAmelCase					:						Any					=					orig_state_dict.pop(UpperCAmelCase__						)

            if key[:8] == "encoder.":
                  __lowerCAmelCase					:						Any					=					key[8:]

            if "qkv" in key:
                  __lowerCAmelCase					:						Tuple					=					key.split('''.'''						)
                  __lowerCAmelCase					:						Any					=					int(key_split[0][6:]						) - 1
                  __lowerCAmelCase					:						Optional[int]					=					int(key_split[3]						)
                  __lowerCAmelCase					:						Any					=					model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}"""						)
                  __lowerCAmelCase					:						int					=					layer.transformer.layer[transformer_num].attention.attention.all_head_size
                  __lowerCAmelCase					:						str					=					(
                      f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                  )
                  if "weight" in key:
                        __lowerCAmelCase					:						Optional[Any]					=					val[:dim, :]
                        __lowerCAmelCase					:						int					=					val[dim : dim * 2, :]
                        __lowerCAmelCase					:						Optional[int]					=					val[-dim:, :]
                  else:
                        __lowerCAmelCase					:						Any					=					val[:dim]
                        __lowerCAmelCase					:						Dict					=					val[dim : dim * 2]
                        __lowerCAmelCase					:						Dict					=					val[-dim:]
            else:
                  __lowerCAmelCase					:						Optional[int]					=					val

      return orig_state_dict







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







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase					:						int					=					get_mobilevit_config(UpperCAmelCase__						)

      # load original state_dict
      __lowerCAmelCase					:						List[Any]					=					torch.load(UpperCAmelCase__   ,	map_location='''cpu'''						)

      # load 🤗 model
      if mobilevit_name.startswith('''deeplabv3_'''						):
            __lowerCAmelCase					:						str					=					MobileViTForSemanticSegmentation(UpperCAmelCase__						).eval()
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					MobileViTForImageClassification(UpperCAmelCase__						).eval()

      __lowerCAmelCase					:						List[Any]					=					convert_state_dict(UpperCAmelCase__   ,	UpperCAmelCase__						)
      model.load_state_dict(UpperCAmelCase__						)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      __lowerCAmelCase					:						int					=					MobileViTImageProcessor(crop_size=config.image_size   ,	size=config.image_size + 3_2						)
      __lowerCAmelCase					:						Dict					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''						)
      __lowerCAmelCase					:						int					=					model(**UpperCAmelCase__						)
      __lowerCAmelCase					:						Tuple					=					outputs.logits

      if mobilevit_name.startswith('''deeplabv3_'''						):
            assert logits.shape == (1, 2_1, 3_2, 3_2)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  __lowerCAmelCase					:						Optional[int]					=					torch.tensor(
                      [
                          [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                          [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                          [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                      ]						)
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  __lowerCAmelCase					:						List[str]					=					torch.tensor(
                      [
                          [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                          [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                          [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                      ]						)
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  __lowerCAmelCase					:						Dict					=					torch.tensor(
                      [
                          [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                          [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                          [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                      ]						)
            else:
                  raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""						)

            assert torch.allclose(logits[0, :3, :3, :3]   ,	UpperCAmelCase__   ,	atol=1E-4						)
      else:
            assert logits.shape == (1, 1_0_0_0)

            if mobilevit_name == "mobilevit_s":
                  __lowerCAmelCase					:						Optional[Any]					=					torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]						)
            elif mobilevit_name == "mobilevit_xs":
                  __lowerCAmelCase					:						str					=					torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]						)
            elif mobilevit_name == "mobilevit_xxs":
                  __lowerCAmelCase					:						Union[str, Any]					=					torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]						)
            else:
                  raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""						)

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

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

      if push_to_hub:
            __lowerCAmelCase					:						List[Any]					=					{
                """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...'''						)
            __lowerCAmelCase					:						int					=					model_mapping[mobilevit_name]
            image_processor.push_to_hub(UpperCAmelCase__   ,	organization='''apple'''						)
            model.push_to_hub(UpperCAmelCase__   ,	organization='''apple'''						)


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

 _UpperCamelCase 			=		parser.parse_args()
 convert_movilevit_checkpoint(
     args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )



'''simple docstring'''




from itertools import product




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      list[int]:
							lowercase_					:			List[Any]										=       sides_number
							lowercase_					:			Dict										=       max_face_number * dice_number
							lowercase_					:			List[str]										=       [0] * (max_total + 1)

							lowercase_					:			Union[str, Any]										=       1
							lowercase_					:			Dict										=       range(UpperCAmelCase__      ,      max_face_number + 1 )
							for dice_numbers in product(UpperCAmelCase__      ,      repeat=UpperCAmelCase__ ):
														lowercase_					:			Any										=       sum(UpperCAmelCase__ )
														totals_frequencies[total] += 1

							return totals_frequencies




def 							lowerCamelCase	(      )			->      float:
							lowercase_					:			Optional[Any]										=       total_frequency_distribution(
							    sides_number=4      ,      dice_number=9 )
							lowercase_					:			List[str]										=       total_frequency_distribution(
							    sides_number=6      ,      dice_number=6 )

							lowercase_					:			Union[str, Any]										=       0
							lowercase_					:			Tuple										=       9
							lowercase_					:			Optional[int]										=       4 * 9
							lowercase_					:			List[Any]										=       6
							for peter_total in range(UpperCAmelCase__      ,      max_peter_total + 1 ):
														peter_wins_count += peter_totals_frequencies[peter_total] * sum(
														    colin_totals_frequencies[min_colin_total:peter_total] )

							lowercase_					:			str										=       (4**9) * (6**6)
							lowercase_					:			List[Any]										=       peter_wins_count / total_games_number

							lowercase_					:			Dict										=       round(UpperCAmelCase__      ,      ndigits=7 )

							return rounded_peter_win_probability


if __name__ == "__main__":
						print(f"""{solution() = }""")





