import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE      						=	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""")
     _SCREAMING_SNAKE_CASE      						=	parser.parse_args()

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

     _SCREAMING_SNAKE_CASE      						=	model.state_dict()
     _SCREAMING_SNAKE_CASE      						=	{}

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

     _SCREAMING_SNAKE_CASE      						=	0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          for w in ["weight", "bias"]:
               _SCREAMING_SNAKE_CASE      						=	state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      						=	state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      						=	state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

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

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

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




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 _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Any     =			KandinskyImgaImgPipeline
       lowercase_ :	Union[str, Any]     =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowercase_ :	Any     =			[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowercase_ :	List[Any]     =			[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_ :	Union[str, Any]     =			False
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return self.time_input_dim
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return 1_00
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
           return tokenizer
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		MCLIPConfig(
               numDims=self.cross_attention_dim,      transformerDimensions=self.text_embedder_hidden_size,      hidden_size=self.text_embedder_hidden_size,      intermediate_size=37,      num_attention_heads=4,      num_hidden_layers=5,      vocab_size=10_05,      )

           _lowercase       :							Optional[int]      		=		MultilingualCLIP(lowerCamelCase)
           _lowercase       :							List[str]      		=		text_encoder.eval()

           return text_encoder
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase       :							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,
           }

           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return {
               "block_out_channels": [32, 64],
               "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": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		VQModel(**self.dummy_movq_kwargs)
           return model
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.dummy_text_encoder
           _lowercase       :							List[Any]      		=		self.dummy_tokenizer
           _lowercase       :							int      		=		self.dummy_unet
           _lowercase       :							int      		=		self.dummy_movq

           _lowercase       :							Optional[int]      		=		{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase       :							List[Any]      		=		DDIMScheduler(**lowerCamelCase)

           _lowercase       :							List[Any]      		=		{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(seed + 1)).to(lowerCamelCase)
           # create init_image
           _lowercase       :							Tuple      		=		floats_tensor((1, 3, 64, 64),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		image.cpu().permute(0,      2,      3,      1)[0]
           _lowercase       :							Tuple      		=		Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[str]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               'prompt': 'horse',
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		'cpu'

           _lowercase       :							Tuple      		=		self.get_dummy_components()

           _lowercase       :							str      		=		self.pipeline_class(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							List[Any]      		=		pipe(
               **self.get_dummy_inputs(lowerCamelCase),      return_dict=lowerCamelCase,      )[0]

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

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

           _lowercase       :							Tuple      		=		np.array(
               [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
           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 _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_img2img_frog.npy')

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

           _lowercase       :							Union[str, Any]      		=		KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		KandinskyImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1',      torch_dtype=torch.floataa)
           _lowercase       :							List[Any]      		=		pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		torch.Generator(device='cpu').manual_seed(0)
           _lowercase		,				_lowercase       :							List[Any]      		=		pipe_prior(
               lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=5,      negative_prompt='',      ).to_tuple()

           _lowercase       :							Union[str, Any]      		=		pipeline(
               lowerCamelCase,      image=lowerCamelCase,      image_embeds=lowerCamelCase,      negative_image_embeds=lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=1_00,      height=7_68,      width=7_68,      strength=0.2,      output_type='np',      )

           _lowercase       :							Dict      		=		output.images[0]

           assert image.shape == (7_68, 7_68, 3)

           assert_mean_pixel_difference(lowerCamelCase,      lowerCamelCase)


