
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()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	MobileViTConfig()

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

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

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

		return config


def 							__A(lowerCAmelCase						,  lowerCAmelCase=False )		->				List[Any]:

		"""simple docstring"""






		for i in range(1						,  6 ):
				if F'layer_{i}.' in name:
						_UpperCamelCase						    =	name.replace(F'layer_{i}.'						,  F'encoder.layer.{i - 1}.' )

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

		if "exp_1x1" in name:
				_UpperCamelCase						    =	name.replace("""exp_1x1"""						,  """expand_1x1""" )
		if "red_1x1" in name:
				_UpperCamelCase						    =	name.replace("""red_1x1"""						,  """reduce_1x1""" )
		if ".local_rep.conv_3x3." in name:
				_UpperCamelCase						    =	name.replace(""".local_rep.conv_3x3."""						,  """.conv_kxk.""" )
		if ".local_rep.conv_1x1." in name:
				_UpperCamelCase						    =	name.replace(""".local_rep.conv_1x1."""						,  """.conv_1x1.""" )
		if ".norm." in name:
				_UpperCamelCase						    =	name.replace(""".norm."""						,  """.normalization.""" )
		if ".conv." in name:
				_UpperCamelCase						    =	name.replace(""".conv."""						,  """.convolution.""" )
		if ".conv_proj." in name:
				_UpperCamelCase						    =	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						    =	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						    =	name.replace(F'.{i}.{j}.'						,  F'.{i}.' )
								if "expand_1x1" in name:
										_UpperCamelCase						    =	name.replace("""expand_1x1"""						,  """downsampling_layer.expand_1x1""" )
								if "conv_3x3" in name:
										_UpperCamelCase						    =	name.replace("""conv_3x3"""						,  """downsampling_layer.conv_3x3""" )
								if "reduce_1x1" in name:
										_UpperCamelCase						    =	name.replace("""reduce_1x1"""						,  """downsampling_layer.reduce_1x1""" )

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

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

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

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

		return name


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False )		->				str:

		"""simple docstring"""






		if base_model:
				_UpperCamelCase						    =	""""""
		else:
				_UpperCamelCase						    =	"""mobilevit."""

		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

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

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

		return orig_state_dict


def 							__A()		->				Dict:

		"""simple docstring"""






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


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	get_mobilevit_config(lowerCAmelCase )

		# load original state_dict
		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )

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

		_UpperCamelCase						    =	convert_state_dict(lowerCAmelCase						,  lowerCAmelCase )
		model.load_state_dict(lowerCAmelCase )

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

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

				if mobilevit_name == "deeplabv3_mobilevit_s":
						_UpperCamelCase						    =	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						    =	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						    =	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, 1_0_0_0)

				if mobilevit_name == "mobilevit_s":
						_UpperCamelCase						    =	torch.tensor([-0.9866, 0.2392, -1.1241] )
				elif mobilevit_name == "mobilevit_xs":
						_UpperCamelCase						    =	torch.tensor([-2.4761, -0.9399, -1.9587] )
				elif mobilevit_name == "mobilevit_xxs":
						_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	{
				    """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						    =	model_mapping[mobilevit_name]
				image_processor.push_to_hub(lowerCAmelCase						,  organization="""apple""" )
				model.push_to_hub(lowerCAmelCase						,  organization="""apple""" )


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

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





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCamelCase__	=							logging.get_logger(__name__)  # pylint: disable=invalid-name







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								super().__init__()
								self.register_modules(unet=a       ,							scheduler=a				)







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

								if audio_length_in_s is None:
										_UpperCamelCase						    =	self.unet.config.sample_size / self.unet.config.sample_rate

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	2 ** len(self.unet.up_blocks				)
								if sample_size < 3 * down_scale_factor:
										raise ValueError(
										    F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
										    F' {3 * down_scale_factor / self.unet.config.sample_rate}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
										) * down_scale_factor
										logger.info(
										    F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
										    F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
										    """ process."""				)
								_UpperCamelCase						    =	int(a				)

								_UpperCamelCase						    =	next(iter(self.unet.parameters()				)				).dtype
								_UpperCamelCase						    =	(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(a       ,							a				) and len(a				) != batch_size:
										raise ValueError(
										    F'You have passed a list of generators of length {len(a				)}, but requested an effective batch'
										    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

								# set step values
								self.scheduler.set_timesteps(a       ,							device=audio.device				)
								_UpperCamelCase						    =	self.scheduler.timesteps.to(a				)

								for t in self.progress_bar(self.scheduler.timesteps				):
										# 1. predict noise model_output
										_UpperCamelCase						    =	self.unet(a       ,							a				).sample

										# 2. compute previous image: x_t -> t_t-1
										_UpperCamelCase						    =	self.scheduler.step(a       ,							a       ,							a				).prev_sample

								_UpperCamelCase						    =	audio.clamp(-1       ,							1				).float().cpu().numpy()

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



