






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





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

	a		=			pt_attn_layer.out_proj.weight
	a		=			pt_attn_layer.out_proj.bias

	a		=			q_proj
	a		=			q_proj_bias

	a		=			k_proj
	a		=			k_proj_bias

	a		=			v_proj
	a		=			v_proj_bias

	a		=			out_proj_weights
	a		=			out_proj_bias





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Union[str, Any]:
	copy_linear(hf_mlp.fca,  pt_mlp.c_fc  )
	copy_linear(hf_mlp.fca,  pt_mlp.c_proj  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[str]:
	a		=			pt_linear.weight
	a		=			pt_linear.bias





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:
	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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Tuple:



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  int:
	a		=			pt_model.token_embedding.weight
	a		=			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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Tuple:
	a		=			pt_model.text_projection.data.T

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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:
	a		=			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
	a		=			pt_model.visual.conva.weight.data
	a		=			pt_model.visual.class_embedding
	a		=			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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_=None  )   ->  str:



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

	a		=			CLIPModel(snake_case_  ).eval()

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

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

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

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

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

	hf_model.save_pretrained(snake_case_  )


if __name__ == "__main__":
				UpperCamelCase__						:      Tuple								=					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__						:      Optional[Any]								=					parser.parse_args()

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__						:      Tuple								=					{
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					[
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						:      str								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





