

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 						lowercase     (					_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	KandinskyVaaPriorEmbaEmbPipeline
		a__       							=	["prompt", "image"]
		a__       							=	["prompt", "image"]
		a__       							=	[
		    "num_images_per_prompt",
		    "strength",
		    "generator",
		    "num_inference_steps",
		    "latents",
		    "negative_prompt",
		    "guidance_scale",
		    "output_type",
		    "return_dict",
		]
		a__       							=	False


		@property
		def 		A__       (					self):
			return 32


		@property
		def 		A__       (					self):
			return 32


		@property
		def 		A__       (					self):
			return self.time_input_dim


		@property
		def 		A__       (					self):
			return self.time_input_dim * 4


		@property
		def 		A__       (					self):
			return 1_00


		@property
		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
			return tokenizer


		@property
		def 		A__       (					self):
			torch.manual_seed(0)
			_UpperCamelCase :		List[str]								=    CLIPTextConfig(
			    bos_token_id=0						,							eos_token_id=2						,							hidden_size=self.text_embedder_hidden_size						,							projection_dim=self.text_embedder_hidden_size						,							intermediate_size=37						,							layer_norm_eps=1e-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=10_00						,							)
			return CLIPTextModelWithProjection(__snake_case)


		@property
		def 		A__       (					self):
			torch.manual_seed(0)

			_UpperCamelCase :		str								=    {
			    'num_attention_heads': 2,
			    'attention_head_dim': 12,
			    'embedding_dim': self.text_embedder_hidden_size,
			    'num_layers': 1,
			}

			_UpperCamelCase :		List[str]								=    PriorTransformer(**__snake_case)
			# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
			_UpperCamelCase :		Optional[Any]								=    nn.Parameter(torch.ones(model.clip_std.shape))
			return model


		@property
		def 		A__       (					self):
			torch.manual_seed(0)
			_UpperCamelCase :		str								=    CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size						,							image_size=2_24						,							projection_dim=self.text_embedder_hidden_size						,							intermediate_size=37						,							num_attention_heads=4						,							num_channels=3						,							num_hidden_layers=5						,							patch_size=14						,							)

			_UpperCamelCase :		Optional[int]								=    CLIPVisionModelWithProjection(__snake_case)
			return model


		@property
		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    CLIPImageProcessor(
			    crop_size=2_24						,							do_center_crop=__snake_case						,							do_normalize=__snake_case						,							do_resize=__snake_case						,							image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]						,							image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]						,							resample=3						,							size=2_24						,							)

			return image_processor


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    self.dummy_prior
			_UpperCamelCase :		str								=    self.dummy_image_encoder
			_UpperCamelCase :		Optional[int]								=    self.dummy_text_encoder
			_UpperCamelCase :		Any								=    self.dummy_tokenizer
			_UpperCamelCase :		int								=    self.dummy_image_processor

			_UpperCamelCase :		Dict								=    UnCLIPScheduler(
			    variance_type='fixed_small_log'						,							prediction_type='sample'						,							num_train_timesteps=10_00						,							clip_sample=__snake_case						,							clip_sample_range=1_0.0						,							)

			_UpperCamelCase :		Optional[int]								=    {
			    'prior': prior,
			    'image_encoder': image_encoder,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'scheduler': scheduler,
			    'image_processor': image_processor,
			}

			return components


		def 		A__       (					self						,							__snake_case						,							__snake_case=0):
			if str(__snake_case).startswith('mps'):
				_UpperCamelCase :		Any								=    torch.manual_seed(__snake_case)
			else:
				_UpperCamelCase :		Any								=    torch.Generator(device=__snake_case).manual_seed(__snake_case)

			_UpperCamelCase :		Dict								=    floats_tensor((1, 3, 64, 64)						,							rng=random.Random(__snake_case)).to(__snake_case)
			_UpperCamelCase :		str								=    image.cpu().permute(0						,							2						,							3						,							1)[0]
			_UpperCamelCase :		int								=    Image.fromarray(np.uinta(__snake_case)).convert('RGB').resize((2_56, 2_56))

			_UpperCamelCase :		Optional[Any]								=    {
			    'prompt': 'horse',
			    'image': init_image,
			    'strength': 0.5,
			    'generator': generator,
			    'guidance_scale': 4.0,
			    'num_inference_steps': 2,
			    'output_type': 'np',
			}
			return inputs


		def 		A__       (					self):
			_UpperCamelCase :		Optional[int]								=    'cpu'

			_UpperCamelCase :		Tuple								=    self.get_dummy_components()

			_UpperCamelCase :		Dict								=    self.pipeline_class(**__snake_case)
			_UpperCamelCase :		Any								=    pipe.to(__snake_case)

			pipe.set_progress_bar_config(disable=__snake_case)

			_UpperCamelCase :		Optional[Any]								=    pipe(**self.get_dummy_inputs(__snake_case))
			_UpperCamelCase :		Union[str, Any]								=    output.image_embeds

			_UpperCamelCase :		Dict								=    pipe(
			    **self.get_dummy_inputs(__snake_case)						,							return_dict=__snake_case						,							)[0]

			_UpperCamelCase :		str								=    image[0, -10:]
			_UpperCamelCase :		Any								=    image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			_UpperCamelCase :		Optional[Any]								=    np.array(
			    [
			        0.1_0_7_1_2_8_4,
			        1.3_3_3_0_2_7_1,
			        0.6_1_2_6_0_2_2_3,
			        -0.6_6_9_1_0_6_5,
			        -0.3_8_4_6_8_5_2,
			        -1.0_3_0_3_6_6_1,
			        0.2_2_7_1_6_1_1_1,
			        0.0_3_3_4_8_9_0_1,
			        0.3_0_0_4_0_6_7_5,
			        -0.2_4_8_0_5_0_2_9,
			    ])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


		@skip_mps
		def 		A__       (					self):
			_UpperCamelCase :		str								=    torch_device == 'cpu'
			_UpperCamelCase :		List[Any]								=    True
			_UpperCamelCase :		str								=    False

			self._test_inference_batch_single_identical(
			    test_max_difference=__snake_case						,							relax_max_difference=__snake_case						,							test_mean_pixel_difference=__snake_case						,							)






		@skip_mps
		def 		A__       (					self):
			_UpperCamelCase :		int								=    torch_device == 'cpu'
			_UpperCamelCase :		Dict								=    False

			self._test_attention_slicing_forward_pass(
			    test_max_difference=__snake_case						,							test_mean_pixel_difference=__snake_case						,							)







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''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        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    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 :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		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        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




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

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)
	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)

	hf_model.save_pretrained(UpperCAmelCase_						)


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

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




