





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					UNetaDModel(**__lowercase)
  __lowerCAmelCase								=					checkpoint['all_modules.0.W'].data
  __lowerCAmelCase								=					checkpoint['all_modules.0.W'].data
  __lowerCAmelCase								=					checkpoint['all_modules.1.weight'].data
  __lowerCAmelCase								=					checkpoint['all_modules.1.bias'].data

  __lowerCAmelCase								=					checkpoint['all_modules.2.weight'].data
  __lowerCAmelCase								=					checkpoint['all_modules.2.bias'].data

  __lowerCAmelCase								=					checkpoint['all_modules.3.weight'].data
  __lowerCAmelCase								=					checkpoint['all_modules.3.bias'].data

  __lowerCAmelCase								=					checkpoint[list(checkpoint.keys())[-4]].data
  __lowerCAmelCase								=					checkpoint[list(checkpoint.keys())[-3]].data
  __lowerCAmelCase								=					checkpoint[list(checkpoint.keys())[-2]].data
  __lowerCAmelCase								=					checkpoint[list(checkpoint.keys())[-1]].data

  __lowerCAmelCase								=					4

  def set_attention_weights(__lowercase:		int  ,				__lowercase:		List[Any]  ,				__lowercase:		str):
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

  def set_resnet_weights(__lowercase:		str  ,				__lowercase:		Tuple  ,				__lowercase:		List[Any]):
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
    __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

    if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
      __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
      __lowerCAmelCase								=					old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

  for i, block in enumerate(new_model_architecture.downsample_blocks):
    __lowerCAmelCase								=					hasattr(__lowercase  ,				'''attentions''')
    for j in range(len(block.resnets)):
      set_resnet_weights(block.resnets[j]  ,				__lowercase  ,				__lowercase)
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j]  ,				__lowercase  ,				__lowercase)
        module_index += 1

    if hasattr(__lowercase  ,				'''downsamplers''') and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down  ,				__lowercase  ,				__lowercase)
      module_index += 1
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
      module_index += 1

  set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				__lowercase  ,				__lowercase)
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				__lowercase  ,				__lowercase)
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				__lowercase  ,				__lowercase)
  module_index += 1

  for i, block in enumerate(new_model_architecture.up_blocks):
    __lowerCAmelCase								=					hasattr(__lowercase  ,				'''attentions''')
    for j in range(len(block.resnets)):
      set_resnet_weights(block.resnets[j]  ,				__lowercase  ,				__lowercase)
      module_index += 1
    if has_attentions:
      set_attention_weights(
          block.attentions[0]  ,				__lowercase  ,				__lowercase)  # why can there only be a single attention layer for up?
      module_index += 1

    if hasattr(__lowercase  ,				'''resnet_up''') and block.resnet_up is not None:
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.weight'''].data
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.weight'''].data
      __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      set_resnet_weights(block.resnet_up  ,				__lowercase  ,				__lowercase)
      module_index += 1

  __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.weight'''].data
  __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.bias'''].data
  module_index += 1
  __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.weight'''].data
  __lowerCAmelCase								=					checkpoint[F'''all_modules.{module_index}.bias'''].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
      __lowercase				    =      argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
          type=str,
          required=False,
          help="""Path to the checkpoint to convert.""",
      )

      parser.add_argument(
          """--config_file""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
          type=str,
          required=False,
          help="""The config json file corresponding to the architecture.""",
      )

      parser.add_argument(
          """--dump_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
          type=str,
          required=False,
          help="""Path to the output model.""",
      )

      __lowercase				    =      parser.parse_args()

      __lowercase				    =      torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            __lowercase				    =      json.loads(f.read())

      __lowercase				    =      convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

      if "sde" in config:
            del config["sde"]

      __lowercase				    =      UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            __lowercase				    =      ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            __lowercase				    =      ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)

'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    def __init__(					self     :						Dict							,							UpperCAmelCase_     :						List[str]							,							):


         """simple docstring"""







         a					:							Any							= parent
         a					:							Tuple							= 1_3
         a					:							Any							= 7
         a					:							Any							= True
         a					:							Optional[Any]							= True
         a					:							Optional[Any]							= True
         a					:							Dict							= True
         a					:							List[str]							= True
         a					:							Dict							= 9_9
         a					:							Tuple							= 3_2
         a					:							Optional[int]							= 2
         a					:							str							= 4
         a					:							str							= 3_7
         a					:							Union[str, Any]							= 'gelu'
         a					:							Any							= 0.1
         a					:							Any							= 0.1
         a					:							List[Any]							= 5_1_2
         a					:							Any							= 1_6
         a					:							int							= 2
         a					:							Dict							= 0.02
         a					:							Optional[int]							= 3
         a					:							Union[str, Any]							= 4
         a					:							int							= None
         a					:							Tuple							= self.vocab_size - 1





    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

         a					:							List[str]							= None
         if self.use_input_mask:
              a					:							Optional[Any]							= random_attention_mask([self.batch_size, self.seq_length])

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

         a					:							List[str]							= None
         if self.use_mc_token_ids:
              a					:							List[str]							= ids_tensor([self.batch_size, self.num_choices]							,							self.seq_length)

         a					:							str							= None
         a					:							Optional[Any]							= None
         a					:							Optional[int]							= None
         if self.use_labels:
              a					:							Tuple							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
              a					:							Any							= ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels)
              a					:							List[Any]							= ids_tensor([self.batch_size]							,							self.num_choices)

         a					:							Dict							= CTRLConfig(
             vocab_size=self.vocab_size							,							n_embd=self.hidden_size							,							n_layer=self.num_hidden_layers							,							n_head=self.num_attention_heads							,							n_positions=self.max_position_embeddings							,							pad_token_id=self.pad_token_id							,							)

         a					:							Tuple							= ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,							2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						str							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Union[str, Any]							= TFCTRLModel(config=UpperCAmelCase_)
         a					:							Dict							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							Tuple							= model(UpperCAmelCase_)

         a					:							str							= [input_ids, None, input_mask]  # None is the input for 'past'
         a					:							Any							= model(UpperCAmelCase_)

         a					:							List[str]							= model(UpperCAmelCase_)

         self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Dict							,							*UpperCAmelCase_     :						str):


         """simple docstring"""







         a					:							Optional[int]							= TFCTRLLMHeadModel(config=UpperCAmelCase_)
         a					:							Tuple							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							int							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Tuple							= self.num_labels
         a					:							List[str]							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
         a					:							int							= {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'labels': sequence_labels,
         }
         a					:							str							= TFCTRLForSequenceClassification(UpperCAmelCase_)
         a					:							Union[str, Any]							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))







    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= self.prepare_config_and_inputs()

         (
             (
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			
         )					:							int							= config_and_inputs

         a					:							Tuple							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_tf
class   UpperCamelCase					(       a_    ,     a_    ,     unittest.TestCase       ):







    """simple docstring"""





    A     :    str				=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
    A     :    Optional[int]				=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
    A     :    str				=			(
        {
            "feature-extraction": TFCTRLModel,
            "text-classification": TFCTRLForSequenceClassification,
            "text-generation": TFCTRLLMHeadModel,
            "zero-shot": TFCTRLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    A     :    int				=			False
    A     :    Optional[Any]				=			False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Any							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]):


         """simple docstring"""







         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
              # config could not be created.
              return True

         return False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Any							= TFCTRLModelTester(self)
         a					:							Dict							= ConfigTester(self							,							config_class=UpperCAmelCase_							,							n_embd=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         self.config_tester.run_common_tests()





    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							List[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							str							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a							,			a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs_for_common()
         a					:							int							= [TFCTRLLMHeadModel]
         a					:							int							= [TFCTRLForSequenceClassification]

         for model_class in self.all_model_classes:
              a					:							int							= model_class(UpperCAmelCase_)
              model.build()  # may be needed for the get_bias() call below
              assert isinstance(model.get_input_embeddings()							,							tf.keras.layers.Layer)

              if model_class in list_lm_models:
                   a					:							Optional[Any]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							int							= model.get_bias()
                   assert isinstance(UpperCAmelCase_							,							UpperCAmelCase_)
                   for k, v in name.items():
                        assert isinstance(UpperCAmelCase_							,							tf.Variable)
              elif model_class in list_other_models_with_output_ebd:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							List[Any]							= model.get_bias()
                   assert name is None
              else:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert x is None
                   a					:							Optional[Any]							= model.get_bias()
                   assert name is None







    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a					:							Dict							= TFCTRLModel.from_pretrained(UpperCAmelCase_)
              self.assertIsNotNone(UpperCAmelCase_)






@require_tf
class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							List[Any]							= TFCTRLLMHeadModel.from_pretrained('ctrl')
         a					:							Union[str, Any]							= tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]							,							dtype=tf.intaa)  # Legal the president is
         a					:							List[Any]							= [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         a					:							List[str]							= model.generate(UpperCAmelCase_							,							do_sample=UpperCAmelCase_)
         self.assertListEqual(output_ids[0].numpy().tolist()							,							UpperCAmelCase_)

