

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__A					=	get_logger(__name__)

# Datasets
__A					=	'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
__A					=	'https://cdn-datasets.huggingface.co/datasets/datasets'
__A					=	'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
__A					=	'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
__A					=	'https://cdn-datasets.huggingface.co/datasets/metric'
__A					=	'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
__A					=	os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
__A					=	HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
__A					=	'main'

__A					=	version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
__A					=	{'1', 'ON', 'YES', 'TRUE'}
__A					=	ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
__A					=	version.parse(importlib_metadata.version('''dill'''))
__A					=	version.parse(importlib_metadata.version('''pandas'''))
__A					=	version.parse(importlib_metadata.version('''pyarrow'''))

__A					=	os.environ.get('''USE_TF''', '''AUTO''').upper()
__A					=	os.environ.get('''USE_TORCH''', '''AUTO''').upper()
__A					=	os.environ.get('''USE_JAX''', '''AUTO''').upper()

__A					=	'N/A'
__A					=	False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
 __A					=	importlib.util.find_spec('''torch''') is not None
 if TORCH_AVAILABLE:
  try:
   __A					=	version.parse(importlib_metadata.version('''torch'''))
   logger.info(f'''PyTorch version {TORCH_VERSION} available.''')
  except importlib_metadata.PackageNotFoundError:
   pass
else:
 logger.info('''Disabling PyTorch because USE_TF is set''')

__A					=	'N/A'
__A					=	False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
 __A					=	importlib.util.find_spec('''tensorflow''') is not None
 if TF_AVAILABLE:
  # For the metadata, we have to look for both tensorflow and tensorflow-cpu
  for package in [
      "tensorflow",
      "tensorflow-cpu",
      "tensorflow-gpu",
      "tf-nightly",
      "tf-nightly-cpu",
      "tf-nightly-gpu",
      "intel-tensorflow",
      "tensorflow-rocm",
      "tensorflow-macos",
  ]:
   try:
    __A					=	version.parse(importlib_metadata.version(package))
   except importlib_metadata.PackageNotFoundError:
    continue
   else:
    break
  else:
   __A					=	False
 if TF_AVAILABLE:
  if TF_VERSION.major < 2:
   logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
   __A					=	False
  else:
   logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
 logger.info('''Disabling Tensorflow because USE_TORCH is set''')


__A					=	'N/A'
__A					=	False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
 __A					=	importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
 if JAX_AVAILABLE:
  try:
   __A					=	version.parse(importlib_metadata.version('''jax'''))
   logger.info(f'''JAX version {JAX_VERSION} available.''')
  except importlib_metadata.PackageNotFoundError:
   pass
else:
 logger.info('''Disabling JAX because USE_JAX is set to False''')


__A					=	os.environ.get('''USE_BEAM''', '''AUTO''').upper()
__A					=	'N/A'
__A					=	False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
 try:
  __A					=	version.parse(importlib_metadata.version('''apache_beam'''))
  __A					=	True
  logger.info(f'''Apache Beam version {BEAM_VERSION} available.''')
 except importlib_metadata.PackageNotFoundError:
  pass
else:
 logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
__A					=	importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
__A					=	importlib.util.find_spec('''PIL''') is not None
__A					=	importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
__A					=	importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
__A					=	importlib.util.find_spec('''rarfile''') is not None
__A					=	importlib.util.find_spec('''zstandard''') is not None
__A					=	importlib.util.find_spec('''lz4''') is not None
__A					=	importlib.util.find_spec('''py7zr''') is not None


# Cache location
__A					=	'~/.cache'
__A					=	os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
__A					=	os.path.join(XDG_CACHE_HOME, '''huggingface''')
__A					=	os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

__A					=	os.path.join(HF_CACHE_HOME, '''datasets''')
__A					=	Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

__A					=	os.path.join(HF_CACHE_HOME, '''metrics''')
__A					=	Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

__A					=	os.path.join(HF_CACHE_HOME, '''modules''')
__A					=	Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

__A					=	'downloads'
__A					=	os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
__A					=	Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

__A					=	'extracted'
__A					=	os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
__A					=	Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
__A					=	(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
__A					=	10_00

# Size of the preloaded record batch in `Dataset.__iter__`
__A					=	10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
__A					=	4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
__A					=	'500MB'

# Parquet configuration
__A					=	1_00
__A					=	1_00
__A					=	1_00

# Offline mode
__A					=	os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
__A					=	0  # Disabled
__A					=	float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
__A					=	'dataset.arrow'
__A					=	'indices.arrow'
__A					=	'state.json'
__A					=	'dataset_info.json'
__A					=	'dataset_infos.json'
__A					=	'LICENSE'
__A					=	'metric_info.json'
__A					=	'dataset_dict.json'

__A					=	'datasets_modules'

__A					=	2_55

# Streaming
__A					=	20
__A					=	5

# Datasets without script
__A					=	2_00
__A					=	10
__A					=	2_00

# Progress bars
__A					=	0.05  # 20 progress updates per sec



'''simple docstring'''
import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase :  Dict      =      logging.get_logger()


@dataclass
class        SCREAMING_SNAKE_CASE__	:
				lowerCAmelCase_			       =				42
				lowerCAmelCase_			       =				field(default_factory=snake_case_)
				lowerCAmelCase_			       =				field(default_factory=snake_case_)
				lowerCAmelCase_			       =				field(default_factory=snake_case_)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_			)->						str:







											'''simple docstring'''






											UpperCamelCase					       =			len(list(m.modules()			)			) == 1 or isinstance(A_		,							nn.Convad			) or isinstance(A_		,							nn.BatchNormad			)
											if has_not_submodules:
																		self.traced.append(A_			)
																		UpperCamelCase					       =			m







				def __call__(    self		,							A_			)->						int:







											'''simple docstring'''






											for name, m in self.module.named_modules():
																		self.handles.append(m.register_forward_hook(partial(self._forward_hook		,							name=A_			)			)			)
											self.module(A_			)
											[x.remove() for x in self.handles]
											return self







				@property
				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()			)			) > 0}


class        SCREAMING_SNAKE_CASE__	(			nn.Module):







				def __init__(    self		,							A_			)->						List[Any]:







											'''simple docstring'''






											super().__init__()

											UpperCamelCase					       =			[]
											# - get the stem
											feature_blocks.append(('conv1', model.stem)			)
											# - get all the feature blocks
											for k, v in model.trunk_output.named_children():
																		assert k.startswith('block'			), F'''Unexpected layer name {k}'''
																		UpperCamelCase					       =			len(A_			) + 1
																		feature_blocks.append((F'''res{block_index}''', v)			)

											UpperCamelCase					       =			nn.ModuleDict(A_			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											return get_trunk_forward_outputs(
											    A_		,							out_feat_keys=A_		,							feature_blocks=self._feature_blocks		,							)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def 						A_(			A		:						str):
							UpperCamelCase					       =			RegNetConfig(depths=[2, 7, 17, 1]						,			hidden_sizes=[8, 8, 8, 8]						,			groups_width=8)
							if "in1k" in model_name:
														UpperCamelCase					       =			RegNetForImageClassification(A)
							else:
														UpperCamelCase					       =			RegNetModel(A)
							# create from model (with small weights)
							UpperCamelCase					       =			FakeRegNetVisslWrapper(
							    RegNet(FakeRegNetParams(depth=27						,			group_width=1010						,			w_a=1744						,			w_a=620.83						,			w_m=2.52)))

							with torch.no_grad():
														UpperCamelCase					       =			from_model.eval()
														UpperCamelCase					       =			our_model.eval()

														UpperCamelCase					       =			torch.randn((1, 3, 32, 32))
														# trace both
														UpperCamelCase					       =			Tracker(A)
														UpperCamelCase					       =			dest_tracker(A).parametrized

														pprint(dest_tracker.nameamodule)
														UpperCamelCase					       =			Tracker(A)
														UpperCamelCase					       =			src_tracker(A).parametrized

							# convert the keys -> module dict to keys -> params
							def to_params_dict(A		:						Union[str, Any]):
														UpperCamelCase					       =			OrderedDict()
														for name, module in dict_with_modules.items():
																					for param_name, param in module.state_dict().items():
																												UpperCamelCase					       =			param
														return params_dict

							UpperCamelCase					       =			{}

							UpperCamelCase					       =			to_params_dict(A)
							UpperCamelCase					       =			to_params_dict(A)

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,			dst_state_dict.items()):
														UpperCamelCase					       =			dest_key
														logger.info(f'''{src_key} -> {dest_key}''')
							# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
							if "in1k" in model_name:
														UpperCamelCase					       =			'classifier.1.weight'
														UpperCamelCase					       =			'classifier.1.bias'

							return from_to_ours_keys







def 						A_(			A		:						Path						,			A		:						str = None						,			A		:						bool = True):
							UpperCamelCase					       =			'imagenet-1k-id2label.json'
							UpperCamelCase					       =			1000

							UpperCamelCase					       =			'huggingface/label-files'
							UpperCamelCase					       =			num_labels
							UpperCamelCase					       =			json.load(open(cached_download(hf_hub_url(A						,			A						,			repo_type='dataset'))						,			'r'))
							UpperCamelCase					       =			{int(A): v for k, v in idalabel.items()}

							UpperCamelCase					       =			idalabel
							UpperCamelCase					       =			{v: k for k, v in idalabel.items()}

							UpperCamelCase					       =			partial(A						,			num_labels=A						,			idalabel=A						,			labelaid=A)

							UpperCamelCase					       =			{
							    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]						,			hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,			groups_width=1010),
							    # finetuned on imagenet
							    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]						,			hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,			groups_width=1010),
							}

							# add seer weights logic
							def load_using_classy_vision(A		:						str) -> Tuple[Dict, Dict]:
														UpperCamelCase					       =			torch.hub.load_state_dict_from_url(A						,			model_dir=str(A)						,			map_location='cpu')
														# check if we have a head, if yes add it
														UpperCamelCase					       =			files['classy_state_dict']['base_model']['model']
														return model_state_dict["trunk"], model_state_dict["heads"]

							UpperCamelCase					       =			{
							    'regnet-y-10b-seer': partial(
							        A						,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'						,			),
							    'regnet-y-10b-seer-in1k': partial(
							        A						,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'						,			),
							}

							UpperCamelCase					       =			get_from_to_our_keys(A)

							if not (save_directory / f'''{model_name}.pth''').exists():
														logger.info('Loading original state_dict.')
														UpperCamelCase       ,     UpperCamelCase					       =			names_to_from_model[model_name]()
														UpperCamelCase					       =			from_state_dict_trunk
														if "in1k" in model_name:
																					# add the head
																					UpperCamelCase					       =			{**from_state_dict_trunk, **from_state_dict_head}
														logger.info('Done!')

														UpperCamelCase					       =			{}

														UpperCamelCase					       =			list(from_state_dict.keys())
														UpperCamelCase					       =			r'\.block.-part.'
														# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
														for key in from_state_dict.keys():
																					# remove the weird "block[0,1]-part" from the key
																					UpperCamelCase					       =			re.sub(A						,			''						,			A)
																					# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																					UpperCamelCase					       =			from_to_ours_keys[src_key]
																					# store the parameter with our key
																					UpperCamelCase					       =			from_state_dict[key]
																					not_used_keys.remove(A)
														# check that all keys have been updated
														assert len(A) == 0, f'''Some keys where not used {",".join(A)}'''

														logger.info(f'''The following keys were not used: {",".join(A)}''')

														# save our state dict to disk
														torch.save(A						,			save_directory / f'''{model_name}.pth''')

														del converted_state_dict
							else:
														logger.info('The state_dict was already stored on disk.')
							if push_to_hub:
														logger.info(f'''Token is {os.environ["HF_TOKEN"]}''')
														logger.info('Loading our model.')
														# create our model
														UpperCamelCase					       =			names_to_config[model_name]
														UpperCamelCase					       =			RegNetModel
														if "in1k" in model_name:
																					UpperCamelCase					       =			RegNetForImageClassification
														UpperCamelCase					       =			our_model_func(A)
														# place our model to the meta device (so remove all the weights)
														our_model.to(torch.device('meta'))
														logger.info('Loading state_dict in our model.')
														# load state dict
														UpperCamelCase					       =			our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    A						,			A						,			[save_directory / f'''{model_name}.pth'''])
														logger.info('Finally, pushing!')
														# push it to hub
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / model_name						,			commit_message='Add model'						,			output_dir=save_directory / model_name						,			)
														UpperCamelCase					       =			384
														# we can use the convnext one
														UpperCamelCase					       =			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						,			size=A)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / model_name						,			commit_message='Add image processor'						,			output_dir=save_directory / model_name						,			)


if __name__ == "__main__":
						lowerCAmelCase :  List[str]      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default=None,
						    type=str,
						    help=(
						        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
						        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=Path,
						    required=True,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    default=True,
						    type=bool,
						    required=False,
						    help='If True, push model and image processor to the hub.',
						)

						lowerCAmelCase :  int      =      parser.parse_args()

						lowerCAmelCase :  Path      =      args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




