
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DebertaForMaskedLM,
         DebertaForQuestionAnswering,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaModel,
     )
     from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      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=5_12		,	lowerCamelCase=16		,	lowerCamelCase=2		,	lowerCamelCase=0.0_2		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase="None"		,	lowerCamelCase=3		,	lowerCamelCase=4		,	lowerCamelCase=None		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_input_mask
          snake_case__						=				use_token_type_ids
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				hidden_size
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				max_position_embeddings
          snake_case__						=				type_vocab_size
          snake_case__						=				type_sequence_label_size
          snake_case__						=				initializer_range
          snake_case__						=				num_labels
          snake_case__						=				num_choices
          snake_case__						=				relative_attention
          snake_case__						=				position_biased_input
          snake_case__						=				pos_att_type
          snake_case__						=				scope
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_input_mask:
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2 )

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

          snake_case__						=				None
          snake_case__						=				None
          snake_case__						=				None
          if self.use_labels:
              snake_case__						=				ids_tensor([self.batch_size]		,	self.type_sequence_label_size )
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels )
              snake_case__						=				ids_tensor([self.batch_size]		,	self.num_choices )

          snake_case__						=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def A_			(			self ):
          return DebertaConfig(
              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		,	initializer_range=self.initializer_range		,	relative_attention=self.relative_attention		,	position_biased_input=self.position_biased_input		,	pos_att_type=self.pos_att_type		,	)
      def A_			(			self ):
          snake_case__						=				self.get_config()
          snake_case__						=				3_00
          return config
      def A_			(			self		,	lowerCamelCase ):
          self.parent.assertListEqual(list(result.loss.size() )		,	[] )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				DebertaModel(config=UpperCamelCase_ )
          model.to(UpperCamelCase_ )
          model.eval()
          snake_case__						=				model(UpperCamelCase_		,	attention_mask=UpperCamelCase_		,	token_type_ids=UpperCamelCase_ )[0]
          snake_case__						=				model(UpperCamelCase_		,	token_type_ids=UpperCamelCase_ )[0]
          snake_case__						=				model(UpperCamelCase_ )[0]

          self.parent.assertListEqual(list(sequence_output.size() )		,	[self.batch_size, self.seq_length, self.hidden_size] )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				DebertaForMaskedLM(config=UpperCamelCase_ )
          model.to(UpperCamelCase_ )
          model.eval()
          snake_case__						=				model(UpperCamelCase_		,	attention_mask=UpperCamelCase_		,	token_type_ids=UpperCamelCase_		,	labels=UpperCamelCase_ )

          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				DebertaForSequenceClassification(UpperCamelCase_ )
          model.to(UpperCamelCase_ )
          model.eval()
          snake_case__						=				model(UpperCamelCase_		,	attention_mask=UpperCamelCase_		,	token_type_ids=UpperCamelCase_		,	labels=UpperCamelCase_ )
          self.parent.assertListEqual(list(result.logits.size() )		,	[self.batch_size, self.num_labels] )
          self.check_loss_output(UpperCamelCase_ )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				DebertaForTokenClassification(config=UpperCamelCase_ )
          model.to(UpperCamelCase_ )
          model.eval()
          snake_case__						=				model(UpperCamelCase_		,	attention_mask=UpperCamelCase_		,	token_type_ids=UpperCamelCase_		,	labels=UpperCamelCase_ )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				DebertaForQuestionAnswering(config=UpperCamelCase_ )
          model.to(UpperCamelCase_ )
          model.eval()
          snake_case__						=				model(
              UpperCamelCase_		,	attention_mask=UpperCamelCase_		,	token_type_ids=UpperCamelCase_		,	start_positions=UpperCamelCase_		,	end_positions=UpperCamelCase_		,	)
          self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          (
              snake_case__    
          )						=				config_and_inputs
          snake_case__						=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		str    	=     (
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _A						:		Union[str, Any]    	=     (
          {
              'feature-extraction': DebertaModel,
              'fill-mask': DebertaForMaskedLM,
              'question-answering': DebertaForQuestionAnswering,
              'text-classification': DebertaForSequenceClassification,
              'token-classification': DebertaForTokenClassification,
              'zero-shot': DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      _A						:		Any    	=     True
      _A						:		str    	=     False
      _A						:		int    	=     False
      _A						:		List[Any]    	=     False
      _A						:		List[str]    	=     False
      def A_			(			self ):
          snake_case__						=				DebertaModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=UpperCamelCase_		,	hidden_size=37 )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*UpperCamelCase_ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase_ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase_ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase_ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase_ )



      @slow
      def A_			(			self ):





          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				DebertaModel.from_pretrained(UpperCamelCase_ )
              self.assertIsNotNone(UpperCamelCase_ )





@require_torch
@require_sentencepiece
@require_tokenizers
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @unittest.skip(reason="""Model not available yet""" )
      def A_			(			self ):
          pass



      @slow
      def A_			(			self ):
          snake_case__						=				DebertaModel.from_pretrained("""microsoft/deberta-base""" )

          snake_case__						=				torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
          snake_case__						=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
              snake_case__						=				model(UpperCamelCase_		,	attention_mask=UpperCamelCase_ )[0]
          # compare the actual values for a slice.
          snake_case__						=				torch.tensor(
              [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]] )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,	UpperCamelCase_		,	atol=1e-4 )		,	F"""{output[:, 1:4, 1:4]}""" )





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         if string == "True":
             return True
         elif string == "False":
             return False
         else:
             raise ValueError(F"""could not parse string as bool {string}"""							)

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



