
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()
_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		snake_case_						:     Optional[Any]     =		[1_44, 1_92, 2_40]
		snake_case_						:     Optional[int]     =		[16, 32, 64, 96, 1_28, 1_60, 6_40]
	elif "mobilevit_xs" in mobilevit_name:
		snake_case_						:     Dict     =		[96, 1_20, 1_44]
		snake_case_						:     int     =		[16, 32, 48, 64, 80, 96, 3_84]
	elif "mobilevit_xxs" in mobilevit_name:
		snake_case_						:     List[str]     =		[64, 80, 96]
		snake_case_						:     Union[str, Any]     =		[16, 16, 24, 48, 64, 80, 3_20]
		snake_case_						:     Tuple     =		0.05
		snake_case_						:     Optional[int]     =		2.0

	if mobilevit_name.startswith('deeplabv3_'				):
		snake_case_						:     Union[str, Any]     =		5_12
		snake_case_						:     int     =		16
		snake_case_						:     str     =		21
		snake_case_						:     Optional[int]     =		'pascal-voc-id2label.json'
	else:
		snake_case_						:     int     =		10_00
		snake_case_						:     Tuple     =		'imagenet-1k-id2label.json'

	snake_case_						:     int     =		'huggingface/label-files'
	snake_case_						:     List[Any]     =		json.load(open(hf_hub_download(__A				,	__A				,	repo_type='dataset'				)				,	'r'				)				)
	snake_case_						:     Tuple     =		{int(__A				): v for k, v in idalabel.items()}
	snake_case_						:     int     =		idalabel
	snake_case_						:     Tuple     =		{v: k for k, v in idalabel.items()}

	return config


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=False				):


	for i in range(1				,	6				):
		if f"""layer_{i}.""" in name:
			snake_case_						:     Optional[Any]     =		name.replace(f"""layer_{i}."""				,	f"""encoder.layer.{i - 1}."""				)

	if "conv_1." in name:
		snake_case_						:     Union[str, Any]     =		name.replace('conv_1.'				,	'conv_stem.'				)
	if ".block." in name:
		snake_case_						:     int     =		name.replace('.block.'				,	'.'				)

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

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

	for i in range(2				,	5				):
		if f""".global_rep.{i}.weight""" in name:
			snake_case_						:     Tuple     =		name.replace(f""".global_rep.{i}.weight"""				,	'.layernorm.weight'				)
		if f""".global_rep.{i}.bias""" in name:
			snake_case_						:     int     =		name.replace(f""".global_rep.{i}.bias"""				,	'.layernorm.bias'				)

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

	if ".aspp_layer." in name:
		snake_case_						:     Tuple     =		name.replace('.aspp_layer.'				,	'.'				)
	if ".aspp_pool." in name:
		snake_case_						:     int     =		name.replace('.aspp_pool.'				,	'.'				)
	if "seg_head." in name:
		snake_case_						:     Union[str, Any]     =		name.replace('seg_head.'				,	'segmentation_head.'				)
	if "segmentation_head.classifier.classifier." in name:
		snake_case_						:     Dict     =		name.replace('segmentation_head.classifier.classifier.'				,	'segmentation_head.classifier.'				)

	if "classifier.fc." in name:
		snake_case_						:     Optional[int]     =		name.replace('classifier.fc.'				,	'classifier.'				)
	elif (not base_model) and ("segmentation_head." not in name):
		snake_case_						:     int     =		'mobilevit.' + name

	return name


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a=False				):


	if base_model:
		snake_case_						:     Tuple     =		''
	else:
		snake_case_						:     Dict     =		'mobilevit.'

	for key in orig_state_dict.copy().keys():
		snake_case_						:     Dict     =		orig_state_dict.pop(__A				)

		if key[:8] == "encoder.":
			snake_case_						:     List[Any]     =		key[8:]

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

	return orig_state_dict


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Union[str, Any]     =		'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_						:     List[str]     =		Image.open(requests.get(__A				,	stream=__A				).raw				)
	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a=False				):
	snake_case_						:     List[str]     =		get_mobilevit_config(__A				)

	# load original state_dict
	snake_case_						:     Optional[int]     =		torch.load(__A				,	map_location='cpu'				)

	# load 🤗 model
	if mobilevit_name.startswith('deeplabv3_'				):
		snake_case_						:     Union[str, Any]     =		MobileViTForSemanticSegmentation(__A				).eval()
	else:
		snake_case_						:     List[Any]     =		MobileViTForImageClassification(__A				).eval()

	snake_case_						:     Union[str, Any]     =		convert_state_dict(__A				,	__A				)
	model.load_state_dict(__A				)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	snake_case_						:     List[str]     =		MobileViTImageProcessor(crop_size=config.image_size				,	size=config.image_size + 32				)
	snake_case_						:     Tuple     =		image_processor(images=prepare_img()				,	return_tensors='pt'				)
	snake_case_						:     Dict     =		model(**__A				)
	snake_case_						:     Union[str, Any]     =		outputs.logits

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			snake_case_						:     Dict     =		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":
			snake_case_						:     str     =		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":
			snake_case_						:     Optional[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]				,	__A				,	atol=1E-4				)
	else:
		assert logits.shape == (1, 10_00)

		if mobilevit_name == "mobilevit_s":
			snake_case_						:     Union[str, Any]     =		torch.tensor([-0.9866, 0.2392, -1.1241]				)
		elif mobilevit_name == "mobilevit_xs":
			snake_case_						:     Tuple     =		torch.tensor([-2.4761, -0.9399, -1.9587]				)
		elif mobilevit_name == "mobilevit_xxs":
			snake_case_						:     Union[str, Any]     =		torch.tensor([-1.9364, -1.2327, -0.4653]				)
		else:
			raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""				)

		assert torch.allclose(logits[0, :3]				,	__A				,	atol=1E-4				)

	Path(__A				).mkdir(exist_ok=__A				)
	print(f"""Saving model {mobilevit_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 push_to_hub:
		snake_case_						:     int     =		{
		    '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...'				)
		snake_case_						:     Any     =		model_mapping[mobilevit_name]
		image_processor.push_to_hub(__A				,	organization='apple'				)
		model.push_to_hub(__A				,	organization='apple'				)


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

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








import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE 		=						"""docs/source/en/_toctree.yml"""


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[Any]     =		defaultdict(__a				)
	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[Any]     =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
		else:
			new_doc_list.append(__a				)

	snake_case_						:     Any     =		new_doc_list
	snake_case_						:     str     =		[key for key, value in counts.items() if value > 1]

	snake_case_						:     Any     =		[]
	for duplicate_key in duplicates:
		snake_case_						:     Any     =		list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
		if len(__a				) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
			    'others.'				)
		# Only add this once
		new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
	snake_case_						:     str     =		sorted(__a				,	key=lambda __a				: s["title"].lower()				)

	# "overview" gets special treatment and is always first
	if len(__a				) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

	overview_doc.extend(__a				)

	# Sort
	return overview_doc


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     int     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     str     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     Dict     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     Tuple     =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	snake_case_						:     Union[str, Any]     =		api_doc[scheduler_idx]['sections']
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	snake_case_						:     int     =		False
	if new_scheduler_doc != scheduler_doc:
		snake_case_						:     int     =		True
		if overwrite:
			snake_case_						:     Union[str, Any]     =		new_scheduler_doc

	if diff:
		if overwrite:
			snake_case_						:     Optional[int]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     Any     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     str     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     List[Any]     =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	snake_case_						:     Dict     =		False
	snake_case_						:     Union[str, Any]     =		api_doc[pipeline_idx]['sections']
	snake_case_						:     Union[str, Any]     =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			snake_case_						:     Optional[Any]     =		pipeline_doc['section']
			snake_case_						:     Optional[int]     =		clean_doc_toc(__a				)
			if overwrite:
				snake_case_						:     Tuple     =		new_sub_pipeline_doc
		new_pipeline_docs.append(__a				)

	# sort overall pipeline doc
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	if new_pipeline_docs != pipeline_docs:
		snake_case_						:     List[str]     =		True
		if overwrite:
			snake_case_						:     List[str]     =		new_pipeline_docs

	if diff:
		if overwrite:
			snake_case_						:     List[Any]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)

