






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 						__UpperCAmelCase      (					a_							,					a_) ->    Any:
							snake_case_      ,		snake_case_      ,		snake_case_           =							pt_attn_layer.in_proj_weight.chunk(3							,					dim=0)
							snake_case_      ,		snake_case_      ,		snake_case_           =							pt_attn_layer.in_proj_bias.chunk(3							,					dim=0)

							snake_case_           =							pt_attn_layer.out_proj.weight
							snake_case_           =							pt_attn_layer.out_proj.bias

							snake_case_           =							q_proj
							snake_case_           =							q_proj_bias

							snake_case_           =							k_proj
							snake_case_           =							k_proj_bias

							snake_case_           =							v_proj
							snake_case_           =							v_proj_bias

							snake_case_           =							out_proj_weights
							snake_case_           =							out_proj_bias





def 						__UpperCAmelCase      (					a_							,					a_) ->    Tuple:
							copy_linear(hf_mlp.fca							,					pt_mlp.c_fc)
							copy_linear(hf_mlp.fca							,					pt_mlp.c_proj)





def 						__UpperCAmelCase      (					a_							,					a_) ->    List[Any]:
							snake_case_           =							pt_linear.weight
							snake_case_           =							pt_linear.bias





def 						__UpperCAmelCase      (					a_							,					a_) ->    Optional[Any]:
							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 						__UpperCAmelCase      (					a_							,					a_) ->    Tuple:





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





def 						__UpperCAmelCase      (					a_							,					a_) ->    Any:
							snake_case_           =							pt_model.token_embedding.weight
							snake_case_           =							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 						__UpperCAmelCase      (					a_							,					a_) ->    List[Any]:
							snake_case_           =							pt_model.text_projection.data.T

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





def 						__UpperCAmelCase      (					a_							,					a_) ->    int:
							snake_case_           =							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
							snake_case_           =							pt_model.visual.conva.weight.data
							snake_case_           =							pt_model.visual.class_embedding
							snake_case_           =							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 						__UpperCAmelCase      (					a_							,					a_							,					a_=None) ->    Optional[int]:





							if config_path is not None:
														snake_case_           =							CLIPConfig.from_pretrained(A_)
							else:
														snake_case_           =							CLIPConfig(projection_dim=5_12							,					text_config={}							,					vision_config={})

							snake_case_           =							CLIPModel(A_).eval()

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

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

							snake_case_           =							torch.arange(0							,					77).unsqueeze(0)
							snake_case_           =							torch.randn(1							,					3							,					2_24							,					2_24)

							snake_case_           =							hf_model(input_ids=A_							,					pixel_values=A_							,					return_dict=A_)
							snake_case_           =							hf_outputs.logits_per_image
							snake_case_           =							hf_outputs.logits_per_text
							snake_case_      ,		snake_case_           =							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__":
	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)













from __future__ import annotations

from typing import Any





def 						__UpperCAmelCase      (					*a_):
							if all(_check_not_integer(a_) for m in matrix_s):
														for i in matrix_s[1:]:
																					_verify_matrix_sizes(matrix_s[0]							,					a_)
														return [[sum(a_) for t in zip(*a_)] for m in zip(*a_)]
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_):
							if (
							    _check_not_integer(a_)
							    and _check_not_integer(a_)
							    and _verify_matrix_sizes(a_							,					a_)
							):
														return [[i - j for i, j in zip(*a_)] for m in zip(a_							,					a_)]
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_):
							return [[x * n for x in row] for row in matrix]





def 						__UpperCAmelCase      (					a_							,					a_):
							if _check_not_integer(a_) and _check_not_integer(a_):
														snake_case_      ,		snake_case_           =							_verify_matrix_sizes(a_							,					a_)

							if cols[0] != rows[1]:
														snake_case_           =							(
														    'Cannot multiply matrix of dimensions '
														    f'''({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'''
														)
														raise ValueError(a_)
							return [
							    [sum(m * n for m, n in zip(a_							,					a_)) for j in zip(*a_)] for i in matrix_a
							]





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							int(a_)
							return [[int(row == column) for column in range(a_)] for row in range(a_)]





def 						__UpperCAmelCase      (					a_							,					a_ = True):
							if _check_not_integer(a_):
														if return_map:
																					return map(a_							,					zip(*a_))
														else:
																					return list(map(a_							,					zip(*a_)))
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							matrix[:row] + matrix[row + 1 :]
							return [row[:column] + row[column + 1 :] for row in minor]





def 						__UpperCAmelCase      (					a_):
							if len(a_) == 1:
														return matrix[0][0]

							return sum(
							    x * determinant(minor(a_							,					0							,					a_)) * (-1) ** i
							    for i, x in enumerate(matrix[0]))





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							determinant(a_)
							if det == 0:
														return None

							snake_case_           =							[
							    [determinant(minor(a_							,					a_							,					a_)) for j in range(len(a_))]
							    for i in range(len(a_))
							]

							snake_case_           =							[
							    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])]
							    for row in range(len(a_))
							]
							snake_case_           =							list(transpose(a_))
							return scalar_multiply(a_							,					1 / det)





def 						__UpperCAmelCase      (					a_):
							return not isinstance(a_							,					a_) and not isinstance(matrix[0]							,					a_)





def 						__UpperCAmelCase      (					a_):
							return len(a_), len(matrix[0])





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							_shape(a_) + _shape(a_)
							if shape[0] != shape[3] or shape[1] != shape[2]:
														snake_case_           =							(
														    'operands could not be broadcast together with shape '
														    f'''({shape[0], shape[1]}), ({shape[2], shape[3]})'''
														)
														raise ValueError(a_)
							return (shape[0], shape[2]), (shape[1], shape[3])





def 						__UpperCAmelCase      (					):
							snake_case_           =							[[12, 10], [3, 9]]
							snake_case_           =							[[3, 4], [7, 4]]
							snake_case_           =							[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
							snake_case_           =							[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
							print(f'''Add Operation, {add(a_							,					a_) = } \n''')
							print(
							    f'''Multiply Operation, {multiply(a_							,					a_) = } \n'''							,					)
							print(f'''Identity: {identity(5)}\n''')
							print(f'''Minor of {matrix_c} = {minor(a_							,					1							,					2)} \n''')
							print(f'''Determinant of {matrix_b} = {determinant(a_)} \n''')
							print(f'''Inverse of {matrix_d} = {inverse(a_)}\n''')


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





