




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


UpperCamelCase__						=		[nn.LayerNorm]

UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__						=		parsed_torch_version_base >= version.parse('2.0')
UpperCamelCase__						=		parsed_torch_version_base >= version.parse('1.12')
UpperCamelCase__						=		parsed_torch_version_base >= version.parse('1.11')
UpperCamelCase__						=		parsed_torch_version_base < version.parse('1.11')






def    snake_case   (				A		,  A		,  A		,  A		,  self     )				->				Any:




	'''simple docstring'''


	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(A		,  A		,  parent.dim		,  self     )
	else:
		return _softmax_backward_data(A		,  A		,  parent.dim		,  self.dtype     )






def    snake_case   (				A		,  A		,  A = 0     )				->				nn.Linear:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				index.to(layer.weight.device     )
	lowerCamelCase_				:						Dict				  =				layer.weight.index_select(A		,  A     ).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			lowerCamelCase_				:						List[str]				  =				layer.bias.clone().detach()
		else:
			lowerCamelCase_				:						List[Any]				  =				layer.bias[index].clone().detach()
	lowerCamelCase_				:						List[str]				  =				list(layer.weight.size()     )
	lowerCamelCase_				:						Union[str, Any]				  =				len(A     )
	lowerCamelCase_				:						str				  =				nn.Linear(new_size[1]		,  new_size[0]		,  bias=layer.bias is not None     ).to(layer.weight.device     )
	lowerCamelCase_				:						Optional[int]				  =				False
	new_layer.weight.copy_(W.contiguous()     )
	lowerCamelCase_				:						Tuple				  =				True
	if layer.bias is not None:
		lowerCamelCase_				:						Tuple				  =				False
		new_layer.bias.copy_(b.contiguous()     )
		lowerCamelCase_				:						Union[str, Any]				  =				True
	return new_layer


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						List[str]				  =				nf
						lowerCamelCase_				:						Any				  =				nn.Parameter(torch.empty(__lowercase					,			__lowercase   )   )
						lowerCamelCase_				:						List[Any]				  =				nn.Parameter(torch.zeros(__lowercase   )   )
						nn.init.normal_(self.weight					,			std=0.02   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				x.size()[:-1] + (self.nf,)
						lowerCamelCase_				:						Optional[Any]				  =				torch.addmm(self.bias					,			x.view(-1					,			x.size(-1   )   )					,			self.weight   )
						lowerCamelCase_				:						int				  =				x.view(__lowercase   )
						return x






def    snake_case   (				A		,  A		,  A = 1     )				->				ConvaD:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				index.to(layer.weight.device     )
	lowerCamelCase_				:						Dict				  =				layer.weight.index_select(A		,  A     ).clone().detach()
	if dim == 0:
		lowerCamelCase_				:						Tuple				  =				layer.bias.clone().detach()
	else:
		lowerCamelCase_				:						List[str]				  =				layer.bias[index].clone().detach()
	lowerCamelCase_				:						List[Any]				  =				list(layer.weight.size()     )
	lowerCamelCase_				:						Dict				  =				len(A     )
	lowerCamelCase_				:						Optional[Any]				  =				ConvaD(new_size[1]		,  new_size[0]     ).to(layer.weight.device     )
	lowerCamelCase_				:						Dict				  =				False
	new_layer.weight.copy_(W.contiguous()     )
	lowerCamelCase_				:						List[str]				  =				True
	lowerCamelCase_				:						List[str]				  =				False
	new_layer.bias.copy_(b.contiguous()     )
	lowerCamelCase_				:						Tuple				  =				True
	return new_layer






def    snake_case   (				A		,  A		,  A = None     )				->				Union[nn.Linear, ConvaD]:




	'''simple docstring'''


	if isinstance(A		,  nn.Linear     ):
		return prune_linear_layer(A		,  A		,  dim=0 if dim is None else dim     )
	elif isinstance(A		,  A     ):
		return prune_convad_layer(A		,  A		,  dim=1 if dim is None else dim     )
	else:
		raise ValueError(f"Can't prune layer of class {layer.__class__}"     )






def    snake_case   (				A		,  A		,  A		,  *A     )				->				torch.Tensor:




	'''simple docstring'''


	assert len(A     ) > 0, f"{input_tensors} has to be a tuple/list of tensors"

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	lowerCamelCase_				:						str				  =				len(inspect.signature(A     ).parameters     )
	if num_args_in_forward_chunk_fn != len(A     ):
		raise ValueError(
		    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A     )} input "
		    'tensors are given'     )

	if chunk_size > 0:
		lowerCamelCase_				:						List[Any]				  =				input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"All input tenors have to be of the same shape: {tensor_shape}, "
				    f"found shape {input_tensor.shape[chunk_dim]}"     )

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
			    f"size {chunk_size}"     )

		lowerCamelCase_				:						str				  =				input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		lowerCamelCase_				:						List[Any]				  =				tuple(input_tensor.chunk(A		,  dim=A     ) for input_tensor in input_tensors     )
		# apply forward fn to every tuple
		lowerCamelCase_				:						List[Any]				  =				tuple(forward_fn(*A     ) for input_tensors_chunk in zip(*A     )     )
		# concatenate output at same dimension
		return torch.cat(A		,  dim=A     )

	return forward_fn(*A     )






def    snake_case   (				A		,  A		,  A		,  A     )				->				Tuple[Set[int], torch.LongTensor]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				torch.ones(A		,  A     )
	lowerCamelCase_				:						List[Any]				  =				set(A     ) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		lowerCamelCase_				:						Tuple				  =				head - sum(1 if h < head else 0 for h in already_pruned_heads     )
		lowerCamelCase_				:						Optional[Any]				  =				0
	lowerCamelCase_				:						Union[str, Any]				  =				mask.view(-1     ).contiguous().eq(1     )
	lowerCamelCase_				:						torch.LongTensor				  =				torch.arange(len(A     )     )[mask].long()
	return heads, index






def    snake_case   (				*A		,  A = None     )				->				Tuple[torch.Tensor, ...]:




	'''simple docstring'''


	return torch.meshgrid(*A		,  indexing=A     )






def    snake_case   (				A     )				->				Tuple[torch.device, int, int]:




	'''simple docstring'''


	return tensor.device, storage_ptr(A     ), storage_size(A     )


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''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_			(				snake_case				: Optional[Any]			,   snake_case				: int			)   ->     Any:



						'''simple docstring'''

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




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''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 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

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




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''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 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

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




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''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 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase  	=      CLIPConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      CLIPConfig(projection_dim=512			,   text_config={}			,   vision_config={}			)

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

						__UpperCamelCase  	=      torch.arange(0			,   77			).unsqueeze(0			)
						__UpperCamelCase  	=      torch.randn(1			,   3			,   224			,   224			)

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      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__":
				lowercase__				:				Union[str, 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")
				lowercase__				:				str						=      parser.parse_args()

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




