



"""simple docstring"""
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		# Initialise PyTorch model
		a_     :      int 						=     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
		print("Building PyTorch model from configuration: {}".format(str(SCREAMING_SNAKE_CASE__	)	)	)
		a_     :      Dict 						=     RemBertModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		print("Save PyTorch model to {}".format(SCREAMING_SNAKE_CASE__	)	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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(
					    """--rembert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained RemBERT 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."""
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




"""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, KandinskyImgaImgPipeline, 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 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					KandinskyImgaImgPipeline
							__lowerCAmelCase    					=					["prompt", "image_embeds", "negative_image_embeds", "image"]
							__lowerCAmelCase    					=					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							__lowerCAmelCase    					=					[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							__lowerCAmelCase    					=					False






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim * 4






							@property
							def snake_case_  (   self      ):
									return 1_0_0






							@property
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
									return tokenizer






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     MCLIPConfig(
									    numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=3_7			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1_0_0_5			,     )

									a_     :      str 						=     MultilingualCLIP(a_      )
									a_     :      Any 						=     text_encoder.eval()

									return text_encoder






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )

									a_     :      Union[str, Any] 						=     {
									    "in_channels": 4,
									    # 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,
									}

									a_     :      Dict 						=     UNetaDConditionModel(**a_      )
									return model






							@property
							def snake_case_  (   self      ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     VQModel(**self.dummy_movq_kwargs      )
									return model






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.dummy_text_encoder
									a_     :      Dict 						=     self.dummy_tokenizer
									a_     :      Optional[int] 						=     self.dummy_unet
									a_     :      Dict 						=     self.dummy_movq

									a_     :      List[str] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.00_085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									a_     :      List[Any] 						=     DDIMScheduler(**a_      )

									a_     :      Union[str, Any] 						=     {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									a_     :      Optional[int] 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Tuple 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(a_      )
									# create init_image
									a_     :      Optional[int] 						=     floats_tensor((1, 3, 6_4, 6_4)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Any 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      int 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )

									if str(a_      ).startswith("mps"      ):
											a_     :      Any 						=     torch.manual_seed(a_      )
									else:
											a_     :      Any 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "cpu"

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

									a_     :      Union[str, Any] 						=     self.pipeline_class(**a_      )
									a_     :      Tuple 						=     pipe.to(a_      )

									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Union[str, Any] 						=     pipe(**self.get_dummy_inputs(a_      )      )
									a_     :      Any 						=     output.images

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

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]
									a_     :      Optional[Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a_     :      Optional[int] 						=     np.array(
									    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]      )
									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()}"""




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






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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy"      )

									a_     :      Union[str, Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
									a_     :      Optional[Any] 						=     "A red cartoon frog, 4k"

									a_     :      int 						=     KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"			,     torch_dtype=torch.floataa      )
									pipe_prior.to(a_      )

									a_     :      int 						=     KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"			,     torch_dtype=torch.floataa      )
									a_     :      List[Any] 						=     pipeline.to(a_      )

									pipeline.set_progress_bar_config(disable=a_      )

									a_     :      int 						=     torch.Generator(device="cpu"      ).manual_seed(0      )
									a_						,				a_     :      Optional[int] 						=     pipe_prior(
									    a_			,     generator=a_			,     num_inference_steps=5			,     negative_prompt=""			,     ).to_tuple()

									a_     :      List[Any] 						=     pipeline(
									    a_			,     image=a_			,     image_embeds=a_			,     negative_image_embeds=a_			,     generator=a_			,     num_inference_steps=1_0_0			,     height=7_6_8			,     width=7_6_8			,     strength=0.2			,     output_type="np"			,     )

									a_     :      int 						=     output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_			,     a_      )