

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






    '''simple docstring'''
    copy_linear(hf_mlp.fca    ,				pt_mlp.c_fc )
    copy_linear(hf_mlp.fca    ,				pt_mlp.c_proj )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Union[str, Any]:






    '''simple docstring'''
    __UpperCAmelCase			   =     pt_linear.weight
    __UpperCAmelCase			   =     pt_linear.bias


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






    '''simple docstring'''
    # 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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Union[str, 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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






    '''simple docstring'''
    # copy  embeds
    __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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Tuple:






    '''simple docstring'''
    # copy projection
    __UpperCAmelCase			   =     pt_model.text_projection.data.T

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    # copy projection
    __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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=None ) ->       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__":
       A_						:     List[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')
       A_						:     Union[str, Any]			  =				parser.parse_args()

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







from __future__ import annotations


def  __a    (				SCREAMING_SNAKE_CASE ) ->       list[int]:






    '''simple docstring'''
    if not list_of_ints:
        return []

    if min(SCREAMING_SNAKE_CASE ) < 0:
        raise ValueError('''All numbers must be positive''' )

    __UpperCAmelCase			   =     max(len(bin(SCREAMING_SNAKE_CASE )[2:] ) for x in list_of_ints )
    return _msd_radix_sort(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[int]:






    '''simple docstring'''
    if bit_position == 0 or len(SCREAMING_SNAKE_CASE ) in [0, 1]:
        return list_of_ints

    __UpperCAmelCase			   =     []
    __UpperCAmelCase			   =     []
    # Split numbers based on bit at bit_position from the right
    for number in list_of_ints:
        if (number >> (bit_position - 1)) & 1:
            # number has a one at bit bit_position
            ones.append(SCREAMING_SNAKE_CASE )
        else:
            # number has a zero at bit bit_position
            zeros.append(SCREAMING_SNAKE_CASE )

    # recursively split both lists further
    __UpperCAmelCase			   =     _msd_radix_sort(SCREAMING_SNAKE_CASE    ,				bit_position - 1 )
    __UpperCAmelCase			   =     _msd_radix_sort(SCREAMING_SNAKE_CASE    ,				bit_position - 1 )

    # recombine lists
    __UpperCAmelCase			   =     zeros
    res.extend(SCREAMING_SNAKE_CASE )

    return res


def  __a    (				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase			   =     len(SCREAMING_SNAKE_CASE )
    if not list_of_ints or length == 1:
        return

    if min(SCREAMING_SNAKE_CASE ) < 0:
        raise ValueError('''All numbers must be positive''' )

    __UpperCAmelCase			   =     max(len(bin(SCREAMING_SNAKE_CASE )[2:] ) for x in list_of_ints )
    _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				0    ,				SCREAMING_SNAKE_CASE )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if bit_position == 0 or end_index - begin_index <= 1:
        return

    bit_position -= 1

    __UpperCAmelCase			   =     begin_index
    __UpperCAmelCase			   =     end_index - 1
    while i <= j:
        __UpperCAmelCase			   =     False
        if not (list_of_ints[i] >> bit_position) & 1:
            # found zero at the beginning
            i += 1
            __UpperCAmelCase			   =     True
        if (list_of_ints[j] >> bit_position) & 1:
            # found one at the end
            j -= 1
            __UpperCAmelCase			   =     True

        if changed:
            continue

        __UpperCAmelCase   ,       __UpperCAmelCase			   =     list_of_ints[j], list_of_ints[i]
        j -= 1
        if j != i:
            i += 1

    _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
    _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
       import doctest

       doctest.testmod()




