



import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				Tuple		):

				"""simple docstring"""
				print("""Loading config file..."""		)

				def flatten_yaml_as_dict(__a     :				List[Any] ,		__a     :				Optional[int]="" ,		__a     :				Optional[int]="."		):
								a__				      =		[]
								for k, v in d.items():
												a__				      =		parent_key + sep + k if parent_key else k
												if isinstance(__a ,		collections.abc.MutableMapping		):
																items.extend(flatten_yaml_as_dict(__a ,		__a ,		sep=__a		).items()		)
												else:
																items.append((new_key, v)		)
								return dict(__a		)

				a__				      =		argparse.Namespace()
				with open(__a ,		"""r"""		) as yaml_file:
								try:
												a__				      =		yaml.load(__a ,		Loader=yaml.FullLoader		)

												a__				      =		flatten_yaml_as_dict(__a		)
												for k, v in flat_cfg.items():
																setattr(__a ,		__a ,		__a		)
								except yaml.YAMLError as exc:
												logger.error("""Error while loading config file: {}. Error message: {}""".format(__a ,		str(__a		)		)		)
				return config

def 			A_      (	__a     :				int ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		MobileViTVaConfig()

				a__				      =		False

				# dataset
				if task_name.startswith("""imagenet1k_"""		):
								a__				      =		1_000
								if int(task_name.strip().split("""_"""		)[-1]		) == 384:
												a__				      =		384
								else:
												a__				      =		256
								a__				      =		"""imagenet-1k-id2label.json"""
				elif task_name.startswith("""imagenet21k_to_1k_"""		):
								a__				      =		21_000
								if int(task_name.strip().split("""_"""		)[-1]		) == 384:
												a__				      =		384
								else:
												a__				      =		256
								a__				      =		"""imagenet-22k-id2label.json"""
				elif task_name.startswith("""ade20k_"""		):
								a__				      =		151
								a__				      =		512
								a__				      =		"""ade20k-id2label.json"""
								a__				      =		True
				elif task_name.startswith("""voc_"""		):
								a__				      =		21
								a__				      =		512
								a__				      =		"""pascal-voc-id2label.json"""
								a__				      =		True

				# orig_config
				a__				      =		load_orig_config_file(__a		)
				assert getattr(__a ,		"""model.classification.name""" ,		-1		) == "mobilevit_v2", "Invalid model"
				a__				      =		getattr(__a ,		"""model.classification.mitv2.width_multiplier""" ,		1.0		)
				assert (
				    getattr(__a ,		"""model.classification.mitv2.attn_norm_layer""" ,		-1		) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				a__				      =		getattr(__a ,		"""model.classification.activation.name""" ,		"""swish"""		)
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								a__				      =		getattr(__a ,		"""model.segmentation.output_stride""" ,		16		)
								if "_deeplabv3" in task_name:
												a__				      =		getattr(__a ,		"""model.segmentation.deeplabv3.aspp_rates""" ,		[12, 24, 36]		)
												a__				      =		getattr(__a ,		"""model.segmentation.deeplabv3.aspp_out_channels""" ,		512		)
												a__				      =		getattr(__a ,		"""model.segmentation.deeplabv3.aspp_dropout""" ,		0.1		)

    # id2label
				a__				      =		"""huggingface/label-files"""
				a__				      =		json.load(open(hf_hub_download(__a ,		__a ,		repo_type="""dataset"""		) ,		"""r"""		)		)
				a__				      =		{int(__a		): v for k, v in idalabel.items()}
				a__				      =		idalabel
				a__				      =		{v: k for k, v in idalabel.items()}

				return config

def 			A_      (	__a     :				str ,		__a     :				Tuple ,		__a     :				List[Any]		):

				"""simple docstring"""
				a__				      =		dct.pop(__a		)
				a__				      =		val

def 			A_      (	__a     :				Optional[int] ,		__a     :				Union[str, Any]=False		):

				"""simple docstring"""
				if base_model:
								a__				      =		""""""
				else:
								a__				      =		"""mobilevitv2."""

				a__				      =		[]
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												a__				      =		k[8:]
								else:
												a__				      =		k

								if ".block." in k:
												a__				      =		k_new.replace(""".block.""" ,		"""."""		)
								if ".conv." in k:
												a__				      =		k_new.replace(""".conv.""" ,		""".convolution."""		)
								if ".norm." in k:
												a__				      =		k_new.replace(""".norm.""" ,		""".normalization."""		)

								if "conv_1." in k:
												a__				      =		k_new.replace("""conv_1.""" ,		F'''{model_prefix}conv_stem.'''		)
								for i in [1, 2]:
												if F'''layer_{i}.''' in k:
																a__				      =		k_new.replace(F'''layer_{i}.''' ,		F'''{model_prefix}encoder.layer.{i-1}.layer.'''		)
								if ".exp_1x1." in k:
												a__				      =		k_new.replace(""".exp_1x1.""" ,		""".expand_1x1."""		)
								if ".red_1x1." in k:
												a__				      =		k_new.replace(""".red_1x1.""" ,		""".reduce_1x1."""		)

								for i in [3, 4, 5]:
												if F'''layer_{i}.0.''' in k:
																a__				      =		k_new.replace(F'''layer_{i}.0.''' ,		F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''		)
												if F'''layer_{i}.1.local_rep.0.''' in k:
																a__				      =		k_new.replace(F'''layer_{i}.1.local_rep.0.''' ,		F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''		)
												if F'''layer_{i}.1.local_rep.1.''' in k:
																a__				      =		k_new.replace(F'''layer_{i}.1.local_rep.1.''' ,		F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''		)

								for i in [3, 4, 5]:
												if i == 3:
																a__				      =		[0, 1]
												elif i == 4:
																a__				      =		[0, 1, 2, 3]
												elif i == 5:
																a__				      =		[0, 1, 2]

												for j in j_in:
																if F'''layer_{i}.1.global_rep.{j}.''' in k:
																				a__				      =		k_new.replace(
																				    F'''layer_{i}.1.global_rep.{j}.''' ,		F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''		)
												if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
																a__				      =		k_new.replace(
																    F'''layer_{i}.1.global_rep.{j+1}.''' ,		F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''		)

												if F'''layer_{i}.1.conv_proj.''' in k:
																a__				      =		k_new.replace(F'''layer_{i}.1.conv_proj.''' ,		F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''		)

								if "pre_norm_attn.0." in k:
												a__				      =		k_new.replace("""pre_norm_attn.0.""" ,		"""layernorm_before."""		)
								if "pre_norm_attn.1." in k:
												a__				      =		k_new.replace("""pre_norm_attn.1.""" ,		"""attention."""		)
								if "pre_norm_ffn.0." in k:
												a__				      =		k_new.replace("""pre_norm_ffn.0.""" ,		"""layernorm_after."""		)
								if "pre_norm_ffn.1." in k:
												a__				      =		k_new.replace("""pre_norm_ffn.1.""" ,		"""ffn.conv1."""		)
								if "pre_norm_ffn.3." in k:
												a__				      =		k_new.replace("""pre_norm_ffn.3.""" ,		"""ffn.conv2."""		)

								if "classifier.1." in k:
												a__				      =		k_new.replace("""classifier.1.""" ,		"""classifier."""		)

								if "seg_head." in k:
												a__				      =		k_new.replace("""seg_head.""" ,		"""segmentation_head."""		)
								if ".aspp_layer." in k:
												a__				      =		k_new.replace(""".aspp_layer.""" ,		"""."""		)
								if ".aspp_pool." in k:
												a__				      =		k_new.replace(""".aspp_pool.""" ,		"""."""		)

								rename_keys.append((k, k_new)		)
				return rename_keys

def 			A_      (	__a     :				Dict		):

				"""simple docstring"""
				a__				      =		[]
				for k in state_dict.keys():
								if k.startswith("""seg_head.aux_head."""		):
												keys_to_ignore.append(__a		)
				for k in keys_to_ignore:
								state_dict.pop(__a ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				a__				      =		Image.open(requests.get(__a ,		stream=__a		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Union[str, Any] ,		__a     :				str ,		__a     :				List[str] ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		get_mobilevitva_config(__a ,		__a		)

				# load original state_dict
				a__				      =		torch.load(__a ,		map_location="""cpu"""		)

				# load huggingface model
				if task_name.startswith("""ade20k_"""		) or task_name.startswith("""voc_"""		):
								a__				      =		MobileViTVaForSemanticSegmentation(__a		).eval()
								a__				      =		False
				else:
								a__				      =		MobileViTVaForImageClassification(__a		).eval()
								a__				      =		False

				# remove and rename some keys of load the original model
				a__				      =		checkpoint
				remove_unused_keys(__a		)
				a__				      =		create_rename_keys(__a ,		base_model=__a		)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(__a ,		__a ,		__a		)

				# load modified state_dict
				model.load_state_dict(__a		)

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

				# verify classification model
				if task_name.startswith("""imagenet"""		):
								a__				      =		outputs.logits
								a__				      =		logits.argmax(-1		).item()
								print("""Predicted class:""" ,		model.config.idalabel[predicted_class_idx]		)
								if task_name.startswith("""imagenet1k_256"""		) and config.width_multiplier == 1.0:
												# expected_logits for base variant
												a__				      =		torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]		)
												assert torch.allclose(logits[0, :3] ,		__a ,		atol=1e-4		)

				Path(__a		).mkdir(exist_ok=__a		)
				print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							 =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--task""",
	    default="""imagenet1k_256""",
	    type=str,
	    help=(
	        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
	        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
	    ),
	    choices=[
	        """imagenet1k_256""",
	        """imagenet1k_384""",
	        """imagenet21k_to_1k_256""",
	        """imagenet21k_to_1k_384""",
	        """ade20k_deeplabv3""",
	        """voc_deeplabv3""",
	    ],
	)

	parser.add_argument(
	    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
	)
	parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)

	UpperCAmelCase							 =					parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@register_to_config
							def __init__(							self  ,  a_ = 128  ,  a_ = 256  ,  a_ = 2_000.0  ,  a_ = 768  ,  a_ = 12  ,  a_ = 12  ,  a_ = 64  ,  a_ = 2_048  ,  a_ = 0.1  ,  ):
											super().__init__()

											a__				      =		nn.Sequential(
											    nn.Linear(a_  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  nn.Linear(d_model * 4  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  )

											a__				      =		nn.Embedding(a_  ,  a_		)
											a__				      =		False

											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)

											a__				      =		nn.Dropout(p=a_		)

											a__				      =		nn.ModuleList()
											for lyr_num in range(a_		):
															# FiLM conditional T5 decoder
															a__				      =		DecoderLayer(d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
															self.decoders.append(a_		)

											a__				      =		TaLayerNorm(a_		)

											a__				      =		nn.Dropout(p=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		torch.mul(query_input.unsqueeze(-1		)  ,  key_input.unsqueeze(-2		)		)
											return mask.unsqueeze(-3		)







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__      ,			a__      ,			a__				      =		decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

											# decoder_noise_time is in [0, 1), so rescale to expected timing range.
											a__				      =		get_timestep_embedding(
											    decoder_noise_time * self.config.max_decoder_noise_time  ,  embedding_dim=self.config.d_model  ,  max_period=self.config.max_decoder_noise_time  ,  ).to(dtype=self.dtype		)

											a__				      =		self.conditioning_emb(a_		).unsqueeze(1		)

											assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

											a__				      =		decoder_input_tokens.shape[1]

											# If we want to use relative positions for audio context, we can just offset
											# this sequence by the length of encodings_and_masks.
											a__				      =		torch.broadcast_to(
											    torch.arange(a_  ,  device=decoder_input_tokens.device		)  ,  (batch, seq_length)  ,  )

											a__				      =		self.position_encoding(a_		)

											a__				      =		self.continuous_inputs_projection(a_		)
											inputs += position_encodings
											a__				      =		self.dropout(a_		)

											# decoder: No padding present.
											a__				      =		torch.ones(
											    decoder_input_tokens.shape[:2]  ,  device=decoder_input_tokens.device  ,  dtype=inputs.dtype		)

											# Translate encoding masks to encoder-decoder masks.
											a__				      =		[(x, self.encoder_decoder_mask(a_  ,  a_		)) for x, y in encodings_and_masks]

											# cross attend style: concat encodings
											a__				      =		torch.cat([x[0] for x in encodings_and_encdec_masks]  ,  dim=1		)
											a__				      =		torch.cat([x[1] for x in encodings_and_encdec_masks]  ,  dim=-1		)

											for lyr in self.decoders:
															a__				      =		lyr(
															    a_  ,  conditioning_emb=a_  ,  encoder_hidden_states=a_  ,  encoder_attention_mask=a_  ,  )[0]

											a__				      =		self.decoder_norm(a_		)
											a__				      =		self.post_dropout(a_		)

											a__				      =		self.spec_out(a_		)
											return spec_out




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.ModuleList()

											# cond self attention: layer 0
											self.layer.append(
											    TaLayerSelfAttentionCond(d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  dropout_rate=a_		)		)

											# cross attention: layer 1
											self.layer.append(
											    TaLayerCrossAttention(
											        d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  dropout_rate=a_  ,  layer_norm_epsilon=a_  ,  )		)

											# Film Cond MLP + dropout: last layer
											self.layer.append(
											    TaLayerFFCond(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_  ,  layer_norm_epsilon=a_		)		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer[0](
											    a_  ,  conditioning_emb=a_  ,  attention_mask=a_  ,  )

											if encoder_hidden_states is not None:
															a__				      =		torch.where(encoder_attention_mask > 0  ,  0  ,  -1E1_0		).to(
															    encoder_hidden_states.dtype		)

															a__				      =		self.layer[1](
															    a_  ,  key_value_states=a_  ,  attention_mask=a_  ,  )

											# Apply Film Conditional Feed Forward layer
											a__				      =		self.layer[-1](a_  ,  a_		)

											return (hidden_states,)




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaLayerNorm(a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											# pre_self_attention_layer_norm
											a__				      =		self.layer_norm(a_		)

											if conditioning_emb is not None:
															a__				      =		self.FiLMLayer(a_  ,  a_		)

											# Self-attention block
											a__				      =		self.attention(a_		)

											a__				      =		hidden_states + self.dropout(a_		)

											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer_norm(a_		)
											a__				      =		self.attention(
											    a_  ,  encoder_hidden_states=a_  ,  attention_mask=attention_mask.squeeze(1		)  ,  )
											a__				      =		hidden_states + self.dropout(a_		)
											return layer_output




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaDenseGatedActDense(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None		):
											a__				      =		self.layer_norm(a_		)
											if conditioning_emb is not None:
															a__				      =		self.film(a_  ,  a_		)

											a__				      =		self.DenseReluDense(a_		)
											a__				      =		hidden_states + self.dropout(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Dropout(a_		)
											a__				      =		NewGELUActivation()







							def _a      (							self  ,  a_		):
											a__				      =		self.act(self.wi_a(a_		)		)
											a__				      =		self.wi_a(a_		)
											a__				      =		hidden_gelu * hidden_linear
											a__				      =		self.dropout(a_		)

											a__				      =		self.wo(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.Parameter(torch.ones(a_		)		)
											a__				      =		eps







							def _a      (							self  ,  a_		):
											# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
											# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
											# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
											# half-precision inputs is done in fp32

											a__				      =		hidden_states.to(torch.floataa		).pow(2		).mean(-1  ,  keepdim=a_		)
											a__				      =		hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

											# convert into half-precision if necessary
											if self.weight.dtype in [torch.floataa, torch.bfloataa]:
															a__				      =		hidden_states.to(self.weight.dtype		)

											return self.weight * hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def _a      (							self  ,  a_		):
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044_715 * torch.pow(a_  ,  3.0		))		))




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  out_features * 2  ,  bias=a_		)







							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.scale_bias(a_		)
											a__      ,			a__				      =		torch.chunk(a_  ,  2  ,  -1		)
											a__				      =		x * (1 + scale) + shift
											return x

