
"""simple docstring"""

import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

class __lowerCAmelCase						( unittest.TestCase     ):
     lowercase		  =		"hf-internal-testing/tiny-stable-diffusion-pipe"
     lowercase		  =		1
     lowercase		  =		77
     lowercase		  =		32
     lowercase		  =		4
     lowercase		  =		lowercase		  =		64
     lowercase		  =		torch.manual_seed(0     )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase=0.9_9_9_9 ):

      '''simple docstring'''




      __UpperCamelCase							    =			UNetaDConditionModel.from_pretrained(self.model_id      ,	subfolder='unet' )
      __UpperCamelCase							    =			unet.to(__UpperCAmelCase )
      __UpperCamelCase							    =			EMAModel(unet.parameters()      ,	decay=__UpperCAmelCase      ,	model_cls=__UpperCAmelCase      ,	model_config=unet.config )
      return unet, ema_unet





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			torch.randn(
          self.batch_size      ,	self.num_in_channels      ,	self.latent_height      ,	self.latent_width      ,	generator=self.generator ).to(__UpperCAmelCase )
      __UpperCamelCase							    =			torch.randint(0      ,	1000      ,	size=(self.batch_size,)      ,	generator=self.generator ).to(__UpperCAmelCase )
      __UpperCamelCase							    =			torch.randn(
          self.batch_size      ,	self.prompt_length      ,	self.text_encoder_hidden_dim      ,	generator=self.generator ).to(__UpperCAmelCase )
      return noisy_latents, timesteps, encoder_hidden_states





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			{}
      for k, param in unet.state_dict().items():
       __UpperCamelCase							    =			torch.randn_like(__UpperCAmelCase ) + (param * torch.randn_like(__UpperCAmelCase ))
       updated_state_dict.update({k: updated_param} )
      unet.load_state_dict(__UpperCAmelCase )
      return unet





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models()
      # Take the first (hypothetical) EMA step.
      ema_unet.step(unet.parameters() )
      assert ema_unet.optimization_step == 1

      # Take two more.
      for _ in range(2 ):
       ema_unet.step(unet.parameters() )
      assert ema_unet.optimization_step == 3





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models()
      # Since the `unet` is not being updated (i.e., backprop'd)
      # there won't be any difference between the `params` of `unet`
      # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
      ema_unet.step(unet.parameters() )
      __UpperCamelCase							    =			list(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params      ,	__UpperCAmelCase ):
       assert torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )

      # The above holds true even if we call `ema.step()` multiple times since
      # `unet` params are still not being updated.
      for _ in range(4 ):
       ema_unet.step(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params      ,	__UpperCAmelCase ):
       assert torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models()
      # Here we simulate the parameter updates for `unet`. Since there might
      # be some parameters which are initialized to zero we take extra care to
      # initialize their values to something non-zero before the multiplication.
      __UpperCamelCase							    =			self.simulate_backprop(__UpperCAmelCase )

      # Take the EMA step.
      ema_unet.step(unet_pseudo_updated_step_one.parameters() )

      # Now the EMA'd parameters won't be equal to the original model parameters.
      __UpperCamelCase							    =			list(unet_pseudo_updated_step_one.parameters() )
      for s_param, param in zip(ema_unet.shadow_params      ,	__UpperCAmelCase ):
       assert ~torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )

      # Ensure this is the case when we take multiple EMA steps.
      for _ in range(4 ):
       ema_unet.step(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params      ,	__UpperCAmelCase ):
       assert ~torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models()

      # First backprop + EMA
      __UpperCamelCase							    =			self.simulate_backprop(__UpperCAmelCase )
      ema_unet.step(unet_step_one.parameters() )
      __UpperCamelCase							    =			ema_unet.shadow_params

      # Second backprop + EMA
      __UpperCamelCase							    =			self.simulate_backprop(__UpperCAmelCase )
      ema_unet.step(unet_step_two.parameters() )
      __UpperCamelCase							    =			ema_unet.shadow_params

      for step_one, step_two in zip(__UpperCAmelCase      ,	__UpperCAmelCase ):
       assert ~torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models(decay=0.0 )
      __UpperCamelCase							    =			self.simulate_backprop(__UpperCAmelCase )
      ema_unet.step(unet_step_one.parameters() )
      __UpperCamelCase							    =			ema_unet.shadow_params

      __UpperCamelCase							    =			self.simulate_backprop(__UpperCAmelCase )
      ema_unet.step(unet_step_two.parameters() )
      __UpperCamelCase							    =			ema_unet.shadow_params

      for step_one, step_two in zip(__UpperCAmelCase      ,	__UpperCAmelCase ):
       assert torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase )







     @skip_mps
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_models()
      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
       ema_unet.save_pretrained(__UpperCAmelCase )
       __UpperCamelCase							    =			UNetaDConditionModel.from_pretrained(__UpperCAmelCase      ,	model_cls=__UpperCAmelCase )
       __UpperCamelCase							    =			loaded_unet.to(unet.device )

      # Since no EMA step has been performed the outputs should match.
      __UpperCamelCase							    =			unet(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ).sample
      __UpperCamelCase							    =			loaded_unet(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ).sample

      assert torch.allclose(__UpperCAmelCase      ,	__UpperCAmelCase      ,	atol=1E-4 )





"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



