



"""simple docstring"""
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 					a				:



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Optional[int]:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: int     			=TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: str     			=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		)
     SCREAMING_SNAKE_CASE_: List[str]     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		thresholding=lowerCAmelCase       ,		dynamic_thresholding_ratio=0.9_5       ,		sample_max_value=1.0       ,		prediction_type="""epsilon"""       ,		variance_type="""learned_range"""       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=IFWatermarker()

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



  def 	lowerCamelCase__       (						self :					str		)     ->					Any:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Any     			=TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Dict     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=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=1.4_1_4       ,		time_embedding_act_fn="""gelu"""       ,		time_embedding_dim=32       ,		)
     unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: str     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		thresholding=lowerCAmelCase       ,		dynamic_thresholding_ratio=0.9_5       ,		sample_max_value=1.0       ,		prediction_type="""epsilon"""       ,		variance_type="""learned_range"""       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Tuple     			=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 	lowerCamelCase__       (						self :					int		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=self.get_dummy_components()
     SCREAMING_SNAKE_CASE_: str     			=self.pipeline_class(**lowerCAmelCase		)
     pipe.to(lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=self.get_dummy_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Dict     			=inputs["""prompt"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""generator"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""num_inference_steps"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""output_type"""]

     if "image" in inputs:
        SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""image"""]
     else:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None

     if "mask_image" in inputs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=inputs["""mask_image"""]
     else:
        SCREAMING_SNAKE_CASE_: Tuple     			=None

     if "original_image" in inputs:
        SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""original_image"""]
     else:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=None

     SCREAMING_SNAKE_CASE_: str     			=pipe.encode_prompt(lowerCAmelCase		)

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={
         """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:
        SCREAMING_SNAKE_CASE_: int     			=image

     if mask_image is not None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=mask_image

     if original_image is not None:
        SCREAMING_SNAKE_CASE_: str     			=original_image

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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=pipe(**lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Any     			=self.pipeline_class.from_pretrained(lowerCAmelCase		)
        pipe_loaded.to(lowerCAmelCase		)
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase		)

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

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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_dummy_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""generator"""]
     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""num_inference_steps"""]
     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""output_type"""]

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE_: Dict     			={
         """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:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image

     if mask_image is not None:
        SCREAMING_SNAKE_CASE_: List[str]     			=mask_image

     if original_image is not None:
        SCREAMING_SNAKE_CASE_: int     			=original_image

     SCREAMING_SNAKE_CASE_: List[str]     			=pipe_loaded(**lowerCAmelCase		)[0]

     SCREAMING_SNAKE_CASE_: Optional[int]     			=np.abs(to_np(lowerCAmelCase		) - to_np(lowerCAmelCase		)		).max()
     self.assertLess(lowerCAmelCase       ,		1E-4		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=self.get_dummy_components()
     SCREAMING_SNAKE_CASE_: Any     			=self.pipeline_class(**lowerCAmelCase		)
     pipe.to(lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.get_dummy_inputs(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=pipe(**lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Tuple     			=self.pipeline_class.from_pretrained(lowerCAmelCase		)
        pipe_loaded.to(lowerCAmelCase		)
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase		)

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

     SCREAMING_SNAKE_CASE_: Tuple     			=self.get_dummy_inputs(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=pipe_loaded(**lowerCAmelCase		)[0]

     SCREAMING_SNAKE_CASE_: List[Any]     			=np.abs(to_np(lowerCAmelCase		) - to_np(lowerCAmelCase		)		).max()
     self.assertLess(lowerCAmelCase       ,		1E-4		)








"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


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

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

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

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

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

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

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

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



