



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()





class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		1
           lowerCamelCase             =		3
           lowerCamelCase             =		(32, 32)

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







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		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     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		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     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		RobertaSeriesConfig(
               hidden_size=32					,      project_dim=32					,      intermediate_size=37					,      layer_norm_eps=1E-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=50_06					,      )
           return RobertaSeriesModelWithTransformation(__a     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           def extract(*__a					,      **__a     ):





                 class  lowerCamelCase__	:
                      """simple docstring"""



                      def __init__(self     ):






                            '''simple docstring'''



                            lowerCamelCase             =		torch.ones([0]     )







                      def     _a   (self					,      __a     ):






                            '''simple docstring'''



                            self.pixel_values.to(__a     )
                            return self



                 return Out()

           return extract







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.dummy_cond_unet
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"     )
           lowerCamelCase             =		77

           lowerCamelCase             =		self.dummy_image.to(__a     )
           lowerCamelCase             =		init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		AltDiffusionImgaImgPipeline(
               unet=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      safety_checker=__a					,      feature_extractor=self.dummy_extractor					,      )
           lowerCamelCase             =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,      do_normalize=__a     )
           lowerCamelCase             =		alt_pipe.to(__a     )
           alt_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		alt_pipe(
               [prompt]					,      generator=__a					,      guidance_scale=6.0					,      num_inference_steps=2					,      output_type="np"					,      image=__a					,      )

           lowerCamelCase             =		output.images

           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		alt_pipe(
               [prompt]					,      generator=__a					,      guidance_scale=6.0					,      num_inference_steps=2					,      output_type="np"					,      image=__a					,      return_dict=__a					,      )[0]

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

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase             =		np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]     )

           assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 5E-3







     @unittest.skipIf(torch_device != "cuda"					,      "This test requires a GPU"     )
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.dummy_cond_unet
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"     )
           lowerCamelCase             =		77

           lowerCamelCase             =		self.dummy_image.to(__a     )

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

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		AltDiffusionImgaImgPipeline(
               unet=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      safety_checker=__a					,      feature_extractor=self.dummy_extractor					,      )
           lowerCamelCase             =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,      do_normalize=__a     )
           lowerCamelCase             =		alt_pipe.to(__a     )
           alt_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		alt_pipe(
               [prompt]					,      generator=__a					,      num_inference_steps=2					,      output_type="np"					,      image=__a					,      ).images

           assert image.shape == (1, 32, 32, 3)







     @unittest.skipIf(torch_device != "cuda"					,      "This test requires a GPU"     )
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/img2img/sketch-mountains-input.jpg"     )
           # resize to resolution that is divisible by 8 but not 16 or 32
           lowerCamelCase             =		init_image.resize((7_60, 5_04)     )

           lowerCamelCase             =		"BAAI/AltDiffusion"
           lowerCamelCase             =		AltDiffusionImgaImgPipeline.from_pretrained(
               __a					,      safety_checker=__a					,      )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"A fantasy landscape, trending on artstation"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      strength=0.75					,      guidance_scale=7.5					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           lowerCamelCase             =		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           lowerCamelCase             =		np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]     )

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





@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     ):






           '''simple docstring'''



           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/img2img/sketch-mountains-input.jpg"     )
           lowerCamelCase             =		init_image.resize((7_68, 5_12)     )
           lowerCamelCase             =		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"     )

           lowerCamelCase             =		"BAAI/AltDiffusion"
           lowerCamelCase             =		AltDiffusionImgaImgPipeline.from_pretrained(
               __a					,      safety_checker=__a					,      )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"A fantasy landscape, trending on artstation"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      strength=0.75					,      guidance_scale=7.5					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image     ).max() < 1E-2




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		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(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT 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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)