


import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


SCREAMING_SNAKE_CASE__       =      '''<<<<<<< This should probably be modified because it mentions: '''

SCREAMING_SNAKE_CASE__       =      '''=======
>>>>>>>
'''

SCREAMING_SNAKE_CASE__       =      [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

SCREAMING_SNAKE_CASE__       =      [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'''tfds\.core''', r'''datasets'''),
    (r'''tf\.io\.gfile\.GFile''', r'''open'''),
    (r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
    (r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
    (r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
    (r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
    (r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
    (r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (r'''tfds\.''', r'''datasets.'''),
    (r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
    (r'''self\.builder_config''', r'''self.config'''),
]


def A						(       __UpperCamelCase					)						->      Optional[Any]:
			return ConvertCommand(args.tfds_path ,	args.datasets_directory					)






class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@staticmethod
					def     _a							(     _snake_case					:       ArgumentParser					):






								"""simple docstring"""


								A__       =    parser.add_parser(
								    'convert'				,					help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'				,					)
								train_parser.add_argument(
								    '--tfds_path'				,					type=_snake_case				,					required=_snake_case				,					help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'				,					)
								train_parser.add_argument(
								    '--datasets_directory'				,					type=_snake_case				,					required=_snake_case				,					help='Path to the HuggingFace Datasets folder.'					)
								train_parser.set_defaults(func=_snake_case					)



					def __init__(     self					:       str				,					_snake_case					:       str				,					_snake_case					:       str				,					*_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    get_logger('datasets-cli/converting'					)

								A__       =    tfds_path
								A__       =    datasets_directory



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if os.path.isdir(self._tfds_path					):
											A__       =    os.path.abspath(self._tfds_path					)
								elif os.path.isfile(self._tfds_path					):
											A__       =    os.path.dirname(self._tfds_path					)
								else:
											raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'					)

								A__       =    os.path.abspath(self._datasets_directory					)

								self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''					)

								A__       =    []
								A__       =    []
								A__       =    {}

								if os.path.isdir(self._tfds_path					):
											A__       =    os.listdir(_snake_case					)
								else:
											A__       =    [os.path.basename(self._tfds_path					)]

								for f_name in file_names:
											self._logger.info(F'''Looking at file {f_name}'''					)
											A__       =    os.path.join(_snake_case				,					_snake_case					)
											A__       =    os.path.join(_snake_case				,					_snake_case					)

											if not os.path.isfile(_snake_case					) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
														self._logger.info('Skipping file'					)
														continue

											with open(_snake_case				,					encoding='utf-8'					) as f:
														A__       =    f.readlines()

											A__       =    []
											A__       =    False
											A__       =    False
											A__       =    []
											for line in lines:
														A__       =    line

														# Convert imports
														if "import tensorflow.compat.v2 as tf" in out_line:
																	continue
														elif "@tfds.core" in out_line:
																	continue
														elif "builder=self" in out_line:
																	continue
														elif "import tensorflow_datasets.public_api as tfds" in out_line:
																	A__       =    'import datasets\n'
														elif "import tensorflow" in out_line:
																	# order is important here
																	A__       =    ''
																	continue
														elif "from absl import logging" in out_line:
																	A__       =    'from datasets import logging\n'
														elif "getLogger" in out_line:
																	A__       =    out_line.replace('getLogger'				,					'get_logger'					)
														elif any(expression in out_line for expression in TO_HIGHLIGHT					):
																	A__       =    True
																	A__       =    list(filter(lambda _snake_case					: e in out_line				,					_snake_case					)					)
																	out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_snake_case					) + '\n'					)
																	out_lines.append(_snake_case					)
																	out_lines.append(_snake_case					)
																	continue
														else:
																	for pattern, replacement in TO_CONVERT:
																				A__       =    re.sub(_snake_case				,					_snake_case				,					_snake_case					)

                # Take care of saving utilities (to later move them together with main script)
														if "tensorflow_datasets" in out_line:
																	A__       =    re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)'				,					_snake_case					)
																	tfds_imports.extend(imp.strip() for imp in match.group(1					).split(','					)					)
																	A__       =    'from . import ' + match.group(1					)

														# Check we have not forget anything
														if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																	raise ValueError(F'''Error converting {out_line.strip()}'''					)

														if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																	A__       =    True
														out_lines.append(_snake_case					)

											if is_builder or "wmt" in f_name:
														# We create a new directory for each dataset
														A__       =    f_name.replace('.py'				,					''					)
														A__       =    os.path.join(_snake_case				,					_snake_case					)
														A__       =    os.path.join(_snake_case				,					_snake_case					)
														os.makedirs(_snake_case				,					exist_ok=_snake_case					)
														self._logger.info(F'''Adding directory {output_dir}'''					)
														imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}					)
											else:
														# Utilities will be moved at the end
														utils_files.append(_snake_case					)

											if needs_manual_update:
														with_manual_update.append(_snake_case					)

											with open(_snake_case				,					'w'				,					encoding='utf-8'					) as f:
														f.writelines(_snake_case					)
											self._logger.info(F'''Converted in {output_file}'''					)

								for utils_file in utils_files:
											try:
														A__       =    os.path.basename(_snake_case					)
														A__       =    imports_to_builder_map[f_name.replace('.py'				,					''					)]
														self._logger.info(F'''Moving {dest_folder} to {utils_file}'''					)
														shutil.copy(_snake_case				,					_snake_case					)
											except KeyError:
														self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''					)

								if with_manual_update:
											for file_path in with_manual_update:
														self._logger.warning(
														    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''					)









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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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


def A						(       __UpperCamelCase					)						->      YolosConfig:
			A__       =    YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A__       =    192
						A__       =    768
						A__       =    12
						A__       =    3
						A__       =    [800, 1_333]
						A__       =    False
			elif yolos_name == "yolos_s_dWr":
						A__       =    330
						A__       =    14
						A__       =    6
						A__       =    1_320
			elif "yolos_s" in yolos_name:
						A__       =    384
						A__       =    1_536
						A__       =    12
						A__       =    6
			elif "yolos_b" in yolos_name:
						A__       =    [800, 1_344]

			A__       =    91
			A__       =    'huggingface/label-files'
			A__       =    'coco-detection-id2label.json'
			A__       =    json.load(open(hf_hub_download(__UpperCamelCase ,	__UpperCamelCase ,	repo_type='dataset'					) ,	'r'					)					)
			A__       =    {int(__UpperCamelCase					): v for k, v in idalabel.items()}
			A__       =    idalabel
			A__       =    {v: k for k, v in idalabel.items()}

			return config


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      str:
			for i in range(config.num_hidden_layers					):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''					)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''					)
						# next, add query, keys and values (in that order) to the state dict
						A__       =    in_proj_weight[: config.hidden_size, :]
						A__       =    in_proj_bias[: config.hidden_size]
						A__       =    in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__       =    in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A__       =    in_proj_weight[-config.hidden_size :, :]
						A__       =    in_proj_bias[-config.hidden_size :]


def A						(       __UpperCamelCase					)						->      str:
			if "backbone" in name:
						A__       =    name.replace('backbone' ,	'vit'					)
			if "cls_token" in name:
						A__       =    name.replace('cls_token' ,	'embeddings.cls_token'					)
			if "det_token" in name:
						A__       =    name.replace('det_token' ,	'embeddings.detection_tokens'					)
			if "mid_pos_embed" in name:
						A__       =    name.replace('mid_pos_embed' ,	'encoder.mid_position_embeddings'					)
			if "pos_embed" in name:
						A__       =    name.replace('pos_embed' ,	'embeddings.position_embeddings'					)
			if "patch_embed.proj" in name:
						A__       =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
			if "blocks" in name:
						A__       =    name.replace('blocks' ,	'encoder.layer'					)
			if "attn.proj" in name:
						A__       =    name.replace('attn.proj' ,	'attention.output.dense'					)
			if "attn" in name:
						A__       =    name.replace('attn' ,	'attention.self'					)
			if "norm1" in name:
						A__       =    name.replace('norm1' ,	'layernorm_before'					)
			if "norm2" in name:
						A__       =    name.replace('norm2' ,	'layernorm_after'					)
			if "mlp.fc1" in name:
						A__       =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
			if "mlp.fc2" in name:
						A__       =    name.replace('mlp.fc2' ,	'output.dense'					)
			if "class_embed" in name:
						A__       =    name.replace('class_embed' ,	'class_labels_classifier'					)
			if "bbox_embed" in name:
						A__       =    name.replace('bbox_embed' ,	'bbox_predictor'					)
			if "vit.norm" in name:
						A__       =    name.replace('vit.norm' ,	'vit.layernorm'					)

			return name


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      dict:
			for key in orig_state_dict.copy().keys():
						A__       =    orig_state_dict.pop(__UpperCamelCase					)

						if "qkv" in key:
									A__       =    key.split('.'					)
									A__       =    int(key_split[2]					)
									A__       =    model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												A__       =    val[:dim, :]
												A__       =    val[
												    dim : dim * 2, :
												]
												A__       =    val[-dim:, :]
									else:
												A__       =    val[:dim]
												A__       =    val[dim : dim * 2]
												A__       =    val[-dim:]
						else:
									A__       =    val

			return orig_state_dict


def A						(       )						->      torch.Tensor:
			A__       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__       =    Image.open(requests.get(__UpperCamelCase ,	stream=__UpperCamelCase					).raw					)
			return im


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    get_yolos_config(__UpperCamelCase					)

			# load original state_dict
			A__       =    torch.load(__UpperCamelCase ,	map_location='cpu'					)['model']

			# load 🤗 model
			A__       =    YolosForObjectDetection(__UpperCamelCase					)
			model.eval()
			A__       =    convert_state_dict(__UpperCamelCase ,	__UpperCamelCase					)
			model.load_state_dict(__UpperCamelCase					)

			# Check outputs on an image, prepared by YolosImageProcessor
			A__       =    800 if yolos_name != 'yolos_ti' else 512
			A__       =    YolosImageProcessor(format='coco_detection' ,	size=__UpperCamelCase					)
			A__       =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
			A__       =    model(**__UpperCamelCase					)
			A__     , A__       =    outputs.logits, outputs.pred_boxes

			A__     , A__       =    None, None
			if yolos_name == "yolos_ti":
						A__       =    torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
						A__       =    torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
			elif yolos_name == "yolos_s_200_pre":
						A__       =    torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
						A__       =    torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
			elif yolos_name == "yolos_s_300_pre":
						A__       =    torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
						A__       =    torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
			elif yolos_name == "yolos_s_dWr":
						A__       =    torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
						A__       =    torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
			elif yolos_name == "yolos_base":
						A__       =    torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
						A__       =    torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
			else:
						raise ValueError(f'''Unknown yolos_name: {yolos_name}'''					)

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

			Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
			print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(__UpperCamelCase					)
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
			image_processor.save_pretrained(__UpperCamelCase					)

			if push_to_hub:
						A__       =    {
						    'yolos_ti': 'yolos-tiny',
						    'yolos_s_200_pre': 'yolos-small',
						    'yolos_s_300_pre': 'yolos-small-300',
						    'yolos_s_dWr': 'yolos-small-dwr',
						    'yolos_base': 'yolos-base',
						}

						print('Pushing to the hub...'					)
						A__       =    model_mapping[yolos_name]
						image_processor.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)
						model.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--yolos_name''',
							    default='''yolos_s_200_pre''',
							    type=str,
							    help=(
							        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
							        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
							    ),
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, 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_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




