






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Tuple=7 ,  __lowerCamelCase				:					Union[str, Any]=True ,  __lowerCamelCase				:					Optional[int]=True ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Union[str, Any]=99 ,  __lowerCamelCase				:					Tuple=32 ,  __lowerCamelCase				:					Any=2 ,  __lowerCamelCase				:					int=4 ,  __lowerCamelCase				:					List[str]=37 ,  __lowerCamelCase				:					Optional[int]="gelu" ,  __lowerCamelCase				:					Union[str, Any]=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					Optional[int]=512 ,  __lowerCamelCase				:					Union[str, Any]=16 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Optional[Any]=False ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Any="None" ,  __lowerCamelCase				:					List[Any]=3 ,  __lowerCamelCase				:					Optional[Any]=4 ,  __lowerCamelCase				:					Any=None ,  ):
		UpperCamelCase     :List[Any]          =	parent
		UpperCamelCase     :Tuple          =	batch_size
		UpperCamelCase     :Union[str, Any]          =	seq_length
		UpperCamelCase     :Dict          =	is_training
		UpperCamelCase     :Dict          =	use_input_mask
		UpperCamelCase     :Union[str, Any]          =	use_token_type_ids
		UpperCamelCase     :Any          =	use_labels
		UpperCamelCase     :int          =	vocab_size
		UpperCamelCase     :Tuple          =	hidden_size
		UpperCamelCase     :int          =	num_hidden_layers
		UpperCamelCase     :List[Any]          =	num_attention_heads
		UpperCamelCase     :List[str]          =	intermediate_size
		UpperCamelCase     :List[Any]          =	hidden_act
		UpperCamelCase     :Any          =	hidden_dropout_prob
		UpperCamelCase     :str          =	attention_probs_dropout_prob
		UpperCamelCase     :Union[str, Any]          =	max_position_embeddings
		UpperCamelCase     :Any          =	type_vocab_size
		UpperCamelCase     :Tuple          =	type_sequence_label_size
		UpperCamelCase     :str          =	initializer_range
		UpperCamelCase     :Tuple          =	num_labels
		UpperCamelCase     :int          =	num_choices
		UpperCamelCase     :Optional[int]          =	relative_attention
		UpperCamelCase     :List[Any]          =	position_biased_input
		UpperCamelCase     :Optional[Any]          =	pos_att_type
		UpperCamelCase     :Dict          =	scope





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :Union[str, Any]          =	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

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

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

		UpperCamelCase     :Dict          =	None
		UpperCamelCase     :Dict          =	None
		UpperCamelCase     :Any          =	None
		if self.use_labels:
			UpperCamelCase     :str          =	ids_tensor([self.batch_size] ,  self.type_sequence_label_size	)
			UpperCamelCase     :int          =	ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels	)

		UpperCamelCase     :Tuple          =	DebertaVaConfig(
		    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 ,  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 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					List[Any]	):
		UpperCamelCase     :Optional[int]          =	TFDebertaVaModel(config=__lowerCamelCase	)
		UpperCamelCase     :List[str]          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

		UpperCamelCase     :Optional[int]          =	[input_ids, input_mask]
		UpperCamelCase     :str          =	model(__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)

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





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	TFDebertaVaForMaskedLM(config=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	{
		    """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 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	self.num_labels
		UpperCamelCase     :Optional[int]          =	TFDebertaVaForSequenceClassification(config=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	{
		    """input_ids""": input_ids,
		    """attention_mask""": input_mask,
		    """token_type_ids""": token_type_ids,
		}

		UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)	)





	def 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :int          =	self.num_labels
		UpperCamelCase     :Optional[Any]          =	TFDebertaVaForTokenClassification(config=__lowerCamelCase	)
		UpperCamelCase     :Dict          =	{
		    """input_ids""": input_ids,
		    """attention_mask""": input_mask,
		    """token_type_ids""": token_type_ids,
		}
		UpperCamelCase     :int          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels)	)





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	TFDebertaVaForQuestionAnswering(config=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	{
		    """input_ids""": input_ids,
		    """attention_mask""": input_mask,
		    """token_type_ids""": token_type_ids,
		}

		UpperCamelCase     :Dict          =	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 						_A (		self				:					Any	):
		UpperCamelCase     :List[Any]          =	self.prepare_config_and_inputs()
		(
		    (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      (
		    UpperCamelCase
		)     ,      
		)     :str          =	config_and_inputs
		UpperCamelCase     :Union[str, Any]          =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
		return config, inputs_dict

@require_tf
class     _SCREAMING_SNAKE_CASE       (			_a		,   _a		,   unittest.TestCase      ):
	snake_case__							:     List[str]     	=  (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	snake_case__							:     Optional[Any]     	=  (
	    {
	        """feature-extraction""": TFDebertaVaModel,
	        """fill-mask""": TFDebertaVaForMaskedLM,
	        """question-answering""": TFDebertaVaForQuestionAnswering,
	        """text-classification""": TFDebertaVaForSequenceClassification,
	        """token-classification""": TFDebertaVaForTokenClassification,
	        """zero-shot""": TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	snake_case__							:     Any     	=  False
	snake_case__							:     Union[str, Any]     	=  False





	def 						_A (		self				:					Any	):
		UpperCamelCase     :Tuple          =	TFDebertaVaModelTester(self	)
		UpperCamelCase     :Optional[int]          =	ConfigTester(self ,  config_class=__lowerCamelCase ,  hidden_size=37	)





	def 						_A (		self				:					Optional[int]	):
		self.config_tester.run_common_tests()





	def 						_A (		self				:					str	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Optional[Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase	)





	@slow
	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Optional[int]          =	TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""	)
		self.assertIsNotNone(__lowerCamelCase	)

@require_tf
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	@unittest.skip(reason="""Model not available yet"""	)
	def 						_A (		self				:					Union[str, Any]	):
		pass





	@slow
	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Dict          =	TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""	)
		UpperCamelCase     :Any          =	tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
		UpperCamelCase     :Optional[Any]          =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase ,  attention_mask=__lowerCamelCase	)[0]

		UpperCamelCase     :Tuple          =	tf.constant(
		    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]	)
		tf.debugging.assert_near(output[:, 1:4, 1:4] ,  __lowerCamelCase ,  atol=1E-4	)








from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
