




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__			,		a__			,		a__  			=							pt_attn_layer.in_proj_weight.chunk(3 ,     dim=0		)
    a__			,		a__			,		a__  			=							pt_attn_layer.in_proj_bias.chunk(3 ,     dim=0		)

    a__  			=							pt_attn_layer.out_proj.weight
    a__  			=							pt_attn_layer.out_proj.bias

    a__  			=							q_proj
    a__  			=							q_proj_bias

    a__  			=							k_proj
    a__  			=							k_proj_bias

    a__  			=							v_proj
    a__  			=							v_proj_bias

    a__  			=							out_proj_weights
    a__  			=							out_proj_bias
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    copy_linear(hf_mlp.fca ,     pt_mlp.c_fc		)
    copy_linear(hf_mlp.fca ,     pt_mlp.c_proj		)
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							pt_linear.weight
    a__  			=							pt_linear.bias
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    # copy layer norms
    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_ ,     UpperCAmelCase_		):
    for hf_layer, pt_layer in zip(UpperCAmelCase_ ,     UpperCAmelCase_		):
        copy_layer(UpperCAmelCase_ ,     UpperCAmelCase_		)
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    # copy  embeds
    a__  			=							pt_model.token_embedding.weight
    a__  			=							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_ ,     UpperCAmelCase_		):
    # copy projection
    a__  			=							pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model ,     UpperCAmelCase_		)
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    # copy projection
    a__  			=							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__  			=							pt_model.visual.conva.weight.data
    a__  			=							pt_model.visual.class_embedding
    a__  			=							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_ ,     UpperCAmelCase_ ,     UpperCAmelCase_=None		):
    if config_path is not None:
        a__  			=							CLIPConfig.from_pretrained(UpperCAmelCase_		)
    else:
        a__  			=							CLIPConfig(projection_dim=512 ,     text_config={} ,     vision_config={}		)

    a__  			=							CLIPModel(UpperCAmelCase_		).eval()

    a__			,		a__  			=							load(UpperCAmelCase_ ,     device="""cpu""" ,     jit=UpperCAmelCase_		)
    a__  			=							pt_model.eval()

    copy_text_model_and_projection(UpperCAmelCase_ ,     UpperCAmelCase_		)
    copy_vison_model_and_projection(UpperCAmelCase_ ,     UpperCAmelCase_		)
    a__  			=							pt_model.logit_scale

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

    a__  			=							hf_model(input_ids=UpperCAmelCase_ ,     pixel_values=UpperCAmelCase_ ,     return_dict=UpperCAmelCase_		)
    a__  			=							hf_outputs.logits_per_image
    a__  			=							hf_outputs.logits_per_text
    a__			,		a__  			=							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__":
 SCREAMING_SNAKE_CASE_    :       List[str]       =							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_    :       List[str]       =							parser.parse_args()

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












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 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     KandinskyVaaPriorEmbaEmbPipeline
     __UpperCAmelCase						:			str							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Dict							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Optional[Any]							=     [
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCAmelCase						:			Dict							=     False

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Any:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      int:






         """simple docstring"""
         return self.time_input_dim

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """simple docstring"""
         return 1_00

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
         return tokenizer

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							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(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[Any]:






         """simple docstring"""
         torch.manual_seed(0     )

         a__  			=							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         a__  			=							PriorTransformer(**A     )
         # 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
         a__  			=							nn.Parameter(torch.ones(model.clip_std.shape     )     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							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    ,					)

         a__  			=							CLIPVisionModelWithProjection(A     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPImageProcessor(
             crop_size=2_24    ,					do_center_crop=A    ,					do_normalize=A    ,					do_resize=A    ,					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 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_prior
         a__  			=							self.dummy_image_encoder
         a__  			=							self.dummy_text_encoder
         a__  			=							self.dummy_tokenizer
         a__  			=							self.dummy_image_processor

         a__  			=							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,					prediction_type="""sample"""    ,					num_train_timesteps=10_00    ,					clip_sample=A    ,					clip_sample_range=1_0.0    ,					)

         a__  			=							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tuple    ,					A			:							Optional[Any]=0     )      ->      Any:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							floats_tensor((1, 3, 64, 64)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]
         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )

         a__  			=							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							"""cpu"""

         a__  			=							self.get_dummy_components()

         a__  			=							self.pipeline_class(**A     )
         a__  			=							pipe.to(A     )

         pipe.set_progress_bar_config(disable=A     )

         a__  			=							pipe(**self.get_dummy_inputs(A     )     )
         a__  			=							output.image_embeds

         a__  			=							pipe(
             **self.get_dummy_inputs(A     )    ,					return_dict=A    ,					)[0]

         a__  			=							image[0, -10:]
         a__  			=							image_from_tuple[0, -10:]

         assert image.shape == (1, 32)

         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							True
         a__  			=							False

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






     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							False

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






