

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		List[str]				    =				pt_attn_layer.in_proj_weight.chunk(3       ,					dim=0)
			a_		:		Any				    =				pt_attn_layer.in_proj_bias.chunk(3       ,					dim=0)

			a_		:		Union[str, Any]				    =				pt_attn_layer.out_proj.weight
			a_		:		List[str]				    =				pt_attn_layer.out_proj.bias

			a_		:		Any				    =				q_proj
			a_		:		List[Any]				    =				q_proj_bias

			a_		:		Any				    =				k_proj
			a_		:		List[str]				    =				k_proj_bias

			a_		:		Any				    =				v_proj
			a_		:		str				    =				v_proj_bias

			a_		:		int				    =				out_proj_weights
			a_		:		Optional[Any]				    =				out_proj_bias


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



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


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		Dict				    =				pt_linear.weight
			a_		:		List[Any]				    =				pt_linear.bias


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''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							(		a__       ,					a__):

			'''simple docstring'''



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


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		Optional[int]				    =				pt_model.token_embedding.weight
			a_		:		Dict				    =				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__):

			'''simple docstring'''



			a_		:		Any				    =				pt_model.text_projection.data.T

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


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				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_		:		Union[str, Any]				    =				pt_model.visual.conva.weight.data
			a_		:		Tuple				    =				pt_model.visual.class_embedding
			a_		:		List[str]				    =				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):

			'''simple docstring'''



			if config_path is not None:
						a_		:		Tuple				    =				CLIPConfig.from_pretrained(a__)
			else:
						a_		:		Dict				    =				CLIPConfig(projection_dim=5_1_2       ,					text_config={}       ,					vision_config={})

			a_		:		Tuple				    =				CLIPModel(a__).eval()

			a_		:		Tuple				    =				load(a__       ,					device="""cpu"""       ,					jit=a__)
			a_		:		Union[str, Any]				    =				pt_model.eval()

			copy_text_model_and_projection(a__       ,					a__)
			copy_vison_model_and_projection(a__       ,					a__)
			a_		:		Tuple				    =				pt_model.logit_scale

			a_		:		Dict				    =				torch.arange(0       ,					7_7).unsqueeze(0)
			a_		:		str				    =				torch.randn(1       ,					3       ,					2_2_4       ,					2_2_4)

			a_		:		Any				    =				hf_model(input_ids=a__       ,					pixel_values=a__       ,					return_dict=a__)
			a_		:		List[str]				    =				hf_outputs.logits_per_image
			a_		:		int				    =				hf_outputs.logits_per_text
			a_		:		Optional[Any]				    =				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__":
	__snake_case							:     Optional[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""")
	__snake_case							:     Any 						=	parser.parse_args()

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





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Dict				    =				torch.nn.Linear(2       ,					4)
			a_		:		int				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.OneCycleLR(a__       ,					max_lr=0.01       ,					steps_per_epoch=2       ,					epochs=1)
			a_    ,			a_    ,			a_		:		List[Any]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		int				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Tuple				    =				torch.nn.Linear(2       ,					4)
			a_		:		str				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.LambdaLR(a__       ,					lr_lambda=lambda a__: 1 - n / 1_0)
			a_    ,			a_    ,			a_		:		List[str]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			a_		:		Optional[Any]				    =				False
			scheduler.step()
			a_		:		Union[str, Any]				    =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

			# Optimizer has not stepped
			a_		:		Any				    =				True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						a_		:		Any				    =				1 - 2 / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''


def 			_UpperCAmelCase							(		a__ = 2):

			'''simple docstring'''



			from transformers import get_linear_schedule_with_warmup

			a_		:		int				    =				[1, 2, 4]
			for num_steps in steps:
						a_		:		List[str]				    =				GradientAccumulationPlugin(num_steps=a__       ,					adjust_scheduler=num_steps > 1)
						a_		:		List[str]				    =				Accelerator(gradient_accumulation_plugin=a__)
						a_		:		int				    =				torch.nn.Linear(2       ,					4)
						a_		:		Any				    =				torch.optim.AdamW(model.parameters()       ,					lr=10.0)
						a_		:		List[Any]				    =				get_linear_schedule_with_warmup(optimizer=a__       ,					num_warmup_steps=0       ,					num_training_steps=2_0)

						a_    ,			a_    ,			a_		:		int				    =				accelerator.prepare(a__       ,					a__       ,					a__)

						for i in range(1_0 * num_steps):
									with accelerator.accumulate(a__):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
						assert (
						    scheduler.get_last_lr()[0] == 0
						), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
						GradientState._reset_state()



@require_cpu
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				str:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						@require_huggingface_suite
						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1  )  )
									debug_launcher(_lowercase  )


