






"""simple docstring"""
import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





 """simple docstring"""




 def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=99     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=37     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=512     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=4     ,     ):
  A_		:						Tuple	    =   parent
  A_		:						int	    =   batch_size
  A_		:						Optional[Any]	    =   seq_length
  A_		:						int	    =   is_training
  A_		:						List[Any]	    =   use_attention_mask
  A_		:						List[str]	    =   use_token_type_ids
  A_		:						List[Any]	    =   use_labels
  A_		:						Any	    =   vocab_size
  A_		:						str	    =   hidden_size
  A_		:						Any	    =   num_hidden_layers
  A_		:						int	    =   num_attention_heads
  A_		:						Any	    =   intermediate_size
  A_		:						Optional[int]	    =   hidden_act
  A_		:						Tuple	    =   hidden_dropout_prob
  A_		:						int	    =   attention_probs_dropout_prob
  A_		:						Any	    =   max_position_embeddings
  A_		:						Any	    =   type_vocab_size
  A_		:						int	    =   type_sequence_label_size
  A_		:						Optional[int]	    =   initializer_range
  A_		:						Optional[int]	    =   num_choices





 def    lowerCamelCase(self							):
  A_		:						Dict	    =   ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size							)

  A_		:						Dict	    =   None
  if self.use_attention_mask:
   A_		:						Dict	    =   random_attention_mask([self.batch_size, self.seq_length]							)

  A_		:						List[str]	    =   None
  if self.use_token_type_ids:
   A_		:						Union[str, Any]	    =   ids_tensor([self.batch_size, self.seq_length]     ,     self.type_vocab_size							)

  A_		:						List[Any]	    =   AlbertConfig(
      vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     intermediate_size=self.intermediate_size     ,     hidden_act=self.hidden_act     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     max_position_embeddings=self.max_position_embeddings     ,     type_vocab_size=self.type_vocab_size     ,     is_decoder=lowerCAmelCase_     ,     initializer_range=self.initializer_range     ,     )

  return config, input_ids, token_type_ids, attention_mask





 def    lowerCamelCase(self							):
  A_		:						List[Any]	    =   self.prepare_config_and_inputs()
  A_		:						List[str]	    =   config_and_inputs
  A_		:						List[Any]	    =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
  return config, inputs_dict






@require_flax
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





 """simple docstring"""




 _A       :				List[Any]    =				(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )





 def    lowerCamelCase(self							):
  A_		:						Optional[int]	    =   FlaxAlbertModelTester(self							)





 @slow
 def    lowerCamelCase(self							):
  for model_class_name in self.all_model_classes:
   A_		:						Optional[Any]	    =   model_class_name.from_pretrained("""albert-base-v2"""							)
   A_		:						Optional[Any]	    =   model(np.ones((1, 1)							)							)
   self.assertIsNotNone(lowerCAmelCase_							)






@require_flax
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





 """simple docstring"""




 @slow
 def    lowerCamelCase(self							):
  A_		:						List[str]	    =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""							)
  A_		:						Optional[Any]	    =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
  A_		:						int	    =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
  A_		:						List[str]	    =   model(lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_							)[0]
  A_		:						int	    =   (1, 11, 768)
  self.assertEqual(output.shape     ,     lowerCAmelCase_							)
  A_		:						List[Any]	    =   np.array(
      [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]							)

  self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,     lowerCAmelCase_     ,     atol=1e-4							)							)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   DDIMScheduler(
	    timesteps=config.model.params.timesteps   ,   beta_schedule="""scaled_linear"""   ,   beta_start=config.model.params.linear_start   ,   beta_end=config.model.params.linear_end   ,   clip_sample=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							parser.add_argument("--checkpoint_path", type=str, required=True)
							parser.add_argument("--config_path", type=str, required=True)
							parser.add_argument("--output_path", type=str, required=True)
							_lowerCAmelCase         =							parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

