


"""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__				:							Tuple						=     logging.get_logger(__name__)




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__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						   =				1000
										__SCREAMING_SNAKE_CASE						   =				"imagenet-1k-id2label.json"

					__SCREAMING_SNAKE_CASE						   =				"huggingface/label-files"
					__SCREAMING_SNAKE_CASE						   =				json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="dataset"   )  ,						"r"   )   )
					__SCREAMING_SNAKE_CASE						   =				{int(lowerCAmelCase_   ): 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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_=False   ):




					'''simple docstring'''


					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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False   ):




					'''simple docstring'''


					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(lowerCAmelCase_   )

										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    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				"http://images.cocodataset.org/val2017/000000039769.jpg"
					__SCREAMING_SNAKE_CASE						   =				Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )
					return im




@torch.no_grad()
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_mobilevit_config(lowerCAmelCase_   )

					# load original state_dict
					__SCREAMING_SNAKE_CASE						   =				torch.load(lowerCAmelCase_  ,						map_location="cpu"   )

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

					__SCREAMING_SNAKE_CASE						   =				convert_state_dict(lowerCAmelCase_  ,						lowerCAmelCase_   )
					model.load_state_dict(lowerCAmelCase_   )

					# 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(**lowerCAmelCase_   )
					__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(
															    [
															        [[6.2065, 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":
															__SCREAMING_SNAKE_CASE						   =				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":
															__SCREAMING_SNAKE_CASE						   =				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]  ,						lowerCAmelCase_  ,						atol=1E-4   )
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															__SCREAMING_SNAKE_CASE						   =				torch.tensor([-0.9866, 0.2392, -1.1241]   )
										elif mobilevit_name == "mobilevit_xs":
															__SCREAMING_SNAKE_CASE						   =				torch.tensor([-2.4761, -0.9399, -1.9587]   )
										elif mobilevit_name == "mobilevit_xxs":
															__SCREAMING_SNAKE_CASE						   =				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:
										__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(lowerCAmelCase_  ,						organization="apple"   )
										model.push_to_hub(lowerCAmelCase_  ,						organization="apple"   )


if __name__ == "__main__":
		a__				:							Dict						=     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__				:							Any						=     parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)







"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


