


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


snake_case_  :     Optional[int]							  =							get_logger(__name__)

# Datasets
snake_case_  :     Optional[int]							  =							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
snake_case_  :     str							  =							'''https://cdn-datasets.huggingface.co/datasets/datasets'''
snake_case_  :     List[str]							  =							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
snake_case_  :     Optional[int]							  =							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
snake_case_  :     List[Any]							  =							'''https://cdn-datasets.huggingface.co/datasets/metric'''
snake_case_  :     Tuple							  =							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
snake_case_  :     Dict							  =							os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
snake_case_  :     Dict							  =							HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
snake_case_  :     Optional[int]							  =							'''main'''

snake_case_  :     Union[str, Any]							  =							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
snake_case_  :     List[str]							  =							{'''1''', '''ON''', '''YES''', '''TRUE'''}
snake_case_  :     str							  =							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''dill'''))
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''pandas'''))
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''pyarrow'''))

snake_case_  :     Any							  =							os.environ.get('''USE_TF''', '''AUTO''').upper()
snake_case_  :     Dict							  =							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
snake_case_  :     int							  =							os.environ.get('''USE_JAX''', '''AUTO''').upper()

snake_case_  :     str							  =							'''N/A'''
snake_case_  :     Optional[int]							  =							False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
							snake_case_  :     int							  =							importlib.util.find_spec('''torch''') is not None
							if TORCH_AVAILABLE:
														try:
																					snake_case_  :     str							  =							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''')

snake_case_  :     int							  =							'''N/A'''
snake_case_  :     Dict							  =							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							snake_case_  :     List[str]							  =							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:
																												snake_case_  :     Union[str, Any]							  =							version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					snake_case_  :     Any							  =							False
							if TF_AVAILABLE:
														if TF_VERSION.major < 2:
																					logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																					snake_case_  :     Optional[Any]							  =							False
														else:
																					logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
							logger.info('''Disabling Tensorflow because USE_TORCH is set''')


snake_case_  :     Tuple							  =							'''N/A'''
snake_case_  :     Any							  =							False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							snake_case_  :     Union[str, Any]							  =							importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
							if JAX_AVAILABLE:
														try:
																					snake_case_  :     Union[str, Any]							  =							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''')


snake_case_  :     Union[str, Any]							  =							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
snake_case_  :     str							  =							'''N/A'''
snake_case_  :     List[Any]							  =							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														snake_case_  :     str							  =							version.parse(importlib_metadata.version('''apache_beam'''))
														snake_case_  :     str							  =							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
snake_case_  :     Optional[int]							  =							importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
snake_case_  :     List[Any]							  =							importlib.util.find_spec('''PIL''') is not None
snake_case_  :     Any							  =							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
snake_case_  :     Dict							  =							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
snake_case_  :     List[Any]							  =							importlib.util.find_spec('''rarfile''') is not None
snake_case_  :     Any							  =							importlib.util.find_spec('''zstandard''') is not None
snake_case_  :     List[Any]							  =							importlib.util.find_spec('''lz4''') is not None
snake_case_  :     List[str]							  =							importlib.util.find_spec('''py7zr''') is not None


# Cache location
snake_case_  :     Optional[int]							  =							'''~/.cache'''
snake_case_  :     Any							  =							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
snake_case_  :     List[Any]							  =							os.path.join(XDG_CACHE_HOME, '''huggingface''')
snake_case_  :     Dict							  =							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

snake_case_  :     Optional[Any]							  =							os.path.join(HF_CACHE_HOME, '''datasets''')
snake_case_  :     List[str]							  =							Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

snake_case_  :     Tuple							  =							os.path.join(HF_CACHE_HOME, '''metrics''')
snake_case_  :     Union[str, Any]							  =							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

snake_case_  :     Optional[Any]							  =							os.path.join(HF_CACHE_HOME, '''modules''')
snake_case_  :     int							  =							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

snake_case_  :     Any							  =							'''downloads'''
snake_case_  :     int							  =							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
snake_case_  :     List[Any]							  =							Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

snake_case_  :     str							  =							'''extracted'''
snake_case_  :     str							  =							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
snake_case_  :     List[Any]							  =							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
snake_case_  :     Union[str, Any]							  =							(
    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)
snake_case_  :     Union[str, Any]							  =							10_00

# Size of the preloaded record batch in `Dataset.__iter__`
snake_case_  :     List[Any]							  =							10

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

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

# Parquet configuration
snake_case_  :     List[str]							  =							1_00
snake_case_  :     Union[str, Any]							  =							1_00
snake_case_  :     Optional[int]							  =							1_00

# Offline mode
snake_case_  :     Optional[Any]							  =							os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
snake_case_  :     Optional[int]							  =							0  # Disabled
snake_case_  :     Optional[int]							  =							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
snake_case_  :     Optional[int]							  =							'''dataset.arrow'''
snake_case_  :     Tuple							  =							'''indices.arrow'''
snake_case_  :     str							  =							'''state.json'''
snake_case_  :     Optional[Any]							  =							'''dataset_info.json'''
snake_case_  :     List[Any]							  =							'''dataset_infos.json'''
snake_case_  :     Tuple							  =							'''LICENSE'''
snake_case_  :     Tuple							  =							'''metric_info.json'''
snake_case_  :     int							  =							'''dataset_dict.json'''

snake_case_  :     List[str]							  =							'''datasets_modules'''

snake_case_  :     str							  =							2_55

# Streaming
snake_case_  :     str							  =							20
snake_case_  :     str							  =							5

# Datasets without script
snake_case_  :     Union[str, Any]							  =							2_00
snake_case_  :     Any							  =							10
snake_case_  :     Optional[Any]							  =							2_00

# Progress bars
snake_case_  :     List[str]							  =							0.05  # 20 progress updates per sec



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_weight"].chunk(3		,dim=0)
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_bias"].chunk(3		,dim=0)

				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.out_proj.weight"]
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.out_proj.bias"]

				_SCREAMING_SNAKE_CASE     					=q_proj
				_SCREAMING_SNAKE_CASE     					=q_proj_bias

				_SCREAMING_SNAKE_CASE     					=k_proj
				_SCREAMING_SNAKE_CASE     					=k_proj_bias

				_SCREAMING_SNAKE_CASE     					=v_proj
				_SCREAMING_SNAKE_CASE     					=v_proj_bias

				_SCREAMING_SNAKE_CASE     					=out_proj_weights
				_SCREAMING_SNAKE_CASE     					=out_proj_bias







def   lowerCamelCase(				a__		,a__		,a__):
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_fc")
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_proj")







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.weight"].data
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.bias"].data







def   lowerCamelCase(				a__		,a__		,a__):
				# copy layer norms
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_1")
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_2")

				# copy MLP
				copy_mlp(hf_layer.mlp		,a__		,f"{prefix}.mlp")

				# copy attn
				copy_attn_layer(hf_layer.self_attn		,a__		,f"{prefix}.attn")







def   lowerCamelCase(				a__		,a__		,a__):
				for layer_id, hf_layer in enumerate(a__):
								copy_layer(a__		,a__		,f"{prefix}.{layer_id}")







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''text_projection'''].data.T

				# copy text encoder
				for name, param in hf_model.text_model.named_parameters():
								_SCREAMING_SNAKE_CASE     					=pt_weights[f"bert.{name}"].data







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.proj'''].data.T

				# copy layer norms
				copy_linear(hf_model.vision_model.pre_layrnorm		,a__		,'''visual.ln_pre''')
				copy_linear(hf_model.vision_model.post_layernorm		,a__		,'''visual.ln_post''')

				# copy embeddings
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.conv1.weight'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.class_embedding'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.positional_embedding'''].data

				# copy encoder
				copy_layers(hf_model.vision_model.encoder.layers		,a__		,'''visual.transformer.resblocks''')







@torch.no_grad()
def   lowerCamelCase(				a__		,a__		,a__=None):
				assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
				_SCREAMING_SNAKE_CASE     					=ChineseCLIPConfig.from_pretrained(a__)

				_SCREAMING_SNAKE_CASE     					=ChineseCLIPModel(a__).eval()

				_SCREAMING_SNAKE_CASE     					=torch.load(a__		,map_location='''cpu''')['''state_dict''']
				_SCREAMING_SNAKE_CASE     					={(name[7:] if name.startswith('''module.''') else name): value for name, value in pt_weights.items()}

				copy_text_model_and_projection(a__		,a__)
				copy_vision_model_and_projection(a__		,a__)
				_SCREAMING_SNAKE_CASE     					=pt_weights['''logit_scale'''].data

				hf_model.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :     Tuple							  =							argparse.ArgumentParser()
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    help='''Path to the output folder storing converted hf PyTorch model.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
							)
							parser.add_argument(
							    '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
							)
							snake_case_  :     List[str]							  =							parser.parse_args()

							convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
							print('''The conversion is finished!''')