



'''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   :			Any					  =    logging.get_logger(__name__)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							UpperCAmelCase     :		Dict							     =		MobileViTConfig()

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

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

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

							return config



def       __lowerCamelCase  (					_lowercase	,				_lowercase=False		)      ->			Optional[Any]:


							for i in range(1	,				6		):
														if F'''layer_{i}.''' in name:
																					UpperCAmelCase     :		List[str]							     =		name.replace(F'''layer_{i}.'''	,				F'''encoder.layer.{i - 1}.'''		)

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

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

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

							for i in range(2	,				5		):
														if F'''.global_rep.{i}.weight''' in name:
																					UpperCAmelCase     :		str							     =		name.replace(F'''.global_rep.{i}.weight'''	,				""".layernorm.weight"""		)
														if F'''.global_rep.{i}.bias''' in name:
																					UpperCAmelCase     :		List[str]							     =		name.replace(F'''.global_rep.{i}.bias'''	,				""".layernorm.bias"""		)

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

							if ".aspp_layer." in name:
														UpperCAmelCase     :		Optional[Any]							     =		name.replace(""".aspp_layer."""	,				"""."""		)
							if ".aspp_pool." in name:
														UpperCAmelCase     :		str							     =		name.replace(""".aspp_pool."""	,				"""."""		)
							if "seg_head." in name:
														UpperCAmelCase     :		int							     =		name.replace("""seg_head."""	,				"""segmentation_head."""		)
							if "segmentation_head.classifier.classifier." in name:
														UpperCAmelCase     :		Union[str, Any]							     =		name.replace("""segmentation_head.classifier.classifier."""	,				"""segmentation_head.classifier."""		)

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

							return name



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=False		)      ->			Tuple:


							if base_model:
														UpperCAmelCase     :		List[str]							     =		""""""
							else:
														UpperCAmelCase     :		List[Any]							     =		"""mobilevit."""

							for key in orig_state_dict.copy().keys():
														UpperCAmelCase     :		Optional[int]							     =		orig_state_dict.pop(UpperCamelCase_		)

														if key[:8] == "encoder.":
																					UpperCAmelCase     :		Union[str, Any]							     =		key[8:]

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

							return orig_state_dict



def       __lowerCamelCase  (					)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Any							     =		Image.open(requests.get(UpperCamelCase_	,				stream=UpperCamelCase_		).raw		)
							return im



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase=False		)      ->			str:
							UpperCAmelCase     :		str							     =		get_mobilevit_config(UpperCamelCase_		)

							# load original state_dict
							UpperCAmelCase     :		int							     =		torch.load(UpperCamelCase_	,				map_location="""cpu"""		)

							# load 🤗 model
							if mobilevit_name.startswith("""deeplabv3_"""		):
														UpperCAmelCase     :		int							     =		MobileViTForSemanticSegmentation(UpperCamelCase_		).eval()
							else:
														UpperCAmelCase     :		int							     =		MobileViTForImageClassification(UpperCamelCase_		).eval()

							UpperCAmelCase     :		int							     =		convert_state_dict(UpperCamelCase_	,				UpperCamelCase_		)
							model.load_state_dict(UpperCamelCase_		)

							# Check outputs on an image, prepared by MobileViTImageProcessor
							UpperCAmelCase     :		Optional[Any]							     =		MobileViTImageProcessor(crop_size=config.image_size	,				size=config.image_size + 3_2		)
							UpperCAmelCase     :		List[Any]							     =		image_processor(images=prepare_img()	,				return_tensors="""pt"""		)
							UpperCAmelCase     :		Tuple							     =		model(**UpperCamelCase_		)
							UpperCAmelCase     :		Optional[Any]							     =		outputs.logits

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					UpperCAmelCase     :		str							     =		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":
																					UpperCAmelCase     :		int							     =		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":
																					UpperCAmelCase     :		int							     =		torch.tensor(
																					    [
																					        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
																					        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
																					        [[-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]	,				UpperCamelCase_	,				atol=1e-4		)
							else:
														assert logits.shape == (1, 1_0_0_0)

														if mobilevit_name == "mobilevit_s":
																					UpperCAmelCase     :		Optional[int]							     =		torch.tensor([-0.9866, 0.2392, -1.1241]		)
														elif mobilevit_name == "mobilevit_xs":
																					UpperCAmelCase     :		Tuple							     =		torch.tensor([-2.4761, -0.9399, -1.9587]		)
														elif mobilevit_name == "mobilevit_xxs":
																					UpperCAmelCase     :		str							     =		torch.tensor([-1.9364, -1.2327, -0.4653]		)
														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:
														UpperCAmelCase     :		Optional[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..."""		)
														UpperCAmelCase     :		Union[str, Any]							     =		model_mapping[mobilevit_name]
														image_processor.push_to_hub(UpperCamelCase_	,				organization="""apple"""		)
														model.push_to_hub(UpperCamelCase_	,				organization="""apple"""		)


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

							a   :			Optional[int]					  =    parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)




"""simple docstring"""





from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a       =							logging.get_logger(__name__)

_a       =							{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	"time_series_transformer"
 __UpperCAmelCase	:					Any   		=	{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(			self							:			int,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			str = "student_t",					UpperCAmelCase__							:			str = "nll",					UpperCAmelCase__							:			int = 1,					UpperCAmelCase__							:			List[int] = [1, 2, 3, 4, 5, 6, 7],					UpperCAmelCase__							:			Optional[Union[str, bool]] = "mean",					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			str = "gelu",					UpperCAmelCase__							:			int = 6_4,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			int = 1_0_0,					UpperCAmelCase__							:			float = 0.02,					UpperCAmelCase__							:			Any=True,					**UpperCAmelCase__							:			List[str],					):
    # time series specific configuration
    __lowercase								=						prediction_length
    __lowercase								=						context_length or prediction_length
    __lowercase								=						distribution_output
    __lowercase								=						loss
    __lowercase								=						input_size
    __lowercase								=						num_time_features
    __lowercase								=						lags_sequence
    __lowercase								=						scaling
    __lowercase								=						num_dynamic_real_features
    __lowercase								=						num_static_real_features
    __lowercase								=						num_static_categorical_features
    if cardinality and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The cardinality should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						cardinality
    else:
       __lowercase								=						[0]
    if embedding_dimension and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The embedding dimension should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						embedding_dimension
    else:
       __lowercase								=						[min(5_0,					(cat + 1) // 2	) for cat in self.cardinality]
    __lowercase								=						num_parallel_samples

    # Transformer architecture configuration
    __lowercase								=						input_size * len(UpperCAmelCase__	) + self._number_of_features
    __lowercase								=						d_model
    __lowercase								=						encoder_attention_heads
    __lowercase								=						decoder_attention_heads
    __lowercase								=						encoder_ffn_dim
    __lowercase								=						decoder_ffn_dim
    __lowercase								=						encoder_layers
    __lowercase								=						decoder_layers

    __lowercase								=						dropout
    __lowercase								=						attention_dropout
    __lowercase								=						activation_dropout
    __lowercase								=						encoder_layerdrop
    __lowercase								=						decoder_layerdrop

    __lowercase								=						activation_function
    __lowercase								=						init_std

    __lowercase								=						use_cache

    super().__init__(is_encoder_decoder=UpperCAmelCase__,					**UpperCAmelCase__	)



 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    return (
        sum(self.embedding_dimension	)
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )




