


'''simple docstring'''



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				Tuple   )->  List[str]:

										"""simple docstring"""




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




				@property
				def   a__(	self    :				List[str]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			1
										UpperCAmelCase         =			3
										UpperCAmelCase         =			(32, 32)

										UpperCAmelCase         =			floats_tensor((batch_size, num_channels) + sizes   ,					rng=random.Random(0   )   ).to(lowerCAmelCase   )
										return image




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

										"""simple docstring"""




										torch.manual_seed(0   )
										UpperCAmelCase         =			UNetaDConditionModel(
										    block_out_channels=(32, 64)   ,					layers_per_block=2   ,					sample_size=32   ,					in_channels=4   ,					out_channels=4   ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,					cross_attention_dim=32   ,					)
										return model




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

										"""simple docstring"""




										torch.manual_seed(0   )
										UpperCAmelCase         =			AutoencoderKL(
										    block_out_channels=[32, 64]   ,					in_channels=3   ,					out_channels=3   ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,					latent_channels=4   ,					)
										return model




				@property
				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										torch.manual_seed(0   )
										UpperCAmelCase         =			CLIPTextConfig(
										    bos_token_id=0   ,					eos_token_id=2   ,					hidden_size=32   ,					intermediate_size=37   ,					layer_norm_eps=1E-05   ,					num_attention_heads=4   ,					num_hidden_layers=5   ,					pad_token_id=1   ,					vocab_size=1000   ,					)
										return CLIPTextModel(lowerCAmelCase   )




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

										"""simple docstring"""




										def extract(*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Tuple   ):






																class  UpperCamelCase__:




																				def __init__(	self    :				Optional[int]   )->  Any:

																										"""simple docstring"""




																										UpperCAmelCase         =			torch.ones([0]   )





																				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				str   )->  str:

																										"""simple docstring"""




																										self.pixel_values.to(lowerCAmelCase   )
																										return self







																return Out()

										return extract




				def   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										UpperCAmelCase         =			self.dummy_cond_unet
										UpperCAmelCase         =			DDIMScheduler(
										    beta_start=0.00085   ,					beta_end=0.012   ,					beta_schedule='''scaled_linear'''   ,					clip_sample=lowerCAmelCase   ,					set_alpha_to_one=lowerCAmelCase   ,					)

										UpperCAmelCase         =			self.dummy_vae
										UpperCAmelCase         =			self.dummy_text_encoder
										UpperCAmelCase         =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase         =			StableDiffusionPipeline(
										    unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   ,					vae=lowerCAmelCase   ,					text_encoder=lowerCAmelCase   ,					tokenizer=lowerCAmelCase   ,					safety_checker=lowerCAmelCase   ,					feature_extractor=self.dummy_extractor   ,					)
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			'''A painting of a squirrel eating a burger'''

										UpperCAmelCase         =			torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
										UpperCAmelCase         =			sd_pipe([prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=6.0   ,					num_inference_steps=2   ,					output_type='''np'''   )
										UpperCAmelCase         =			output.images

										UpperCAmelCase         =			torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=6.0   ,					num_inference_steps=2   ,					output_type='''np'''   ,					return_dict=lowerCAmelCase   ,					)[0]

										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										UpperCAmelCase         =			np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]   )

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




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										UpperCAmelCase         =			self.dummy_cond_unet
										UpperCAmelCase         =			PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
										UpperCAmelCase         =			self.dummy_vae
										UpperCAmelCase         =			self.dummy_text_encoder
										UpperCAmelCase         =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase         =			StableDiffusionPipeline(
										    unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   ,					vae=lowerCAmelCase   ,					text_encoder=lowerCAmelCase   ,					tokenizer=lowerCAmelCase   ,					safety_checker=lowerCAmelCase   ,					feature_extractor=self.dummy_extractor   ,					)
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			'''A painting of a squirrel eating a burger'''
										UpperCAmelCase         =			torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
										UpperCAmelCase         =			sd_pipe([prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=6.0   ,					num_inference_steps=2   ,					output_type='''np'''   )

										UpperCAmelCase         =			output.images

										UpperCAmelCase         =			torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=6.0   ,					num_inference_steps=2   ,					output_type='''np'''   ,					return_dict=lowerCAmelCase   ,					)[0]

										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										UpperCAmelCase         =			np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]   )

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




				def   a__(	self    :				Any   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''   ,					safety_checker=lowerCAmelCase   )
										assert isinstance(lowerCAmelCase   ,					lowerCAmelCase   )
										assert isinstance(pipe.scheduler   ,					lowerCAmelCase   )
										assert pipe.safety_checker is None

										UpperCAmelCase         =			pipe('''example prompt'''   ,					num_inference_steps=2   ).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowerCAmelCase   )
																UpperCAmelCase         =			StableDiffusionPipeline.from_pretrained(lowerCAmelCase   )

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										UpperCAmelCase         =			pipe('''example prompt'''   ,					num_inference_steps=2   ).images[0]
										assert image is not None





				@unittest.skipIf(torch_device != '''cuda'''   ,					'''This test requires a GPU'''   )
				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.dummy_cond_unet
										UpperCAmelCase         =			PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
										UpperCAmelCase         =			self.dummy_vae
										UpperCAmelCase         =			self.dummy_text_encoder
										UpperCAmelCase         =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										# put models in fp16
										UpperCAmelCase         =			unet.half()
										UpperCAmelCase         =			vae.half()
										UpperCAmelCase         =			bert.half()

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase         =			StableDiffusionPipeline(
										    unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   ,					vae=lowerCAmelCase   ,					text_encoder=lowerCAmelCase   ,					tokenizer=lowerCAmelCase   ,					safety_checker=lowerCAmelCase   ,					feature_extractor=self.dummy_extractor   ,					)
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			'''A painting of a squirrel eating a burger'''
										UpperCAmelCase         =			sd_pipe([prompt]   ,					num_inference_steps=2   ,					output_type='''np'''   ).images

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







@nightly
@require_torch_gpu
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase         =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   ,					safety_checker=lowerCAmelCase   )
										UpperCAmelCase         =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			(
										    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
										    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
										    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
										    ''' children from bahnhof zoo, detailed '''
										)
										UpperCAmelCase         =			4003660346
										UpperCAmelCase         =			7

										# without safety guidance (sld_guidance_scale = 0)
										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=0   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

										assert image.shape == (1, 512, 512, 3)

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

										# without safety guidance (strong configuration)
										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=2000   ,					sld_warmup_steps=7   ,					sld_threshold=0.025   ,					sld_momentum_scale=0.5   ,					sld_mom_beta=0.7   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

										assert image.shape == (1, 512, 512, 3)

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




				def   a__(	self    :				Optional[int]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   ,					safety_checker=lowerCAmelCase   )
										UpperCAmelCase         =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			'''padme amidala taking a bath artwork, safe for work, no nudity'''
										UpperCAmelCase         =			2734971755
										UpperCAmelCase         =			7

										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=0   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

										assert image.shape == (1, 512, 512, 3)

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

										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=2000   ,					sld_warmup_steps=7   ,					sld_threshold=0.025   ,					sld_momentum_scale=0.5   ,					sld_mom_beta=0.7   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

										assert image.shape == (1, 512, 512, 3)

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





				def   a__(	self    :				Union[str, Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
										UpperCAmelCase         =			sd_pipe.to(lowerCAmelCase   )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			(
										    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
										    ''' leyendecker'''
										)
										UpperCAmelCase         =			1044355234
										UpperCAmelCase         =			12

										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=0   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-7

										UpperCAmelCase         =			torch.manual_seed(lowerCAmelCase   )
										UpperCAmelCase         =			sd_pipe(
										    [prompt]   ,					generator=lowerCAmelCase   ,					guidance_scale=lowerCAmelCase   ,					num_inference_steps=50   ,					output_type='''np'''   ,					width=512   ,					height=512   ,					sld_guidance_scale=2000   ,					sld_warmup_steps=7   ,					sld_threshold=0.025   ,					sld_momentum_scale=0.5   ,					sld_mom_beta=0.7   ,					)

										UpperCAmelCase         =			output.images
										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]   )
										assert image.shape == (1, 512, 512, 3)

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









'''simple docstring'''



import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    int   ,     A     :    List[str]   ,     A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			BigBirdConfig.from_json_file(A  )
						print(f"""Building PyTorch model from configuration: {config}"""  )

						if is_trivia_qa:
												UpperCAmelCase         =			BigBirdForQuestionAnswering(A  )
						else:
												UpperCAmelCase         =			BigBirdForPreTraining(A  )

						# Load weights from tf checkpoint
						load_tf_weights_in_big_bird(A   ,     A   ,     is_trivia_qa=A  )

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(A  )


if __name__ == "__main__":
	_lowercase    :    List[str]			     =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--big_bird_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained BERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
	)
	_lowercase    :    Any			     =  parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
	)





