


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






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  MobileViTConfig()

			# size of the architecture
			if "mobilevit_s" in mobilevit_name:
						lowerCamelCase    		=  [144, 192, 240]
						lowerCamelCase    		=  [16, 32, 64, 96, 128, 160, 640]
			elif "mobilevit_xs" in mobilevit_name:
						lowerCamelCase    		=  [96, 120, 144]
						lowerCamelCase    		=  [16, 32, 48, 64, 80, 96, 384]
			elif "mobilevit_xxs" in mobilevit_name:
						lowerCamelCase    		=  [64, 80, 96]
						lowerCamelCase    		=  [16, 16, 24, 48, 64, 80, 320]
						lowerCamelCase    		=  0.0_5
						lowerCamelCase    		=  2.0

			if mobilevit_name.startswith("""deeplabv3_"""      ):
						lowerCamelCase    		=  512
						lowerCamelCase    		=  16
						lowerCamelCase    		=  21
						lowerCamelCase    		=  """pascal-voc-id2label.json"""
			else:
						lowerCamelCase    		=  1000
						lowerCamelCase    		=  """imagenet-1k-id2label.json"""

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

			return config






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]  ,							lowerCamelCase__		:						Any=False      ):





			'''simple docstring'''




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

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

			if "exp_1x1" in name:
						lowerCamelCase    		=  name.replace("""exp_1x1"""  ,							"""expand_1x1"""      )
			if "red_1x1" in name:
						lowerCamelCase    		=  name.replace("""red_1x1"""  ,							"""reduce_1x1"""      )
			if ".local_rep.conv_3x3." in name:
						lowerCamelCase    		=  name.replace(""".local_rep.conv_3x3."""  ,							""".conv_kxk."""      )
			if ".local_rep.conv_1x1." in name:
						lowerCamelCase    		=  name.replace(""".local_rep.conv_1x1."""  ,							""".conv_1x1."""      )
			if ".norm." in name:
						lowerCamelCase    		=  name.replace(""".norm."""  ,							""".normalization."""      )
			if ".conv." in name:
						lowerCamelCase    		=  name.replace(""".conv."""  ,							""".convolution."""      )
			if ".conv_proj." in name:
						lowerCamelCase    		=  name.replace(""".conv_proj."""  ,							""".conv_projection."""      )

			for i in range(0  ,							2      ):
						for j in range(0  ,							4      ):
									if f'.{i}.{j}.' in name:
												lowerCamelCase    		=  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:
												lowerCamelCase    		=  name.replace(f'.{i}.{j}.'  ,							f'.{i}.'      )
												if "expand_1x1" in name:
															lowerCamelCase    		=  name.replace("""expand_1x1"""  ,							"""downsampling_layer.expand_1x1"""      )
												if "conv_3x3" in name:
															lowerCamelCase    		=  name.replace("""conv_3x3"""  ,							"""downsampling_layer.conv_3x3"""      )
												if "reduce_1x1" in name:
															lowerCamelCase    		=  name.replace("""reduce_1x1"""  ,							"""downsampling_layer.reduce_1x1"""      )

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

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

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

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

			return name






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						Optional[int]  ,							lowerCamelCase__		:						str=False      ):





			'''simple docstring'''




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

			for key in orig_state_dict.copy().keys():
						lowerCamelCase    		=  orig_state_dict.pop(lowerCamelCase__      )

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

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

			return orig_state_dict






def 	__lowerCamelCase						(							):





			'''simple docstring'''




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






@torch.no_grad()
def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						Optional[int]  ,							lowerCamelCase__		:						Union[str, Any]=False      ):





			'''simple docstring'''




			lowerCamelCase    		=  get_mobilevit_config(lowerCamelCase__      )

			# load original state_dict
			lowerCamelCase    		=  torch.load(lowerCamelCase__  ,							map_location="""cpu"""      )

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

			lowerCamelCase    		=  convert_state_dict(lowerCamelCase__  ,							lowerCamelCase__      )
			model.load_state_dict(lowerCamelCase__      )

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

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

						if mobilevit_name == "deeplabv3_mobilevit_s":
									lowerCamelCase    		=  torch.tensor(
									    [
									        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
									        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
									        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
									    ]      )
						elif mobilevit_name == "deeplabv3_mobilevit_xs":
									lowerCamelCase    		=  torch.tensor(
									    [
									        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
									        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
									        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
									    ]      )
						elif mobilevit_name == "deeplabv3_mobilevit_xxs":
									lowerCamelCase    		=  torch.tensor(
									    [
									        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
									        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
									        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
									    ]      )
						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":
									lowerCamelCase    		=  torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]      )
						elif mobilevit_name == "mobilevit_xs":
									lowerCamelCase    		=  torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]      )
						elif mobilevit_name == "mobilevit_xxs":
									lowerCamelCase    		=  torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]      )
						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:
						lowerCamelCase    		=  {
						    """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..."""      )
						lowerCamelCase    		=  model_mapping[mobilevit_name]
						image_processor.push_to_hub(lowerCamelCase__  ,							organization="""apple"""      )
						model.push_to_hub(lowerCamelCase__  ,							organization="""apple"""      )


if __name__ == "__main__":
		UpperCAmelCase   :    Optional[Any]     =		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."
		)

		UpperCAmelCase   :    Any     =		parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase   :    Any     =		logging.getLogger(__name__)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  argparse.ArgumentParser(
			    description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""      )
			parser.add_argument(
			    """--dataset_name"""  ,							type=lowerCamelCase__  ,							default="""wikitext"""  ,							help="""Name of the training. Explore datasets at: hf.co/datasets."""  ,							)
			parser.add_argument(
			    """--dataset_config"""  ,							type=lowerCamelCase__  ,							default="""wikitext-103-raw-v1"""  ,							help="""Configuration name of the dataset."""      )
			parser.add_argument(
			    """--tokenizer_name_or_path"""  ,							type=lowerCamelCase__  ,							default="""sayakpaul/unigram-tokenizer-wikitext"""  ,							help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""  ,							)
			parser.add_argument(
			    """--shard_size"""  ,							type=lowerCamelCase__  ,							default=1000  ,							help="""Number of entries to go in a single shard."""  ,							)
			parser.add_argument("""--split"""  ,							type=lowerCamelCase__  ,							default="""train"""  ,							choices=["""train""", """test""", """validation"""]      )
			parser.add_argument(
			    """--limit"""  ,							default=lowerCamelCase__  ,							type=lowerCamelCase__  ,							help="""Limit the number of shards (used for debugging)."""  ,							)
			parser.add_argument(
			    """--max_length"""  ,							type=lowerCamelCase__  ,							default=512  ,							help="""Maximum sequence length. For training on TPUs, it helps to have a maximum"""
			    """ sequence length that is a multiple of 8."""  ,							)
			parser.add_argument(
			    """--output_dir"""  ,							default="""tf-tpu"""  ,							type=lowerCamelCase__  ,							help="""Output directory where the TFRecord shards will be saved. If the"""
			    """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"""
			    """ shards will be directly saved to a Google Cloud Storage bucket."""  ,							)

			lowerCamelCase    		=  parser.parse_args()
			return args






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			def fn(lowerCamelCase__		:						Any      ):
						return tokenizer(examples["""text"""]      )

			return fn






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  []
			for i in range(len(tokenized_data["""input_ids"""]      )      ):
						lowerCamelCase    		=  {
						    """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i]      )      ),
						    """attention_mask""": tf.train.Feature(
						        intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i]      )      ),
						}
						lowerCamelCase    		=  tf.train.Features(feature=lowerCamelCase__      )
						lowerCamelCase    		=  tf.train.Example(features=lowerCamelCase__      )
						lowerCamelCase    		=  example.SerializeToString()
						records.append(lowerCamelCase__      )
			return records






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			lowerCamelCase    		=  datasets.load_dataset(args.dataset_name  ,							args.dataset_config  ,							split=args.split      )

			if args.limit is not None:
						lowerCamelCase    		=  min(len(lowerCamelCase__      )  ,							args.limit      )
						lowerCamelCase    		=  dataset.select(range(lowerCamelCase__      )      )
						print(f'Limiting the dataset to {args.limit} entries.'      )

			lowerCamelCase    		=  AutoTokenizer.from_pretrained(args.tokenizer_name_or_path      )

			# Handle output directory creation.
			# For serializing into a Google Cloud Storage Bucket, one needs to first
			# create a bucket.
			if "gs" not in args.output_dir:
						if not os.path.exists(args.output_dir      ):
									os.makedirs(args.output_dir      )
						lowerCamelCase    		=  os.path.join(args.output_dir  ,							args.split      )
						if not os.path.exists(lowerCamelCase__      ):
									os.makedirs(lowerCamelCase__      )
			else:
						lowerCamelCase    		=  os.path.join(args.output_dir  ,							args.split      )

			# Tokenize the whole dataset at once.
			lowerCamelCase    		=  tokenize_function(lowerCamelCase__      )
			lowerCamelCase    		=  dataset.map(lowerCamelCase__  ,							batched=lowerCamelCase__  ,							num_proc=4  ,							remove_columns=["""text"""]      )

			# We need to concatenate all our texts together, and then split the result
			# into chunks of a fixed size, which we will call block_size. To do this, we
			# will use the map method again, with the option batched=True. When we use batched=True,
			# the function we pass to map() will be passed multiple inputs at once, allowing us
			# to group them into more or fewer examples than we had in the input.
			# This allows us to create our new fixed-length samples. The advantage of this
			# method is that we don't lose a whole lot of content from the dataset compared to the
			# case where we simply tokenize with a pre-defined max_length.

			def group_texts(lowerCamelCase__		:						List[str]      ):
						# Concatenate all texts.
						lowerCamelCase    		=  {k: sum(examples[k]  ,							[]      ) for k in examples.keys()}
						lowerCamelCase    		=  len(concatenated_examples[list(examples.keys()      )[0]]      )
						# We drop the small remainder, though you could add padding instead if the model supports it
						# In this, as in all things, we advise you to follow your heart 🫀
						lowerCamelCase    		=  (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						lowerCamelCase    		=  {
						    k: [t[i : i + args.max_length] for i in range(0  ,							lowerCamelCase__  ,							args.max_length      )]
						    for k, t in concatenated_examples.items()
						}
						return result

			lowerCamelCase    		=  dataset_tokenized.map(lowerCamelCase__  ,							batched=lowerCamelCase__  ,							batch_size=1000  ,							num_proc=4      )

			lowerCamelCase    		=  0
			lowerCamelCase    		=  0
			for shard in range(0  ,							len(lowerCamelCase__      )  ,							args.shard_size      ):
						lowerCamelCase    		=  grouped_dataset[shard : shard + args.shard_size]
						lowerCamelCase    		=  len(dataset_snapshot["""input_ids"""]      )
						lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							f'dataset-{shard_count}-{records_containing}.tfrecord'      )
						lowerCamelCase    		=  get_serialized_examples(lowerCamelCase__      )

						with tf.io.TFRecordWriter(lowerCamelCase__      ) as out_file:
									for i in range(len(lowerCamelCase__      )      ):
												lowerCamelCase    		=  serialized_examples[i]
												out_file.write(lowerCamelCase__      )
									print("""Wrote file {} containing {} records""".format(lowerCamelCase__  ,							lowerCamelCase__      )      )

						shard_count += 1
						total_records += records_containing

			with open(f'split-{args.split}-records-count.txt'  ,							"""w"""      ) as f:
						print(f'Total {args.split} records: {total_records}'  ,							file=lowerCamelCase__      )


if __name__ == "__main__":
		UpperCAmelCase   :    Optional[int]     =		parse_args()
		main(args)

