






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__    :   Any    		=       logging.get_logger(__name__)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Union[str, Any]:


	"""simple docstring"""



	a												=      MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		a												=      [1_4_4, 1_9_2, 2_4_0]
		a												=      [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
	elif "mobilevit_xs" in mobilevit_name:
		a												=      [9_6, 1_2_0, 1_4_4]
		a												=      [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
	elif "mobilevit_xxs" in mobilevit_name:
		a												=      [6_4, 8_0, 9_6]
		a												=      [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
		a												=      0.05
		a												=      2.0

	if mobilevit_name.startswith('''deeplabv3_'''	):
		a												=      5_1_2
		a												=      1_6
		a												=      2_1
		a												=      '''pascal-voc-id2label.json'''
	else:
		a												=      1_0_0_0
		a												=      '''imagenet-1k-id2label.json'''

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

	return config





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_=False	) ->	Optional[int]:


	"""simple docstring"""



	for i in range(1,	6	):
		if f"""layer_{i}.""" in name:
			a												=      name.replace(f"""layer_{i}.""",	f"""encoder.layer.{i - 1}."""	)

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

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

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

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

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

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

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

	return name





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_=False	) ->	Optional[int]:


	"""simple docstring"""



	if base_model:
		a												=      ''''''
	else:
		a												=      '''mobilevit.'''

	for key in orig_state_dict.copy().keys():
		a												=      orig_state_dict.pop(lowerCamelCase__	)

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

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

	return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    ( ) ->	str:


	"""simple docstring"""



	a												=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	a												=      Image.open(requests.get(lowerCamelCase__,	stream=lowerCamelCase__	).raw	)
	return im





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_,	snake_case_=False	) ->	List[str]:


	"""simple docstring"""



	a												=      get_mobilevit_config(lowerCamelCase__	)

	# load original state_dict
	a												=      torch.load(lowerCamelCase__,	map_location='''cpu'''	)

	# load 🤗 model
	if mobilevit_name.startswith('''deeplabv3_'''	):
		a												=      MobileViTForSemanticSegmentation(lowerCamelCase__	).eval()
	else:
		a												=      MobileViTForImageClassification(lowerCamelCase__	).eval()

	a												=      convert_state_dict(lowerCamelCase__,	lowerCamelCase__	)
	model.load_state_dict(lowerCamelCase__	)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	a												=      MobileViTImageProcessor(crop_size=config.image_size,	size=config.image_size + 3_2	)
	a												=      image_processor(images=prepare_img(),	return_tensors='''pt'''	)
	a												=      model(**lowerCamelCase__	)
	a												=      outputs.logits

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			a												=      torch.tensor(
			    [
			        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
			        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
			        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
			    ]	)
		elif mobilevit_name == "deeplabv3_mobilevit_xs":
			a												=      torch.tensor(
			    [
			        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
			        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
			        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
			    ]	)
		elif mobilevit_name == "deeplabv3_mobilevit_xxs":
			a												=      torch.tensor(
			    [
			        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
			        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8624, -9.5964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
			        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
			    ]	)
		else:
			raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""	)

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

		if mobilevit_name == "mobilevit_s":
			a												=      torch.tensor([-0.9866, 0.2392, -1.1241]	)
		elif mobilevit_name == "mobilevit_xs":
			a												=      torch.tensor([-2.4761, -0.9399, -1.9587]	)
		elif mobilevit_name == "mobilevit_xxs":
			a												=      torch.tensor([-1.9364, -1.2327, -0.4653]	)
		else:
			raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""	)

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

	Path(lowerCamelCase__	).mkdir(exist_ok=lowerCamelCase__	)
	print(f"""Saving model {mobilevit_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:
		a												=      {
		    '''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...'''	)
		a												=      model_mapping[mobilevit_name]
		image_processor.push_to_hub(lowerCamelCase__,	organization='''apple'''	)
		model.push_to_hub(lowerCamelCase__,	organization='''apple'''	)


if __name__ == "__main__":
				UpperCamelCase__    :   Optional[Any]    		=       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__    :   Any    		=       parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)









import random
from typing import Any






def 	__lowerCamelCase						(							lowerCamelCase__		:						list      ):





			'''simple docstring'''




			for _ in range(len(lowerCamelCase__      )      ):
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase		,		lowerCamelCase    		=  data[b], data[a]
			return data


if __name__ == "__main__":
		UpperCAmelCase   :    str     =		[0, 1, 2, 3, 4, 5, 6, 7]
		UpperCAmelCase   :    Union[str, Any]     =		["python", "says", "hello", "!"]
		print("Fisher-Yates Shuffle:")
		print("List", integers, strings)
		print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

