



import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		)
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
		__UpperCAmelCase						  =     False


class 	lowerCamelCase__ :


						def __init__( self						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  Any=1_4      ,       _a						:  Any=7      ,       _a						:  Optional[int]=True      ,       _a						:  List[str]=False      ,       _a						:  List[Any]=True      ,       _a						:  int=True      ,       _a						:  Any=True      ,       _a						:  str=9_9      ,       _a						:  Any=3_2      ,       _a						:  int=5      ,       _a						:  List[Any]=3_7      ,       _a						:  int="gelu"      ,       _a						:  Dict=0.1      ,       _a						:  Optional[Any]=0.1      ,       _a						:  int=5_1_2      ,       _a						:  Union[str, Any]=1_6      ,       _a						:  Any=2      ,       _a						:  List[Any]=3      ,       _a						:  str=4      ,       _a						:  int=None      ,       ):
													a__:       Union[str, Any]											=parent
													a__:       str											=batch_size
													a__:       Any											=seq_length
													a__:       int											=is_training
													a__:       List[Any]											=use_token_type_ids
													a__:       List[Any]											=use_input_mask
													a__:       Optional[Any]											=use_labels
													a__:       Dict											=use_mc_token_ids
													a__:       Any											=vocab_size
													a__:       List[str]											=hidden_size
													a__:       List[Any]											=num_hidden_layers
													a__:       Optional[Any]											=intermediate_size
													a__:       str											=hidden_act
													a__:       int											=hidden_dropout_prob
													a__:       Optional[int]											=attention_probs_dropout_prob
													a__:       str											=max_position_embeddings
													a__:       Optional[Any]											=type_vocab_size
													a__:       Union[str, Any]											=type_sequence_label_size
													a__:       List[Any]											=num_labels
													a__:       Optional[Any]											=num_choices
													a__:       Any											=scope
													a__:       Tuple											=vocab_size - 1
													a__:       int											=vocab_size - 1
													a__:       List[Any]											=vocab_size - 1


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"			)


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Tuple=False      ,       _a						:  Optional[Any]=False      ,       _a						:  Any=False			):
													a__:       str											=ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

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

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

													a__:       List[Any]											=None
													if self.use_mc_token_ids:
																				a__:       Optional[int]											=ids_tensor([self.batch_size, self.num_choices]      ,       self.seq_length			)

													a__:       int											=None
													a__:       List[Any]											=None
													a__:       Any											=None
													if self.use_labels:
																				a__:       Union[str, Any]											=ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)
																				a__:       List[Any]											=ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels			)
																				a__:       Tuple											=ids_tensor([self.batch_size]      ,       self.num_choices			)

													a__:       Optional[int]											=self.get_config(
													    gradient_checkpointing=_a      ,       scale_attn_by_inverse_layer_idx=_a      ,       reorder_and_upcast_attn=_a      ,       )

													return (
													    config,
													    input_ids,
													    input_mask,
													    None,
													    token_type_ids,
													    mc_token_ids,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													)


						def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]=False      ,       _a						:  int=False      ,       _a						:  Dict=False			):
													return RwkvConfig(
													    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       intermediate_size=self.intermediate_size      ,       activation_function=self.hidden_act      ,       resid_pdrop=self.hidden_dropout_prob      ,       attn_pdrop=self.attention_probs_dropout_prob      ,       n_positions=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       use_cache=_a      ,       bos_token_id=self.bos_token_id      ,       eos_token_id=self.eos_token_id      ,       pad_token_id=self.pad_token_id      ,       gradient_checkpointing=_a      ,       scale_attn_by_inverse_layer_idx=_a      ,       reorder_and_upcast_attn=_a      ,       )


						def   _lowerCamelCase			( self						:  List[str]			):
													a__:       List[str]											=self.get_config()
													a__:       Any											=3_0_0
													return config


						def   _lowerCamelCase			( self						:  str			):
													(
													    (
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			
													):       List[Any]											=self.prepare_config_and_inputs()

													a__:       str											=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
													a__:       Tuple											=ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2			)

													return (
													    config,
													    input_ids,
													    input_mask,
													    head_mask,
													    token_type_ids,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)


						def   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  Dict      ,       _a						:  str      ,       _a						:  List[str]      ,       *_a						:  List[str]			):
													a__:       List[Any]											=True
													a__:       Optional[Any]											=RwkvModel(config=_a			)
													model.to(_a			)
													model.eval()

													a__:       List[str]											=model(_a			)

													self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)
													self.parent.assertEqual(len(result.hidden_states			)      ,       config.num_hidden_layers + 1			)


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Union[str, Any]      ,       _a						:  Union[str, Any]      ,       _a						:  int      ,       _a						:  Union[str, Any]      ,       _a						:  Dict      ,       *_a						:  Dict			):
													a__:       Tuple											=RwkvForCausalLM(_a			)
													model.to(_a			)
													model.eval()

													a__:       Optional[Any]											=model(_a      ,       labels=_a			)
													self.parent.assertEqual(result.loss.shape      ,       ()			)
													self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)			)


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Any      ,       _a						:  Optional[int]      ,       _a						:  Union[str, Any]      ,       _a						:  Any      ,       _a						:  Dict      ,       *_a						:  str			):
													a__:       List[Any]											=RwkvModel(config=_a			)
													model.to(_a			)
													model.eval()

													a__:       Union[str, Any]											=model(_a			)
													a__:       int											=outputs.last_hidden_state

													a__:       Any											=model(input_ids[:, :2]			)
													a__:       Any											=outputs.last_hidden_state

													# Using the state computed on the first inputs, we will get the same output
													a__:       Union[str, Any]											=model(input_ids[:, 2:]      ,       state=outputs.state			)
													a__:       int											=outputs.last_hidden_state

													self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]      ,       dim=1			)      ,       _a      ,       atol=1e-5			)			)


						def   _lowerCamelCase			( self						:  str      ,       _a						:  str      ,       _a						:  Tuple      ,       _a						:  Any      ,       _a						:  str      ,       _a						:  Optional[Any]      ,       *_a						:  int      ,       _a						:  Optional[int]=False			):
													a__:       Union[str, Any]											=RwkvForCausalLM(_a			)
													model.to(_a			)
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													a__:       Any											=model(_a      ,       labels=_a			)
													self.parent.assertEqual(result.loss.shape      ,       ()			)
													self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)			)
													result.loss.backward()







						def   _lowerCamelCase			( self						:  Dict			):
													a__:       Union[str, Any]											=self.prepare_config_and_inputs()

													(
													    (
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			(
													    a__
													)							,			
													):       List[Any]											=config_and_inputs

													a__:       str											={"input_ids": input_ids}

													return config, inputs_dict


@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0				,					reason='''See https://github.com/huggingface/transformers/pull/24204'''   )
@require_torch
class 	lowerCamelCase__ (		_a				,					_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
						_lowerCAmelCase								= (
						    {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
						)
						# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False  # Rwkv does not support head masking


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Any											=RwkvModelTester(self			)
													a__:       List[str]											=ConfigTester(
													    self      ,       config_class=_a      ,       n_embd=3_7      ,       common_properties=["hidden_size", "num_hidden_layers"]			)


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Tuple      ,       _a						:  Union[str, Any]      ,       _a						:  int=None			):
													if isinstance(_a      ,       torch.Tensor			):
																				a__							,			a__:       Optional[int]											=member.max().item(), member.min().item()
													elif isinstance(_a      ,       _a			) or isinstance(_a      ,       _a			):
																				a__							,			a__:       List[Any]											=max(_a			), min(_a			)

													if not isinstance(_a      ,       _a			):
																				raise TypeError("container should be a list or tuple"			)
													elif len(_a			) != 2:
																				raise ValueError("container should have 2 elements"			)

													a__							,			a__:       Optional[Any]											=container

													a__:       Optional[int]											=(min_value >= expected_min) and (max_value <= expected_max)

													if not is_inside_interval:
																				a__:       Union[str, Any]											="%s not found in %s" % (safe_repr(_a			), safe_repr(_a			))
																				self.fail(self._formatMessage(_a      ,       _a			)			)


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


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Optional[int]											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_rwkv_model(*_a			)


						def   _lowerCamelCase			( self						:  int			):
													a__:       Any											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_causl_lm(*_a			)


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Any											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_state_equivalency(*_a			)


						def   _lowerCamelCase			( self						:  Any			):
													a__							,			a__:       str											=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:       Any											=model_class(config=_a			)
																				for name, param in model.named_parameters():
																											if "time_decay" in name:
																																		if param.requires_grad:
																																									self.assertTrue(param.data.max().item() == 3.0			)
																																									self.assertTrue(param.data.min().item() == -5.0			)
																											elif "time_first" in name:
																																		if param.requires_grad:
																																									# check if it's a ones like
																																									self.assertTrue(torch.allclose(param.data      ,       torch.ones_like(param.data			)      ,       atol=1e-5      ,       rtol=1e-5			)			)
																											elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]			):
																																		if param.requires_grad:
																																									self.assertInterval(
																																									    param.data      ,       [0.0, 1.0]      ,       msg=F"Parameter {name} of model {model_class} seems not properly initialized"      ,       )
																											elif "time_mix_value" in name:
																																		if param.requires_grad:
																																									self.assertInterval(
																																									    param.data      ,       [0.0, 1.3]      ,       msg=F"Parameter {name} of model {model_class} seems not properly initialized"      ,       )


						def   _lowerCamelCase			( self						:  int			):
													a__							,			a__:       Union[str, Any]											=self.model_tester.prepare_config_and_inputs_for_common()
													a__:       Any											=True

													a__:       int											=getattr(self.model_tester      ,       "seq_length"      ,       _a			)

													for model_class in self.all_model_classes:
																				a__:       List[Any]											=True
																				a__:       List[str]											=False
																				a__:       int											=True
																				a__:       Optional[Any]											=model_class(_a			)
																				model.to(_a			)
																				model.eval()

																				a__:       str											=self._prepare_for_class(_a      ,       _a			)
																				a__:       str											=inputs["input_ids"].shape[0]
																				with torch.no_grad():
																											a__:       List[Any]											=model(**_a			)
																				a__:       Optional[int]											=outputs.attentions
																				self.assertEqual(len(_a			)      ,       self.model_tester.num_hidden_layers			)

																				# check that output_attentions also work using config
																				del inputs_dict["output_attentions"]
																				a__:       Dict											=True
																				a__:       str											=model_class(_a			)
																				model.to(_a			)
																				model.eval()

																				a__:       Optional[Any]											=self._prepare_for_class(_a      ,       _a			)
																				a__:       str											=inputs["input_ids"].shape[0]
																				with torch.no_grad():
																											a__:       Union[str, Any]											=model(**_a			)
																				a__:       Optional[Any]											=outputs.attentions
																				self.assertEqual(len(_a			)      ,       self.model_tester.num_hidden_layers			)

																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]			)      ,       [batch_size, seq_len, config.hidden_size]      ,       )
																				a__:       str											=len(_a			)

																				# Check attention is always last and order is fine
																				a__:       Tuple											=True
																				a__:       str											=True
																				a__:       Tuple											=model_class(_a			)
																				model.to(_a			)
																				model.eval()

																				a__:       str											=self._prepare_for_class(_a      ,       _a			)
																				a__:       Union[str, Any]											=inputs["input_ids"].shape[0]
																				with torch.no_grad():
																											a__:       str											=model(**_a			)

																				a__:       Optional[Any]											=1
																				self.assertEqual(out_len + added_hidden_states      ,       len(_a			)			)

																				a__:       Dict											=outputs.attentions

																				self.assertEqual(len(_a			)      ,       self.model_tester.num_hidden_layers			)
																				self.assertListEqual(
																				    list(self_attentions[0].shape[-3:]			)      ,       [batch_size, seq_len, config.hidden_size]      ,       )







						@slow
						def   _lowerCamelCase			( self						:  Dict			):
													for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:       Tuple											=RwkvModel.from_pretrained(_a			)
																				self.assertIsNotNone(_a			)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0				,					reason='''See https://github.com/huggingface/transformers/pull/24204'''   )
@slow
class 	lowerCamelCase__ (		unittest.TestCase   ):


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       List[Any]											="RWKV/rwkv-4-169m-pile"
													a__:       Tuple											=AutoTokenizer.from_pretrained(self.model_id			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Dict											="Hello my name is Jasmine and I am a newbie to the"
													a__:       Optional[Any]											=RwkvForCausalLM.from_pretrained(self.model_id			).to(_a			)

													a__:       Optional[int]											=self.tokenizer("Hello my name is"      ,       return_tensors="pt"			).input_ids.to(_a			)
													a__:       Union[str, Any]											=model.generate(_a      ,       max_new_tokens=1_0			)
													a__:       str											=self.tokenizer.decode(output[0].tolist()			)

													self.assertEqual(_a      ,       _a			)







						def   _lowerCamelCase			( self						:  str			):
													a__:       int											="Hello my name is Jasmine and I am a newbie to the"

													a__:       int											=self.tokenizer("Hello my name is"      ,       return_tensors="pt"			).input_ids.to(_a			)
													a__:       Dict											=RwkvForCausalLM.from_pretrained(self.model_id      ,       torch_dtype=torch.bfloataa			).to(_a			)

													a__:       List[Any]											=model.generate(_a      ,       max_new_tokens=1_0			)
													a__:       Optional[int]											=self.tokenizer.decode(output[0].tolist()			)

													self.assertEqual(_a      ,       _a			)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase						  =     {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     ['''SplinterTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     [
		    '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SplinterForQuestionAnswering''',
		    '''SplinterForPreTraining''',
		    '''SplinterLayer''',
		    '''SplinterModel''',
		    '''SplinterPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase						  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





