


"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=KandinskyInpaintPipeline
		lowerCamelCase__				=['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		lowerCamelCase__				=[
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		lowerCamelCase__				=[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		lowerCamelCase__				=False



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim * 4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1_00



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
			return tokenizer



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Any						=				MCLIPConfig(
			    numDims=self.cross_attention_dim				,       transformerDimensions=self.text_embedder_hidden_size				,       hidden_size=self.text_embedder_hidden_size				,       intermediate_size=37				,       num_attention_heads=4				,       num_hidden_layers=5				,       vocab_size=10_05				,       )

			__snake_case	:						List[Any]						=				MultilingualCLIP(a_					)
			__snake_case	:						Any						=				text_encoder.eval()

			return text_encoder



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Union[str, Any]						=				{
			    '''in_channels''': 9,
			    # Out channels is double in channels because predicts mean and variance
			    '''out_channels''': 8,
			    '''addition_embed_type''': '''text_image''',
			    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
			    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
			    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
			    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
			    '''layers_per_block''': 1,
			    '''encoder_hid_dim''': self.text_embedder_hidden_size,
			    '''encoder_hid_dim_type''': '''text_image_proj''',
			    '''cross_attention_dim''': self.cross_attention_dim,
			    '''attention_head_dim''': 4,
			    '''resnet_time_scale_shift''': '''scale_shift''',
			    '''class_embed_type''': None,
			}

			__snake_case	:						Union[str, Any]						=				UNetaDConditionModel(**a_					)
			return model



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return {
			    "block_out_channels": [32, 64],
			    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
			    "in_channels": 3,
			    "latent_channels": 4,
			    "layers_per_block": 1,
			    "norm_num_groups": 8,
			    "norm_type": "spatial",
			    "num_vq_embeddings": 12,
			    "out_channels": 3,
			    "up_block_types": [
			        "AttnUpDecoderBlock2D",
			        "UpDecoderBlock2D",
			    ],
			    "vq_embed_dim": 4,
			}



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Tuple						=				VQModel(**self.dummy_movq_kwargs					)
			return model



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.dummy_text_encoder
			__snake_case	:						Union[str, Any]						=				self.dummy_tokenizer
			__snake_case	:						Union[str, Any]						=				self.dummy_unet
			__snake_case	:						List[str]						=				self.dummy_movq

			__snake_case	:						Optional[int]						=				DDIMScheduler(
			    num_train_timesteps=10_00				,       beta_schedule='''linear'''				,       beta_start=0.0_0085				,       beta_end=0.012				,       clip_sample=a_				,       set_alpha_to_one=a_				,       steps_offset=1				,       prediction_type='''epsilon'''				,       thresholding=a_				,       )

			__snake_case	:						Tuple						=				{
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''unet''': unet,
			    '''scheduler''': scheduler,
			    '''movq''': movq,
			}

			return components



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

			__snake_case	:						int						=				floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(a_					)					).to(a_					)
			__snake_case	:						List[str]						=				floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(seed + 1					)					).to(a_					)
			# create init_image
			__snake_case	:						List[Any]						=				floats_tensor((1, 3, 64, 64)				,       rng=random.Random(a_					)					).to(a_					)
			__snake_case	:						List[Any]						=				image.cpu().permute(0				,       2				,       3				,       1					)[0]
			__snake_case	:						Union[str, Any]						=				Image.fromarray(np.uinta(a_					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
			# create mask
			__snake_case	:						List[str]						=				np.ones((64, 64)				,       dtype=np.floataa					)
			__snake_case	:						Any						=				0

			if str(a_					).startswith('''mps'''					):
				__snake_case	:						Optional[Any]						=				torch.manual_seed(a_					)
			else:
				__snake_case	:						Any						=				torch.Generator(device=a_					).manual_seed(a_					)
			__snake_case	:						Any						=				{
			    '''prompt''': '''horse''',
			    '''image''': init_image,
			    '''mask_image''': mask,
			    '''image_embeds''': image_embeds,
			    '''negative_image_embeds''': negative_image_embeds,
			    '''generator''': generator,
			    '''height''': 64,
			    '''width''': 64,
			    '''num_inference_steps''': 2,
			    '''guidance_scale''': 4.0,
			    '''output_type''': '''np''',
			}
			return inputs



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				'''cpu'''

			__snake_case	:						List[Any]						=				self.get_dummy_components()

			__snake_case	:						List[Any]						=				self.pipeline_class(**a_					)
			__snake_case	:						Optional[Any]						=				pipe.to(a_					)

			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				pipe(**self.get_dummy_inputs(a_					)					)
			__snake_case	:						Any						=				output.images

			__snake_case	:						str						=				pipe(
			    **self.get_dummy_inputs(a_					)				,       return_dict=a_				,       )[0]

			__snake_case	:						str						=				image[0, -3:, -3:, -1]
			__snake_case	:						Union[str, Any]						=				image_from_tuple[0, -3:, -3:, -1]

			print(f"""image.shape {image.shape}"""					)

			assert image.shape == (1, 64, 64, 3)

			__snake_case	:						Dict						=				np.array(
			    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

			assert (
			    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
			), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
			assert (
			    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
			), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



@slow
@require_torch_gpu
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

			__snake_case	:						int						=				load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
			__snake_case	:						Any						=				np.ones((7_68, 7_68)				,       dtype=np.floataa					)
			__snake_case	:						Optional[Any]						=				0

			__snake_case	:						Tuple						=				'''a hat'''

			__snake_case	:						Union[str, Any]						=				KandinskyPriorPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-prior'''				,       torch_dtype=torch.floataa					)
			pipe_prior.to(a_					)

			__snake_case	:						List[Any]						=				KandinskyInpaintPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-inpaint'''				,       torch_dtype=torch.floataa					)
			__snake_case	:						List[str]						=				pipeline.to(a_					)
			pipeline.set_progress_bar_config(disable=a_					)

			__snake_case	:						List[Any]						=				torch.Generator(device='''cpu'''					).manual_seed(0					)
			__snake_case	,							__snake_case	:						List[Any]						=				pipe_prior(
			    a_				,       generator=a_				,       num_inference_steps=5				,       negative_prompt=''''''				,       ).to_tuple()

			__snake_case	:						Dict						=				pipeline(
			    a_				,       image=a_				,       mask_image=a_				,       image_embeds=a_				,       negative_image_embeds=a_				,       generator=a_				,       num_inference_steps=1_00				,       height=7_68				,       width=7_68				,       output_type='''np'''				,       )

			__snake_case	:						Union[str, Any]						=				output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(a_				,       a_					)









"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_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(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





