"""simple docstring"""






import unittest

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

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


if is_torch_available():
						import torch

						from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
						from transformers.models.esm.modeling_esm import (
						    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EsmEmbeddings,
						    create_position_ids_from_input_ids,
						)

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Dict=13   , _snake_case			:				Optional[Any]=7   , _snake_case			:				Union[str, Any]=False   , _snake_case			:				Any=True   , _snake_case			:				int=False   , _snake_case			:				int=True   , _snake_case			:				Tuple=33   , _snake_case			:				Optional[int]=32   , _snake_case			:				List[Any]=5   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				int=37   , _snake_case			:				Tuple="gelu"   , _snake_case			:				Dict=0.1   , _snake_case			:				Dict=0.1   , _snake_case			:				Tuple=512   , _snake_case			:				Any=16   , _snake_case			:				Union[str, Any]=2   , _snake_case			:				List[str]=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				int=4   , _snake_case			:				List[str]=None   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_input_mask:
													SCREAMING_SNAKE_CASE__		  =				random_attention_mask([self.batch_size, self.seq_length]		)

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Any:
									return EsmConfig(
									    vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , pad_token_id=1   , 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   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				int   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				EsmModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)

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



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				Optional[int]   , _snake_case			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				EsmForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		False

					a   	=		(
					    (
					        EsmForMaskedLM,
					        EsmModel,
					        EsmForSequenceClassification,
					        EsmForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		()
					a   	=		(
					    {
					        "feature-extraction": EsmModel,
					        "fill-mask": EsmForMaskedLM,
					        "text-classification": EsmForSequenceClassification,
					        "token-classification": EsmForTokenClassification,
					        "zero-shot": EsmForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				EsmModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				str		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor([[12, 31, 13, model.padding_idx]]		)
									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor(
									    [
									        [
									            0 + model.padding_idx + 1,
									            1 + model.padding_idx + 1,
									            2 + model.padding_idx + 1,
									            model.padding_idx,
									        ]
									    ]		)
									SCREAMING_SNAKE_CASE__		  =				create_position_ids_from_input_ids(_snake_case   , model.padding_idx		)
									self.assertEqual(position_ids.shape   , expected_positions.shape		)
									self.assertTrue(torch.all(torch.eq(_snake_case   , _snake_case		)		)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.empty(2   , 4   , 30		)
									SCREAMING_SNAKE_CASE__		  =				[
									    0 + embeddings.padding_idx + 1,
									    1 + embeddings.padding_idx + 1,
									    2 + embeddings.padding_idx + 1,
									    3 + embeddings.padding_idx + 1,
									]
									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor([expected_single_positions, expected_single_positions]		)
									SCREAMING_SNAKE_CASE__		  =				embeddings.create_position_ids_from_inputs_embeds(_snake_case		)
									self.assertEqual(position_ids.shape   , expected_positions.shape		)
									self.assertTrue(torch.all(torch.eq(_snake_case   , _snake_case		)		)		)



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									pass



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									pass



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									pass



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()
													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 2, 3, 4, 5]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]

													SCREAMING_SNAKE_CASE__		  =				33

													SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 6, vocab_size)		)
													self.assertEqual(output.shape   , _snake_case		)

													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]		)
													self.assertTrue(torch.allclose(output[:, :3, :3]   , _snake_case   , atol=1e-4		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()

													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
													# compare the actual values for a slice.
													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]		)
													self.assertTrue(torch.allclose(output[:, :3, :3]   , _snake_case   , atol=1e-4		)		)





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




