






"""simple docstring"""







import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # Initialise PyTorch model
       UpperCAmelCase	    =			AlbertConfig.from_json_file(lowerCAmelCase		)
       print(F'''Building PyTorch model from configuration: {config}'''		)
       UpperCAmelCase	    =			AlbertForPreTraining(lowerCAmelCase		)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
       torch.save(model.state_dict()	,      lowerCAmelCase		)


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








"""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   UpperCamelCase_ :



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         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=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		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 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         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=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		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=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		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 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         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(snake_case__				)

         # 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(snake_case__ ,		snake_case__ ,		snake_case__				)

         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

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

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

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

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         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(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

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

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

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)
