"""simple docstring"""


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__UpperCamelCase										=     get_logger(__name__)

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

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

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

__UpperCamelCase										=     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
__UpperCamelCase										=     {'''1''', '''ON''', '''YES''', '''TRUE'''}
__UpperCamelCase										=     ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


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

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

__UpperCamelCase										=     '''N/A'''
__UpperCamelCase										=     False

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

__UpperCamelCase										=     '''N/A'''
__UpperCamelCase										=     False

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


__UpperCamelCase										=     '''N/A'''
__UpperCamelCase										=     False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    __UpperCamelCase										=     importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
    if JAX_AVAILABLE:
        try:
            __UpperCamelCase										=     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''')


__UpperCamelCase										=     os.environ.get('''USE_BEAM''', '''AUTO''').upper()
__UpperCamelCase										=     '''N/A'''
__UpperCamelCase										=     False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        __UpperCamelCase										=     version.parse(importlib_metadata.version('''apache_beam'''))
        __UpperCamelCase										=     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
__UpperCamelCase										=     importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
__UpperCamelCase										=     importlib.util.find_spec('''PIL''') is not None
__UpperCamelCase										=     importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
__UpperCamelCase										=     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
__UpperCamelCase										=     importlib.util.find_spec('''rarfile''') is not None
__UpperCamelCase										=     importlib.util.find_spec('''zstandard''') is not None
__UpperCamelCase										=     importlib.util.find_spec('''lz4''') is not None
__UpperCamelCase										=     importlib.util.find_spec('''py7zr''') is not None


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

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

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

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

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

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

# Download count for the website
__UpperCamelCase										=     (
    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)
__UpperCamelCase										=     1000

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

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

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

# Parquet configuration
__UpperCamelCase										=     100
__UpperCamelCase										=     100
__UpperCamelCase										=     100

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

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

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

__UpperCamelCase										=     '''datasets_modules'''

__UpperCamelCase										=     255

# Streaming
__UpperCamelCase										=     20
__UpperCamelCase										=     5

# Datasets without script
__UpperCamelCase										=     200
__UpperCamelCase										=     10
__UpperCamelCase										=     200

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


"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # copy layer norms
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )

     # copy MLP
     copy_mlp(hf_layer.mlp		,   pt_layer.mlp   )

     # copy attn
     copy_attn_layer(hf_layer.self_attn		,   pt_layer.attn   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   pt_model.positional_embedding

     # copy layer norm
     copy_linear(hf_encoder.final_layer_norm		,   pt_model.ln_final   )

     # copy hidden layers
     copy_layers(hf_encoder.encoder.layers		,   pt_model.transformer.resblocks   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   pt_model.visual.proj.data.T

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

     # copy embeds
     snake_case_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   pt_model.visual.positional_embedding.data

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







@torch.no_grad()
def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )
     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )

     hf_model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
    __UpperCamelCase										=     argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    __UpperCamelCase										=     parser.parse_args()

    convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

