






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def 				A	(			a_				,a_   )	->		Optional[Any]:
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   str									=pt_attn_layer.in_proj_weight.chunk(3				,dim=0   )
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Optional[int]									=pt_attn_layer.in_proj_bias.chunk(3				,dim=0   )

					__UpperCamelCase :   Dict									=pt_attn_layer.out_proj.weight
					__UpperCamelCase :   Tuple									=pt_attn_layer.out_proj.bias

					__UpperCamelCase :   str									=q_proj
					__UpperCamelCase :   List[str]									=q_proj_bias

					__UpperCamelCase :   List[Any]									=k_proj
					__UpperCamelCase :   str									=k_proj_bias

					__UpperCamelCase :   str									=v_proj
					__UpperCamelCase :   List[str]									=v_proj_bias

					__UpperCamelCase :   Optional[Any]									=out_proj_weights
					__UpperCamelCase :   List[Any]									=out_proj_bias






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






def 				A	(			a_				,a_   )	->		str:
					__UpperCamelCase :   Optional[Any]									=pt_linear.weight
					__UpperCamelCase :   Union[str, Any]									=pt_linear.bias






def 				A	(			a_				,a_   )	->		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 				A	(			a_				,a_   )	->		Tuple:
					for hf_layer, pt_layer in zip(a_				,a_   ):
										copy_layer(a_				,a_   )






def 				A	(			a_				,a_   )	->		Any:
					# copy  embeds
					__UpperCamelCase :   Optional[int]									=pt_model.token_embedding.weight
					__UpperCamelCase :   List[Any]									=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	(			a_				,a_   )	->		Dict:
					# copy projection
					__UpperCamelCase :   Union[str, Any]									=pt_model.text_projection.data.T

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






def 				A	(			a_				,a_   )	->		List[str]:
					# copy projection
					__UpperCamelCase :   Tuple									=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 :   Optional[int]									=pt_model.visual.conva.weight.data
					__UpperCamelCase :   Dict									=pt_model.visual.class_embedding
					__UpperCamelCase :   int									=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	(			a_				,a_				,a_=None   )	->		Tuple:
					if config_path is not None:
										__UpperCamelCase :   int									=CLIPConfig.from_pretrained(a_   )
					else:
										__UpperCamelCase :   Tuple									=CLIPConfig(projection_dim=512				,text_config={}				,vision_config={}   )

					__UpperCamelCase :   int									=CLIPModel(a_   ).eval()

					__UpperCamelCase						,							__UpperCamelCase :   List[Any]									=load(a_				,device='cpu'				,jit=a_   )
					__UpperCamelCase :   str									=pt_model.eval()

					copy_text_model_and_projection(a_				,a_   )
					copy_vison_model_and_projection(a_				,a_   )
					__UpperCamelCase :   Optional[Any]									=pt_model.logit_scale

					__UpperCamelCase :   Optional[Any]									=torch.arange(0				,77   ).unsqueeze(0   )
					__UpperCamelCase :   int									=torch.randn(1				,3				,224				,224   )

					__UpperCamelCase :   Optional[int]									=hf_model(input_ids=a_				,pixel_values=a_				,return_dict=a_   )
					__UpperCamelCase :   List[str]									=hf_outputs.logits_per_image
					__UpperCamelCase :   int									=hf_outputs.logits_per_text
					__UpperCamelCase						,							__UpperCamelCase :   Optional[Any]									=pt_model(a_				,a_   )

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

					hf_model.save_pretrained(a_   )


if __name__ == "__main__":
							A_			: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_			:Any	    =		parser.parse_args()

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









import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

