





"""simple docstring"""




import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


UpperCamelCase_           =(30, 487)
UpperCamelCase_           =(36, 1_024)
UpperCamelCase_           =(100, 100)
UpperCamelCase_           =100

UpperCamelCase_           =datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

UpperCamelCase_      ,	UpperCamelCase_           =os.path.split(__file__)
UpperCamelCase_           =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						with ArrowWriter(features=_lowercase       ,						path=os.path.join(_lowercase       ,						'''beta.arrow'''   )   ) as writer:
												for key, record in dummy_data:
																		_UpperCamelCase			:					int     =    my_features.encode_example(_lowercase   )
																		writer.write(_lowercase   )
												_UpperCamelCase   ,							_UpperCamelCase			:					int     =    writer.finalize()





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						for _ in dataset:
												pass





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					str     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						dataset.set_format('''numpy'''   )
						for _ in dataset:
												pass





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Tuple     =    10
						_UpperCamelCase			:					int     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						for i in range(0       ,						len(_lowercase   )       ,						_lowercase   ):
												_UpperCamelCase			:					Dict     =    dataset[i : i + batch_size]





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    10
						_UpperCamelCase			:					List[str]     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						dataset.set_format('''numpy'''   )
						for i in range(0       ,						len(_lowercase   )       ,						_lowercase   ):
												_UpperCamelCase			:					Union[str, Any]     =    dataset[i : i + batch_size]





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Any     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						for col in feats:
												_UpperCamelCase			:					Tuple     =    dataset[col]





@get_duration
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Union[str, Any]     =    datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase       ,						'''beta.arrow'''   )       ,						info=datasets.DatasetInfo(features=_lowercase   )   )
						dataset.set_format('''numpy'''   )
						for col in feats:
												_UpperCamelCase			:					str     =    dataset[col]





def        a_				(							):
						_UpperCamelCase			:					List[Any]     =    {}
						_UpperCamelCase			:					List[Any]     =    (
						    read_unformated,
						    read_formatted_as_numpy,
						    read_batch_unformated,
						    read_batch_formatted_as_numpy,
						    read_col_unformated,
						    read_col_formatted_as_numpy,
						)
						with tempfile.TemporaryDirectory() as tmp_dir:
												_UpperCamelCase			:					List[str]     =    datasets.Features({'''image''': ArrayaD(_lowercase       ,						dtype='''float32'''   )}   )
												_UpperCamelCase			:					Union[str, Any]     =    generate_examples(features=_lowercase       ,						num_examples=_lowercase   )
												_UpperCamelCase			:					Any     =    write(_lowercase       ,						_lowercase       ,						_lowercase   )
												for read_func in read_functions:
																		_UpperCamelCase			:					List[str]     =    read_func(_lowercase       ,						_lowercase   )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
												# )
												_UpperCamelCase			:					Dict     =    datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''   )   )   )}   )
												_UpperCamelCase			:					Tuple     =    generate_examples(
												    features=_lowercase       ,						num_examples=_lowercase       ,						seq_shapes={'''image''': SPEED_TEST_SHAPE}   )
												_UpperCamelCase			:					Tuple     =    write(_lowercase       ,						_lowercase       ,						_lowercase   )
												for read_func in read_functions:
																		_UpperCamelCase			:					List[Any]     =    read_func(_lowercase       ,						_lowercase   )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
												# )
												_UpperCamelCase			:					List[str]     =    datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''   )   )}   )
												_UpperCamelCase			:					Union[str, Any]     =    generate_examples(
												    features=_lowercase       ,						num_examples=_lowercase       ,						seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,						)
												_UpperCamelCase			:					Optional[int]     =    write(_lowercase       ,						_lowercase       ,						_lowercase   )
												for read_func in read_functions:
																		_UpperCamelCase			:					str     =    read_func(_lowercase       ,						_lowercase   )

						with open(_lowercase       ,						'''wb'''   ) as f:
												f.write(json.dumps(_lowercase   ).encode('''utf-8'''   )   )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_array_xd()






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		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
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )
    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			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""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

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