

"""simple docstring"""






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->List[str]:
			__lowercase      ,				__lowercase      ,				__lowercase						      =					pt_attn_layer.in_proj_weight.chunk(3 ,      dim=0			)
			__lowercase      ,				__lowercase      ,				__lowercase						      =					pt_attn_layer.in_proj_bias.chunk(3 ,      dim=0			)

			__lowercase						      =					pt_attn_layer.out_proj.weight
			__lowercase						      =					pt_attn_layer.out_proj.bias

			__lowercase						      =					q_proj
			__lowercase						      =					q_proj_bias

			__lowercase						      =					k_proj
			__lowercase						      =					k_proj_bias

			__lowercase						      =					v_proj
			__lowercase						      =					v_proj_bias

			__lowercase						      =					out_proj_weights
			__lowercase						      =					out_proj_bias



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Optional[Any]:
			copy_linear(hf_mlp.fca ,      pt_mlp.c_fc			)
			copy_linear(hf_mlp.fca ,      pt_mlp.c_proj			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->int:
			__lowercase						      =					pt_linear.weight
			__lowercase						      =					pt_linear.bias



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->int:
			# 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     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->str:
			for hf_layer, pt_layer in zip(__magic_name__ ,      __magic_name__			):
						copy_layer(__magic_name__ ,      __magic_name__			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Optional[Any]:
			# copy  embeds
			__lowercase						      =					pt_model.token_embedding.weight
			__lowercase						      =					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__ (		__magic_name__ ,      __magic_name__			)	->Any:
			# copy projection
			__lowercase						      =					pt_model.text_projection.data.T

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Any:
			# copy projection
			__lowercase						      =					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
			__lowercase						      =					pt_model.visual.conva.weight.data
			__lowercase						      =					pt_model.visual.class_embedding
			__lowercase						      =					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__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=None			)	->str:
			if config_path is not None:
						__lowercase						      =					CLIPConfig.from_pretrained(__magic_name__			)
			else:
						__lowercase						      =					CLIPConfig(projection_dim=5_1_2 ,      text_config={} ,      vision_config={}			)

			__lowercase						      =					CLIPModel(__magic_name__			).eval()

			__lowercase      ,				__lowercase						      =					load(__magic_name__ ,      device="cpu" ,      jit=__magic_name__			)
			__lowercase						      =					pt_model.eval()

			copy_text_model_and_projection(__magic_name__ ,      __magic_name__			)
			copy_vison_model_and_projection(__magic_name__ ,      __magic_name__			)
			__lowercase						      =					pt_model.logit_scale

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

			__lowercase						      =					hf_model(input_ids=__magic_name__ ,      pixel_values=__magic_name__ ,      return_dict=__magic_name__			)
			__lowercase						      =					hf_outputs.logits_per_image
			__lowercase						      =					hf_outputs.logits_per_text
			__lowercase      ,				__lowercase						      =					pt_model(__magic_name__ ,      __magic_name__			)

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

			hf_model.save_pretrained(__magic_name__			)


if __name__ == "__main__":
					_lowercase							 =							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''')
					_lowercase							 =							parser.parse_args()

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







"""simple docstring"""






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


_lowercase							 =							get_logger(__name__)



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





