



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class lowerCAmelCase_       (   A_ ,A_ ,A_ ,unittest.TestCase			):







	'''simple docstring'''




	A_       :     Optional[int]									=		StableDiffusionModelEditingPipeline
	A_       :     Optional[Any]									=		TEXT_TO_IMAGE_PARAMS
	A_       :     Union[str, Any]									=		TEXT_TO_IMAGE_BATCH_PARAMS
	A_       :     List[str]									=		TEXT_TO_IMAGE_IMAGE_PARAMS
	A_       :     Tuple									=		TEXT_TO_IMAGE_IMAGE_PARAMS

	def 				_A			(	self       ):



				'''simple docstring'''


				torch.manual_seed(0       )
				a__					=  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   ,       )
				a__					=  DDIMScheduler()
				torch.manual_seed(0       )
				a__					=  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       )
				a__					=  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   ,       )
				a__					=  CLIPTextModel(lowerCamelCase       )
				a__					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

				a__					=  {
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase=0       ):



				'''simple docstring'''


				a__					=  torch.manual_seed(lowerCamelCase       )
				a__					=  {
				    """prompt""": """A field of roses""",
				    """generator""": generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    """height""": None,
				    """width""": None,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 6.0,
				    """output_type""": """numpy""",
				}
				return inputs

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
				a__					=  self.get_dummy_components()
				a__					=  StableDiffusionModelEditingPipeline(**lowerCamelCase       )
				a__					=  sd_pipe.to(lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

				a__					=  self.get_dummy_inputs(lowerCamelCase       )
				a__					=  sd_pipe(**lowerCamelCase       ).images
				a__					=  image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				a__					=  np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
				a__					=  self.get_dummy_components()
				a__					=  StableDiffusionModelEditingPipeline(**lowerCamelCase       )
				a__					=  sd_pipe.to(lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

				a__					=  self.get_dummy_inputs(lowerCamelCase       )
				a__					=  """french fries"""
				a__					=  sd_pipe(**lowerCamelCase   ,       negative_prompt=lowerCamelCase       )
				a__					=  output.images
				a__					=  image[0, -3:, -3:, -1]

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

				a__					=  np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
				a__					=  self.get_dummy_components()
				a__					=  EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,       beta_end=0.0_1_2   ,       beta_schedule="""scaled_linear"""       )
				a__					=  StableDiffusionModelEditingPipeline(**lowerCamelCase       )
				a__					=  sd_pipe.to(lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

				a__					=  self.get_dummy_inputs(lowerCamelCase       )
				a__					=  sd_pipe(**lowerCamelCase       ).images
				a__					=  image[0, -3:, -3:, -1]

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

				a__					=  np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
				a__					=  self.get_dummy_components()
				a__					=  PNDMScheduler()
				a__					=  StableDiffusionModelEditingPipeline(**lowerCamelCase       )
				a__					=  sd_pipe.to(lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

				a__					=  self.get_dummy_inputs(lowerCamelCase       )
				# the pipeline does not expect pndm so test if it raises error.
				with self.assertRaises(lowerCamelCase       ):
							a__					=  sd_pipe(**lowerCamelCase       ).images

	def 				_A			(	self       ):



				'''simple docstring'''


				super().test_inference_batch_single_identical(expected_max_diff=5e-3       )







	def 				_A			(	self       ):



				'''simple docstring'''


				super().test_attention_slicing_forward_pass(expected_max_diff=5e-3       )


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







	'''simple docstring'''




	def 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase=0       ):



				'''simple docstring'''


				a__					=  torch.manual_seed(lowerCamelCase       )
				a__					=  {
				    """prompt""": """A field of roses""",
				    """generator""": generator,
				    """num_inference_steps""": 3,
				    """guidance_scale""": 7.5,
				    """output_type""": """numpy""",
				}
				return inputs

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """CompVis/stable-diffusion-v1-4"""
				a__					=  StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase   ,       safety_checker=lowerCamelCase       )
				pipe.to(lowerCamelCase       )
				pipe.set_progress_bar_config(disable=lowerCamelCase       )
				pipe.enable_attention_slicing()

				a__					=  self.get_inputs()
				a__					=  pipe(**lowerCamelCase       ).images
				a__					=  image[0, -3:, -3:, -1].flatten()

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

				a__					=  np.array(
				    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

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

				# make sure image changes after editing
				pipe.edit_model("""A pack of roses"""   ,       """A pack of blue roses"""       )

				a__					=  pipe(**lowerCamelCase       ).images
				a__					=  image[0, -3:, -3:, -1].flatten()

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

				assert np.abs(expected_slice - image_slice       ).max() > 1e-1







	def 				_A			(	self       ):



				'''simple docstring'''


				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				a__					=  """CompVis/stable-diffusion-v1-4"""
				a__					=  DDIMScheduler.from_pretrained(lowerCamelCase   ,       subfolder="""scheduler"""       )
				a__					=  StableDiffusionModelEditingPipeline.from_pretrained(
				    lowerCamelCase   ,       scheduler=lowerCamelCase   ,       safety_checker=lowerCamelCase       )
				a__					=  pipe.to(lowerCamelCase       )
				pipe.set_progress_bar_config(disable=lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				a__					=  self.get_inputs()
				a__					=  pipe(**lowerCamelCase       )

				a__					=  torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

