

'''simple docstring'''






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )






class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict=1_3      ,lowercase__     :  Any=7      ,lowercase__     :  Optional[Any]=True      ,lowercase__     :  Tuple=True      ,lowercase__     :  List[str]=True      ,lowercase__     :  Optional[int]=True      ,lowercase__     :  int=9_9      ,lowercase__     :  List[str]=3_2      ,lowercase__     :  Any=5      ,lowercase__     :  Union[str, Any]=4      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  int="gelu"      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Union[str, Any]=0.1      ,lowercase__     :  Optional[int]=5_1_2      ,lowercase__     :  Union[str, Any]=1_6      ,lowercase__     :  Optional[Any]=2      ,lowercase__     :  Optional[int]=0.0_2      ,lowercase__     :  Union[str, Any]=4      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_attention_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				type_sequence_label_size
   __lowercase													=				initializer_range
   __lowercase													=				num_choices






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

   __lowercase													=				RobertaConfig(
       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=lowercase__      ,initializer_range=self.initializer_range      ,)

   return config, input_ids, token_type_ids, attention_mask






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
   return config, inputs_dict






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs

   __lowercase													=				True
   __lowercase													=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,vocab_size=2    )

   return (
       config,
       input_ids,
       token_type_ids,
       encoder_hidden_states,
       encoder_attention_mask,
   )






@require_flax
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    True

  SCREAMING_SNAKE_CASE					:						Dict           =    (
      (
          FlaxRobertaModel,
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				FlaxRobertaModelTester(self    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   for model_class_name in self.all_model_classes:
    __lowercase													=				model_class_name.from_pretrained('''roberta-base'''      ,from_pt=lowercase__    )
    __lowercase													=				model(np.ones((1, 1)    )    )
    self.assertIsNotNone(lowercase__    )



'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


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

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

