from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 import (
     TFDebertaForMaskedLM,
     TFDebertaForQuestionAnswering,
     TFDebertaForSequenceClassification,
     TFDebertaForTokenClassification,
     TFDebertaModel,
 )




class 				__lowerCAmelCase					:



 '''simple docstring'''

 def __init__(     self    :						Any    ,     lowerCamelCase    :						List[Any]    ,     lowerCamelCase    :						Dict=1_3    ,     lowerCamelCase    :						int=7    ,     lowerCamelCase    :						List[Any]=True    ,     lowerCamelCase    :						Optional[Any]=True    ,     lowerCamelCase    :						Optional[Any]=True    ,     lowerCamelCase    :						Union[str, Any]=True    ,     lowerCamelCase    :						Any=9_9    ,     lowerCamelCase    :						str=3_2    ,     lowerCamelCase    :						Optional[int]=2    ,     lowerCamelCase    :						Union[str, Any]=4    ,     lowerCamelCase    :						Optional[int]=3_7    ,     lowerCamelCase    :						Optional[int]="gelu"    ,     lowerCamelCase    :						List[Any]=0.1    ,     lowerCamelCase    :						Optional[int]=0.1    ,     lowerCamelCase    :						Optional[int]=5_1_2    ,     lowerCamelCase    :						int=1_6    ,     lowerCamelCase    :						Any=2    ,     lowerCamelCase    :						Optional[int]=0.0_2    ,     lowerCamelCase    :						List[Any]=3    ,     lowerCamelCase    :						Union[str, Any]=4    ,     lowerCamelCase    :						Optional[int]=None    ,     )	->							int:
        _UpperCamelCase       :    Optional[Any]					  =					parent
        _UpperCamelCase       :    Optional[Any]					  =					1_3
        _UpperCamelCase       :    Tuple					  =					7
        _UpperCamelCase       :    List[Any]					  =					True
        _UpperCamelCase       :    Optional[int]					  =					True
        _UpperCamelCase       :    Optional[Any]					  =					True
        _UpperCamelCase       :    Tuple					  =					True
        _UpperCamelCase       :    Optional[int]					  =					9_9
        _UpperCamelCase       :    Tuple					  =					3_2
        _UpperCamelCase       :    Union[str, Any]					  =					2
        _UpperCamelCase       :    Union[str, Any]					  =					4
        _UpperCamelCase       :    Dict					  =					3_7
        _UpperCamelCase       :    Dict					  =					'gelu'
        _UpperCamelCase       :    Union[str, Any]					  =					0.1
        _UpperCamelCase       :    List[str]					  =					0.1
        _UpperCamelCase       :    Optional[Any]					  =					5_1_2
        _UpperCamelCase       :    List[str]					  =					1_6
        _UpperCamelCase       :    Union[str, Any]					  =					False
        _UpperCamelCase       :    int					  =					-1
        _UpperCamelCase       :    List[Any]					  =					True
        _UpperCamelCase       :    Union[str, Any]					  =					2
        _UpperCamelCase       :    List[Any]					  =					0.0_2
        _UpperCamelCase       :    Union[str, Any]					  =					3
        _UpperCamelCase       :    Any					  =					4
        _UpperCamelCase       :    Tuple					  =					None

 def    __snake_case   (     self    :						Any					)	->							List[str]:
        _UpperCamelCase       :    int					  =					ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size					)

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

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

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

        _UpperCamelCase       :    Dict					  =					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    ,     relative_attention=self.relative_attention    ,     max_relative_positions=self.max_relative_positions    ,     position_biased_input=self.position_biased_input    ,     initializer_range=self.initializer_range    ,     return_dict=lowerCamelCase    ,     )

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

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						int					)	->							int:
        _UpperCamelCase       :    Optional[int]					  =					TFDebertaModel(config=lowerCamelCase					)
        _UpperCamelCase       :    Union[str, Any]					  =					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _UpperCamelCase       :    List[Any]					  =					[input_ids, input_mask]
        _UpperCamelCase       :    str					  =					model(lowerCamelCase					)

        _UpperCamelCase       :    List[Any]					  =					model(lowerCamelCase					)

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

 def    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Dict					)	->							int:
        _UpperCamelCase       :    Optional[Any]					  =					TFDebertaForMaskedLM(config=lowerCamelCase					)
        _UpperCamelCase       :    str					  =					{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _UpperCamelCase       :    Union[str, Any]					  =					model(lowerCamelCase					)
        self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)					)

 def    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						List[Any]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						Optional[Any]					)	->							str:
        _UpperCamelCase       :    List[Any]					  =					self.num_labels
        _UpperCamelCase       :    Union[str, Any]					  =					TFDebertaForSequenceClassification(config=lowerCamelCase					)
        _UpperCamelCase       :    str					  =					{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _UpperCamelCase       :    List[Any]					  =					model(lowerCamelCase					)
        self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)					)

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						List[Any]    ,     lowerCamelCase    :						Dict					)	->							int:
        _UpperCamelCase       :    str					  =					self.num_labels
        _UpperCamelCase       :    List[Any]					  =					TFDebertaForTokenClassification(config=lowerCamelCase					)
        _UpperCamelCase       :    Dict					  =					{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _UpperCamelCase       :    str					  =					model(lowerCamelCase					)
        self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)					)

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						Dict					)	->							Optional[int]:
        _UpperCamelCase       :    Any					  =					TFDebertaForQuestionAnswering(config=lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _UpperCamelCase       :    Union[str, Any]					  =					model(lowerCamelCase					)
        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    __snake_case   (     self    :						Tuple					)	->							Dict:
        _UpperCamelCase       :    Tuple					  =					self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , (
            _UpperCamelCase
        )  , 
        )       :    Optional[Any]					  =					config_and_inputs
        _UpperCamelCase       :    Dict					  =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







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



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int]			      =	(
     (
         TFDebertaModel,
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 _lowerCamelCase  :		Tuple			      =	(
     {
         'feature-extraction': TFDebertaModel,
         'fill-mask': TFDebertaForMaskedLM,
         'question-answering': TFDebertaForQuestionAnswering,
         'text-classification': TFDebertaForSequenceClassification,
         'token-classification': TFDebertaForTokenClassification,
         'zero-shot': TFDebertaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 _lowerCamelCase  :		Dict			      =	False
 _lowerCamelCase  :		Dict			      =	False

 def    __snake_case   (     self    :						Tuple					)	->							List[str]:
        _UpperCamelCase       :    Any					  =					TFDebertaModelTester(self					)
        _UpperCamelCase       :    Dict					  =					ConfigTester(self    ,     config_class=lowerCamelCase    ,     hidden_size=3_7					)

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

 def    __snake_case   (     self    :						str					)	->							List[Any]:
        _UpperCamelCase       :    Union[str, Any]					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase					)

 def    __snake_case   (     self    :						str					)	->							Optional[Any]:
        _UpperCamelCase       :    Tuple					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Dict:
        _UpperCamelCase       :    int					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase					)

 def    __snake_case   (     self    :						Optional[int]					)	->							List[Any]:
        _UpperCamelCase       :    Optional[int]					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase					)

 def    __snake_case   (     self    :						List[Any]					)	->							int:
        _UpperCamelCase       :    Optional[int]					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase					)

 @slow
 def    __snake_case   (     self    :						str					)	->							Tuple:
        _UpperCamelCase       :    Dict					  =					TFDebertaModel.from_pretrained('kamalkraj/deberta-base'					)
        self.assertIsNotNone(lowerCamelCase					)







@require_tf
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 @unittest.skip(reason='Model not available yet'					)
 def    __snake_case   (     self    :						int					)	->							int:
        pass

 @slow
 def    __snake_case   (     self    :						Union[str, Any]					)	->							Any:
        _UpperCamelCase       :    Optional[int]					  =					TFDebertaModel.from_pretrained('kamalkraj/deberta-base'					)
        _UpperCamelCase       :    Tuple					  =					tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					)
        _UpperCamelCase       :    List[Any]					  =					tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
        _UpperCamelCase       :    Union[str, Any]					  =					model(lowerCamelCase    ,     attention_mask=lowerCamelCase					)[0]

        _UpperCamelCase       :    List[str]					  =					tf.constant(
            [
                [
                    [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
                    [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
                    [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
                ]
            ]					)
        tf.debugging.assert_near(output[:, 1:4, 1:4]    ,     lowerCamelCase    ,     atol=1e-4					)




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



