


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class    UpperCAmelCase__	:



    """simple docstring"""






    def _a (      self   )  ->	Any:
         torch.manual_seed(0   )
         __UpperCamelCase   							=TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         __UpperCamelCase   							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         __UpperCamelCase   							=UNetaDConditionModel(
             sample_size=32 ,       layers_per_block=1 ,       block_out_channels=[32, 64] ,       down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ] ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn' ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] ,       in_channels=3 ,       out_channels=6 ,       cross_attention_dim=32 ,       encoder_hid_dim=32 ,       attention_head_dim=8 ,       addition_embed_type='text' ,       addition_embed_type_num_heads=2 ,       cross_attention_norm='group_norm' ,       resnet_time_scale_shift='scale_shift' ,       act_fn='gelu' ,       )
         unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         torch.manual_seed(0   )
         __UpperCamelCase   							=DDPMScheduler(
             num_train_timesteps=1000 ,       beta_schedule='squaredcos_cap_v2' ,       beta_start=0.0001 ,       beta_end=0.02 ,       thresholding=A_ ,       dynamic_thresholding_ratio=0.95 ,       sample_max_value=1.0 ,       prediction_type='epsilon' ,       variance_type='learned_range' ,       )

         torch.manual_seed(0   )
         __UpperCamelCase   							=IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }






    def _a (      self   )  ->	Union[str, Any]:
         torch.manual_seed(0   )
         __UpperCamelCase   							=TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         __UpperCamelCase   							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         __UpperCamelCase   							=UNetaDConditionModel(
             sample_size=32 ,       layers_per_block=[1, 2] ,       block_out_channels=[32, 64] ,       down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ] ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn' ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] ,       in_channels=6 ,       out_channels=6 ,       cross_attention_dim=32 ,       encoder_hid_dim=32 ,       attention_head_dim=8 ,       addition_embed_type='text' ,       addition_embed_type_num_heads=2 ,       cross_attention_norm='group_norm' ,       resnet_time_scale_shift='scale_shift' ,       act_fn='gelu' ,       class_embed_type='timestep' ,       mid_block_scale_factor=1.414 ,       time_embedding_act_fn='gelu' ,       time_embedding_dim=32 ,       )
         unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         torch.manual_seed(0   )
         __UpperCamelCase   							=DDPMScheduler(
             num_train_timesteps=1000 ,       beta_schedule='squaredcos_cap_v2' ,       beta_start=0.0001 ,       beta_end=0.02 ,       thresholding=A_ ,       dynamic_thresholding_ratio=0.95 ,       sample_max_value=1.0 ,       prediction_type='epsilon' ,       variance_type='learned_range' ,       )

         torch.manual_seed(0   )
         __UpperCamelCase   							=DDPMScheduler(
             num_train_timesteps=1000 ,       beta_schedule='squaredcos_cap_v2' ,       beta_start=0.0001 ,       beta_end=0.02 ,       )

         torch.manual_seed(0   )
         __UpperCamelCase   							=IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }






    def _a (      self   )  ->	Tuple:
         __UpperCamelCase   							=self.get_dummy_components()
         __UpperCamelCase   							=self.pipeline_class(**A_   )
         pipe.to(A_   )
         pipe.set_progress_bar_config(disable=A_   )

         __UpperCamelCase   							=self.get_dummy_inputs(A_   )

         __UpperCamelCase   							=inputs['prompt']
         __UpperCamelCase   							=inputs['generator']
         __UpperCamelCase   							=inputs['num_inference_steps']
         __UpperCamelCase   							=inputs['output_type']

         if "image" in inputs:
              __UpperCamelCase   							=inputs['image']
         else:
              __UpperCamelCase   							=None

         if "mask_image" in inputs:
              __UpperCamelCase   							=inputs['mask_image']
         else:
              __UpperCamelCase   							=None

         if "original_image" in inputs:
              __UpperCamelCase   							=inputs['original_image']
         else:
              __UpperCamelCase   							=None

         __UpperCamelCase							,				__UpperCamelCase   							=pipe.encode_prompt(A_   )

         # inputs with prompt converted to embeddings
         __UpperCamelCase   							={
             'prompt_embeds': prompt_embeds,
             'negative_prompt_embeds': negative_prompt_embeds,
             'generator': generator,
             'num_inference_steps': num_inference_steps,
             'output_type': output_type,
         }

         if image is not None:
              __UpperCamelCase   							=image

         if mask_image is not None:
              __UpperCamelCase   							=mask_image

         if original_image is not None:
              __UpperCamelCase   							=original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
              setattr(A_ ,       A_ ,       A_   )

         __UpperCamelCase   							=pipe(**A_   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(A_   )
              __UpperCamelCase   							=self.pipeline_class.from_pretrained(A_   )
              pipe_loaded.to(A_   )
              pipe_loaded.set_progress_bar_config(disable=A_   )

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(A_ ,       A_   ) is None ,       f'`{optional_component}` did not stay set to None after loading.' ,       )

         __UpperCamelCase   							=self.get_dummy_inputs(A_   )

         __UpperCamelCase   							=inputs['generator']
         __UpperCamelCase   							=inputs['num_inference_steps']
         __UpperCamelCase   							=inputs['output_type']

         # inputs with prompt converted to embeddings
         __UpperCamelCase   							={
             'prompt_embeds': prompt_embeds,
             'negative_prompt_embeds': negative_prompt_embeds,
             'generator': generator,
             'num_inference_steps': num_inference_steps,
             'output_type': output_type,
         }

         if image is not None:
              __UpperCamelCase   							=image

         if mask_image is not None:
              __UpperCamelCase   							=mask_image

         if original_image is not None:
              __UpperCamelCase   							=original_image

         __UpperCamelCase   							=pipe_loaded(**A_   )[0]

         __UpperCamelCase   							=np.abs(to_np(A_   ) - to_np(A_   )   ).max()
         self.assertLess(A_ ,       1E-4   )






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.get_dummy_components()
         __UpperCamelCase   							=self.pipeline_class(**A_   )
         pipe.to(A_   )
         pipe.set_progress_bar_config(disable=A_   )

         __UpperCamelCase   							=self.get_dummy_inputs(A_   )
         __UpperCamelCase   							=pipe(**A_   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(A_   )
              __UpperCamelCase   							=self.pipeline_class.from_pretrained(A_   )
              pipe_loaded.to(A_   )
              pipe_loaded.set_progress_bar_config(disable=A_   )

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         __UpperCamelCase   							=self.get_dummy_inputs(A_   )
         __UpperCamelCase   							=pipe_loaded(**A_   )[0]

         __UpperCamelCase   							=np.abs(to_np(A_   ) - to_np(A_   )   ).max()
         self.assertLess(A_ ,       1E-4   )









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)





