






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[Any]:





						"""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       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



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


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase							   =     pt_linear.weight
						_UpperCAmelCase							   =     pt_linear.bias


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    List[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       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



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


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    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       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



						_UpperCAmelCase							   =     pt_model.text_projection.data.T

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


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    List[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       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=None      )->    List[Any]:





						"""simple docstring"""



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

						_UpperCAmelCase							   =     CLIPModel(__lowerCAmelCase      ).eval()

						_UpperCAmelCase   ,					_UpperCAmelCase							   =     load(__lowerCAmelCase					,						device='cpu'					,						jit=__lowerCAmelCase      )
						_UpperCAmelCase							   =     pt_model.eval()

						copy_text_model_and_projection(__lowerCAmelCase					,						__lowerCAmelCase      )
						copy_vison_model_and_projection(__lowerCAmelCase					,						__lowerCAmelCase      )
						_UpperCAmelCase							   =     pt_model.logit_scale

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

						_UpperCAmelCase							   =     hf_model(input_ids=__lowerCAmelCase					,						pixel_values=__lowerCAmelCase					,						return_dict=__lowerCAmelCase      )
						_UpperCAmelCase							   =     hf_outputs.logits_per_image
						_UpperCAmelCase							   =     hf_outputs.logits_per_text
						_UpperCAmelCase   ,					_UpperCAmelCase							   =     pt_model(__lowerCAmelCase					,						__lowerCAmelCase      )

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

						hf_model.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
		_a       =    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       =    parser.parse_args()

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








import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
