





import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			lowerCAmelCase__( __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    KandinskyVaaPriorPipeline
       __snake_case 			=    ['prompt']
       __snake_case 			=    ['prompt', 'negative_prompt']
       __snake_case 			=    [
           'num_images_per_prompt',
           'generator',
           'num_inference_steps',
           'latents',
           'negative_prompt',
           'guidance_scale',
           'output_type',
           'return_dict',
       ]
       __snake_case 			=    False




       @property
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            return 3_2




       @property
       def     UpperCamelCase_			(      self							)			->     int:
            return 3_2




       @property
       def     UpperCamelCase_			(      self							)			->     Any:
            return self.time_input_dim




       @property
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            return self.time_input_dim * 4




       @property
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            return 1_0_0




       @property
       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	int						    =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
            return tokenizer




       @property
       def     UpperCamelCase_			(      self							)			->     Dict:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	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=3_7    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=1_0_0_0    ,						)
            return CLIPTextModelWithProjection(__lowerCamelCase							)




       @property
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            torch.manual_seed(0							)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{
                "num_attention_heads": 2,
                "attention_head_dim": 1_2,
                "embedding_dim": self.text_embedder_hidden_size,
                "num_layers": 1,
            }

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							PriorTransformer(**__lowerCamelCase							)
            # 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
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							nn.Parameter(torch.ones(model.clip_std.shape							)							)
            return model




       @property
       def     UpperCamelCase_			(      self							)			->     List[str]:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	str						    =							CLIPVisionConfig(
                hidden_size=self.text_embedder_hidden_size    ,						image_size=2_2_4    ,						projection_dim=self.text_embedder_hidden_size    ,						intermediate_size=3_7    ,						num_attention_heads=4    ,						num_channels=3    ,						num_hidden_layers=5    ,						patch_size=1_4    ,						)

            _SCREAMING_SNAKE_CASE       :	str						    =							CLIPVisionModelWithProjection(__lowerCamelCase							)
            return model




       @property
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							CLIPImageProcessor(
                crop_size=2_2_4    ,						do_center_crop=__lowerCamelCase    ,						do_normalize=__lowerCamelCase    ,						do_resize=__lowerCamelCase    ,						image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,						image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,						resample=3    ,						size=2_2_4    ,						)

            return image_processor




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.dummy_prior
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.dummy_image_encoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.dummy_text_encoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.dummy_tokenizer
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.dummy_image_processor

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							UnCLIPScheduler(
                variance_type="fixed_small_log"    ,						prediction_type="sample"    ,						num_train_timesteps=1_0_0_0    ,						clip_sample=__lowerCamelCase    ,						clip_sample_range=10.0    ,						)

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

            return components




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=0							)			->     Optional[Any]:
            if str(__lowerCamelCase							).startswith("mps"							):
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.manual_seed(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							{
                "prompt": "horse",
                "generator": generator,
                "guidance_scale": 4.0,
                "num_inference_steps": 2,
                "output_type": "np",
            }
            return inputs




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"cpu"

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_dummy_components()

            _SCREAMING_SNAKE_CASE       :	int						    =							self.pipeline_class(**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							pipe.to(__lowerCamelCase							)

            pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							pipe(**self.get_dummy_inputs(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							output.image_embeds

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							pipe(
                **self.get_dummy_inputs(__lowerCamelCase							)    ,						return_dict=__lowerCamelCase    ,						)[0]

            _SCREAMING_SNAKE_CASE       :	Dict						    =							image[0, -1_0:]
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							image_from_tuple[0, -1_0:]

            assert image.shape == (1, 3_2)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							np.array(
                [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

            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     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							torch_device == "cpu"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							True
            _SCREAMING_SNAKE_CASE       :	int						    =							False

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







       @skip_mps
       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch_device == "cpu"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							False

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






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

     # Load weights from numpy
     load_tf_weights_in_gpta(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
     torch.save(model.state_dict(),       __lowerCamelCase	)
     print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
     with open(__lowerCamelCase,       "w",       encoding="utf-8"	) as f:
          f.write(config.to_json_string()	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--gpt2_config_file',
           default='',
           type=str,
           help=(
               'An optional config json file corresponding to the pre-trained OpenAI model. \n'
               'This specifies the model architecture.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)