




'''simple docstring'''





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def   UpperCamelCase_(			snake_case							: str							,       snake_case							: List[str]  ):

							'''simple docstring'''






							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_(			snake_case							: int							,       snake_case							: Any  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: Union[str, Any]							,       snake_case							: List[Any]  ):

							'''simple docstring'''






							snake_case_			      =   pt_linear.weight
							snake_case_			      =   pt_linear.bias


def   UpperCamelCase_(			snake_case							: str							,       snake_case							: 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   UpperCamelCase_(			snake_case							: Tuple							,       snake_case							: Optional[Any]  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: List[str]							,       snake_case							: Union[str, Any]  ):

							'''simple docstring'''






							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_(			snake_case							: Any							,       snake_case							: Any  ):

							'''simple docstring'''






							snake_case_			      =   pt_model.text_projection.data.T

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


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: int  ):

							'''simple docstring'''






							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_(			snake_case							: Dict							,       snake_case							: List[str]							,       snake_case							: Optional[int]=None  ):

							'''simple docstring'''






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

							snake_case_			      =   CLIPModel(snake_case  ).eval()

							snake_case_      ,   snake_case_			      =   load(snake_case							,       device="cpu"							,       jit=snake_case  )
							snake_case_			      =   pt_model.eval()

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

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

							snake_case_			      =   hf_model(input_ids=snake_case							,       pixel_values=snake_case							,       return_dict=snake_case  )
							snake_case_			      =   hf_outputs.logits_per_image
							snake_case_			      =   hf_outputs.logits_per_text
							snake_case_      ,   snake_case_			      =   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__":
		_SCREAMING_SNAKE_CASE							:     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")
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				parser.parse_args()

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










'''simple docstring'''





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case  )
							np.random.seed(snake_case  )
							torch.manual_seed(snake_case  )
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case  )
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case  )
							else:
														torch.cuda.manual_seed_all(snake_case  )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case  )


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   torch.tensor(xm.get_rng_state()  )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							snake_case_			      =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														snake_case_			      =   rng_state.to(xm.xla_device()  )
														xm.collective_broadcast([rng_state]  )
														xm.mark_step()
														snake_case_			      =   rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														snake_case_			      =   rng_state.to(state.device  )
														torch.distributed.broadcast(snake_case							,       0  )
														snake_case_			      =   rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case							,       0  )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(snake_case  )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(snake_case  )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()  )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(snake_case  )




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case  )							,       generator=snake_case  )




