'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      UpperCAmelCase    (		a__       ,		a__       ,		a__       ,		unittest.TestCase   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       StableDiffusionInpaintPipeline
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       SCREAMING_SNAKE_CASE   						=       frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       SCREAMING_SNAKE_CASE   						=       frozenset([]   )

       def _lowerCAmelCase(		self					)    ->    str:
        torch.manual_seed(0					)
        lowercase__							:       List[Any]										=   UNetaDConditionModel(
            block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=9      ,  out_channels=4      ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,  cross_attention_dim=32      ,  attention_head_dim=(2, 4)      ,  use_linear_projection=__a      ,  )
        lowercase__							:       Union[str, Any]										=   PNDMScheduler(skip_prk_steps=__a					)
        torch.manual_seed(0					)
        lowercase__							:       Dict										=   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      ,  sample_size=128      ,  )
        torch.manual_seed(0					)
        lowercase__							:       List[Any]										=   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      ,  hidden_act='''gelu'''      ,  projection_dim=512      ,  )
        lowercase__							:       Union[str, Any]										=   CLIPTextModel(__a					)
        lowercase__							:       Dict										=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0					)    ->    Optional[int]:
        lowercase__							:       List[str]										=   floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(__a					)					).to(__a					)
        lowercase__							:       List[Any]										=   image.cpu().permute(0      ,  2      ,  3      ,  1					)[0]
        lowercase__							:       Optional[int]										=   Image.fromarray(np.uinta(__a					)					).convert('''RGB'''					).resize((64, 64)					)
        lowercase__							:       int										=   Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((64, 64)					)
        if str(__a					).startswith('''mps'''					):
         lowercase__							:       Optional[Any]										=   torch.manual_seed(__a					)
        else:
         lowercase__							:       Tuple										=   torch.Generator(device=__a					).manual_seed(__a					)
        lowercase__							:       List[str]										=   {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        lowercase__							:       Union[str, Any]										=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase__							:       Tuple										=   self.get_dummy_components()
        lowercase__							:       Optional[Any]										=   StableDiffusionInpaintPipeline(**__a					)
        lowercase__							:       Optional[Any]										=   sd_pipe.to(__a					)
        sd_pipe.set_progress_bar_config(disable=__a					)

        lowercase__							:       int										=   self.get_dummy_inputs(__a					)
        lowercase__							:       Any										=   sd_pipe(**__a					).images
        lowercase__							:       Any										=   image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase__							:       List[str]										=   np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]					)

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


       def _lowerCAmelCase(		self					)    ->    Any:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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





       '''simple docstring'''



       def _lowerCAmelCase(		self					)    ->    Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def _lowerCAmelCase(		self					)    ->    int:
        lowercase__							:       Any										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''					)
        lowercase__							:       Union[str, Any]										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
        lowercase__							:       str										=   load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench.npy'''					)

        lowercase__							:       int										=   '''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__							:       Union[str, Any]										=   StableDiffusionInpaintPipeline.from_pretrained(__a      ,  safety_checker=__a					)
        pipe.to(__a					)
        pipe.set_progress_bar_config(disable=__a					)
        pipe.enable_attention_slicing()

        lowercase__							:       Optional[int]										=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__							:       Dict										=   torch.manual_seed(0					)
        lowercase__							:       Union[str, Any]										=   pipe(
            prompt=__a      ,  image=__a      ,  mask_image=__a      ,  generator=__a      ,  output_type='''np'''      ,  )
        lowercase__							:       Optional[int]										=   output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image					).max() < 9E-3

       def _lowerCAmelCase(		self					)    ->    Any:
        lowercase__							:       Tuple										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''					)
        lowercase__							:       List[str]										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
        lowercase__							:       Optional[int]										=   load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''					)

        lowercase__							:       Dict										=   '''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__							:       Optional[int]										=   StableDiffusionInpaintPipeline.from_pretrained(
            __a      ,  torch_dtype=torch.floataa      ,  safety_checker=__a      ,  )
        pipe.to(__a					)
        pipe.set_progress_bar_config(disable=__a					)
        pipe.enable_attention_slicing()

        lowercase__							:       Optional[int]										=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__							:       Tuple										=   torch.manual_seed(0					)
        lowercase__							:       Optional[int]										=   pipe(
            prompt=__a      ,  image=__a      ,  mask_image=__a      ,  generator=__a      ,  output_type='''np'''      ,  )
        lowercase__							:       Optional[int]										=   output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image					).max() < 5E-1


       def _lowerCAmelCase(		self					)    ->    str:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        lowercase__							:       Tuple										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''					)
        lowercase__							:       List[Any]										=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)

        lowercase__							:       Optional[int]										=   '''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__							:       Optional[Any]										=   PNDMScheduler.from_pretrained(__a      ,  subfolder='''scheduler'''					)
        lowercase__							:       List[Any]										=   StableDiffusionInpaintPipeline.from_pretrained(
            __a      ,  safety_checker=__a      ,  scheduler=__a      ,  torch_dtype=torch.floataa      ,  )
        pipe.to(__a					)
        pipe.set_progress_bar_config(disable=__a					)
        pipe.enable_attention_slicing(1					)
        pipe.enable_sequential_cpu_offload()

        lowercase__							:       Union[str, Any]										=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__							:       List[str]										=   torch.manual_seed(0					)
        lowercase__							:       Optional[Any]										=   pipe(
            prompt=__a      ,  image=__a      ,  mask_image=__a      ,  generator=__a      ,  num_inference_steps=2      ,  output_type='''np'''      ,  )

        lowercase__							:       Optional[Any]										=   torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.6_5 * 10**9



"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






