


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
	    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    OpenAIGPTConfig,
	    OpenAIGPTDoubleHeadsModel,
	    OpenAIGPTForSequenceClassification,
	    OpenAIGPTLMHeadModel,
	    OpenAIGPTModel,
	)





class  A       :


							def __init__(self ,							lowerCAmelCase ,							lowerCAmelCase=1_3 ,							lowerCAmelCase=7 ,							lowerCAmelCase=True ,							lowerCAmelCase=True ,							lowerCAmelCase=True ,							lowerCAmelCase=9_9 ,							lowerCAmelCase=3_2 ,							lowerCAmelCase=5 ,							lowerCAmelCase=4 ,							lowerCAmelCase=3_7 ,							lowerCAmelCase="gelu" ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=5_1_2 ,							lowerCAmelCase=1_6 ,							lowerCAmelCase=2 ,							lowerCAmelCase=0.02 ,							lowerCAmelCase=3 ,							lowerCAmelCase=4 ,							lowerCAmelCase=None ,							):
									__lowercase=     parent
									__lowercase=     batch_size
									__lowercase=     seq_length
									__lowercase=     is_training
									__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_labels
									__lowercase=     num_choices
									__lowercase=     scope
									__lowercase=     self.vocab_size - 1


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

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

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

									__lowercase=     OpenAIGPTConfig(
									    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 ,							)

									__lowercase=     ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,							2   )

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


							def      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							*lowerCAmelCase   ):
									__lowercase=     OpenAIGPTModel(config=snake_case_   )
									model.to(snake_case_   )
									model.eval()

									__lowercase=     model(snake_case_ ,							token_type_ids=snake_case_ ,							head_mask=snake_case_   )
									__lowercase=     model(snake_case_ ,							token_type_ids=snake_case_   )
									__lowercase=     model(snake_case_   )

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


							def      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							*lowerCAmelCase   ):
									__lowercase=     OpenAIGPTLMHeadModel(snake_case_   )
									model.to(snake_case_   )
									model.eval()

									__lowercase=     model(snake_case_ ,							token_type_ids=snake_case_ ,							labels=snake_case_   )
									self.parent.assertEqual(result.loss.shape ,							()   )
									self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)   )


							def      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							*lowerCAmelCase   ):
									__lowercase=     OpenAIGPTDoubleHeadsModel(snake_case_   )
									model.to(snake_case_   )
									model.eval()

									__lowercase=     model(snake_case_ ,							token_type_ids=snake_case_ ,							labels=snake_case_   )
									self.parent.assertEqual(result.loss.shape ,							()   )
									self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)   )


							def      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							*lowerCAmelCase   ):
									__lowercase=     self.num_labels
									__lowercase=     OpenAIGPTForSequenceClassification(snake_case_   )
									model.to(snake_case_   )
									model.eval()

									__lowercase=     ids_tensor([self.batch_size] ,							self.type_sequence_label_size   )
									__lowercase=     model(snake_case_ ,							token_type_ids=snake_case_ ,							labels=snake_case_   )
									self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)   )







							def      _A			(self   ):
									__lowercase=     self.prepare_config_and_inputs()
									(
									    __lowercase							
									)=     config_and_inputs
									__lowercase=     {
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """head_mask""": head_mask,
									}

									return config, inputs_dict





@require_torch
class  A       (  _a   ,							_a   ,							_a   ,							unittest.TestCase					):
							UpperCamelCase_  :				Any			=(
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase_  :				Optional[Any]			=(
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							UpperCamelCase_  :				str			=(
							    {
							        '''feature-extraction''': OpenAIGPTModel,
							        '''text-classification''': OpenAIGPTForSequenceClassification,
							        '''text-generation''': OpenAIGPTLMHeadModel,
							        '''zero-shot''': OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase   ):
									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
											# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
											# `OpenAIGPTConfig` 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      _A			(self ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase=False   ):
									__lowercase=     super()._prepare_for_class(snake_case_ ,							snake_case_ ,							return_labels=snake_case_   )

									if return_labels:
											if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
													__lowercase=     torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,							dtype=torch.long ,							device=snake_case_ ,							)
													__lowercase=     inputs_dict["""labels"""]
													__lowercase=     inputs_dict["""labels"""]
													__lowercase=     torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices) ,							dtype=torch.long ,							device=snake_case_ ,							)
													__lowercase=     torch.zeros(
													    self.model_tester.batch_size ,							dtype=torch.long ,							device=snake_case_   )
									return inputs_dict


							def      _A			(self   ):
									__lowercase=     OpenAIGPTModelTester(self   )
									__lowercase=     ConfigTester(self ,							config_class=snake_case_ ,							n_embd=3_7   )


							def      _A			(self   ):
									self.config_tester.run_common_tests()


							def      _A			(self   ):
									__lowercase=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*snake_case_   )


							def      _A			(self   ):
									__lowercase=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*snake_case_   )


							def      _A			(self   ):
									__lowercase=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_double_lm_head_model(*snake_case_   )


							def      _A			(self   ):
									__lowercase=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case_   )







							@slow
							def      _A			(self   ):
									for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase=     OpenAIGPTModel.from_pretrained(snake_case_   )
											self.assertIsNotNone(snake_case_   )





@require_torch
class  A       (  unittest.TestCase					):


							@slow
							def      _A			(self   ):
									__lowercase=     OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'   )
									model.to(snake_case_   )
									__lowercase=     torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]] ,							dtype=torch.long ,							device=snake_case_   )  # the president is
									__lowercase=     [
									    4_8_1,
									    4_7_3_5,
									    5_4_4,
									    2_4_6,
									    9_6_3,
									    8_7_0,
									    7_6_2,
									    2_3_9,
									    2_4_4,
									    4_0_4_7_7,
									    2_4_4,
									    2_4_9,
									    7_1_9,
									    8_8_1,
									    4_8_7,
									    5_4_4,
									    2_4_0,
									    2_4_4,
									    6_0_3,
									    4_8_1,
									]  # the president is a very good man. " \n " i\'m sure he is, " said the

									__lowercase=     model.generate(snake_case_ ,							do_sample=snake_case_   )
									self.assertListEqual(output_ids[0].tolist() ,							snake_case_   )











'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

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

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= 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=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						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)
       __a  			=						parser.parse_args()

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