





"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =BertConfig.from_json_file(SCREAMING_SNAKE_CASE   )
							print(F'Building PyTorch model from configuration: {config}'   )
							__lowerCamelCase      :      Union[str, Any]         =BertForPreTraining(SCREAMING_SNAKE_CASE   )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'   )
							torch.save(model.state_dict()    ,						SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							_UpperCamelCase							=      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(
							    '--bert_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.'
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""simple docstring"""





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =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						,    )
											__lowerCamelCase      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =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						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      int         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''An astronaut riding an elephant''',
											    '''source_prompt''': '''An astronaut riding a horse''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''eta''': 0.1,
											    '''strength''': 0.8,
											    '''guidance_scale''': 3,
											    '''source_guidance_scale''': 1,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

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




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




				@unittest.skip('''non-deterministic pipeline'''			)
				def 					__lowercase  (     self  :Dict			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Tuple			):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image			).max() < 5e-1




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

											assert np.abs(image - expected_image			).max() < 2e-2




