"""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()
_A        =     logging.get_logger(__name__)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Union[str, Any]:
				SCREAMING_SNAKE_CASE__			=					MobileViTConfig()

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

				if mobilevit_name.startswith("deeplabv3_"							):
								SCREAMING_SNAKE_CASE__			=					512
								SCREAMING_SNAKE_CASE__			=					16
								SCREAMING_SNAKE_CASE__			=					21
								SCREAMING_SNAKE_CASE__			=					"pascal-voc-id2label.json"
				else:
								SCREAMING_SNAKE_CASE__			=					1_000
								SCREAMING_SNAKE_CASE__			=					"imagenet-1k-id2label.json"

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

				return config




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase=False							)				-> Dict:
				for i in range(1	,       6							):
								if F"""layer_{i}.""" in name:
												SCREAMING_SNAKE_CASE__			=					name.replace(F"""layer_{i}."""	,       F"""encoder.layer.{i - 1}."""							)

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

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

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

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

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

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

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

				return name




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=False							)				-> str:
				if base_model:
								SCREAMING_SNAKE_CASE__			=					""
				else:
								SCREAMING_SNAKE_CASE__			=					"mobilevit."

				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE__			=					orig_state_dict.pop(__snake_case							)

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

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

				return orig_state_dict




def     SCREAMING_SNAKE_CASE       (	)				-> Tuple:
				SCREAMING_SNAKE_CASE__			=					"http://images.cocodataset.org/val2017/000000039769.jpg"
				SCREAMING_SNAKE_CASE__			=					Image.open(requests.get(__snake_case	,       stream=__snake_case							).raw							)
				return im




@torch.no_grad()
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=False							)				-> str:
				SCREAMING_SNAKE_CASE__			=					get_mobilevit_config(__snake_case							)

				# load original state_dict
				SCREAMING_SNAKE_CASE__			=					torch.load(__snake_case	,       map_location="cpu"							)

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

				SCREAMING_SNAKE_CASE__			=					convert_state_dict(__snake_case	,       __snake_case							)
				model.load_state_dict(__snake_case							)

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

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

								if mobilevit_name == "deeplabv3_mobilevit_s":
												SCREAMING_SNAKE_CASE__			=					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":
												SCREAMING_SNAKE_CASE__			=					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":
												SCREAMING_SNAKE_CASE__			=					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]	,       __snake_case	,       atol=1e-4							)
				else:
								assert logits.shape == (1, 1_000)

								if mobilevit_name == "mobilevit_s":
												SCREAMING_SNAKE_CASE__			=					torch.tensor([-0.9_866, 0.2_392, -1.1_241]							)
								elif mobilevit_name == "mobilevit_xs":
												SCREAMING_SNAKE_CASE__			=					torch.tensor([-2.4_761, -0.9_399, -1.9_587]							)
								elif mobilevit_name == "mobilevit_xxs":
												SCREAMING_SNAKE_CASE__			=					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]	,       __snake_case	,       atol=1e-4							)

				Path(__snake_case							).mkdir(exist_ok=__snake_case							)
				print(F"""Saving model {mobilevit_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:
								SCREAMING_SNAKE_CASE__			=					{
								    "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..."							)
								SCREAMING_SNAKE_CASE__			=					model_mapping[mobilevit_name]
								image_processor.push_to_hub(__snake_case	,       organization="apple"							)
								model.push_to_hub(__snake_case	,       organization="apple"							)


if __name__ == "__main__":
						_A        =     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.'
						)

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)