




from typing import TYPE_CHECKING

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


lowercase_			:       Dict						       =				{'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			:       Optional[int]						       =				['ViltFeatureExtractor']
				lowercase_			:       int						       =				['ViltImageProcessor']
				lowercase_			:       Union[str, Any]						       =				['ViltProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			:       int						       =				[
				    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ViltForImageAndTextRetrieval',
				    'ViltForImagesAndTextClassification',
				    'ViltForTokenClassification',
				    'ViltForMaskedLM',
				    'ViltForQuestionAnswering',
				    'ViltLayer',
				    'ViltModel',
				    'ViltPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vilt import ViltFeatureExtractor
								from .image_processing_vilt import ViltImageProcessor
								from .processing_vilt import ViltProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vilt import (
								    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViltForImageAndTextRetrieval,
								    ViltForImagesAndTextClassification,
								    ViltForMaskedLM,
								    ViltForQuestionAnswering,
								    ViltForTokenClassification,
								    ViltLayer,
								    ViltModel,
								    ViltPreTrainedModel,
								)


else:
				import sys

				lowercase_			:       Dict						       =				_LazyModule(__name__, globals()['__file__'], _import_structure)










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:
				lowercase_			:       str						       =				False






class 					__lowerCAmelCase				:







			def __init__( self					:    Optional[Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    Union[str, Any]=14					,					snake_case__					:    Optional[Any]=7					,					snake_case__					:    int=True					,					snake_case__					:    Optional[int]=False					,					snake_case__					:    Any=True					,					snake_case__					:    Tuple=True					,					snake_case__					:    Union[str, Any]=True					,					snake_case__					:    Optional[int]=99					,					snake_case__					:    List[str]=32					,					snake_case__					:    Union[str, Any]=5					,					snake_case__					:    Dict=37					,					snake_case__					:    int="gelu"					,					snake_case__					:    List[str]=0.1					,					snake_case__					:    Dict=0.1					,					snake_case__					:    str=512					,					snake_case__					:    Any=16					,					snake_case__					:    Dict=2					,					snake_case__					:    int=3					,					snake_case__					:    str=4					,					snake_case__					:    int=None					,					):




									"""simple docstring"""


									_UpperCAmelCase									=  parent
									_UpperCAmelCase									=  batch_size
									_UpperCAmelCase									=  seq_length
									_UpperCAmelCase									=  is_training
									_UpperCAmelCase									=  use_token_type_ids
									_UpperCAmelCase									=  use_input_mask
									_UpperCAmelCase									=  use_labels
									_UpperCAmelCase									=  use_mc_token_ids
									_UpperCAmelCase									=  vocab_size
									_UpperCAmelCase									=  hidden_size
									_UpperCAmelCase									=  num_hidden_layers
									_UpperCAmelCase									=  intermediate_size
									_UpperCAmelCase									=  hidden_act
									_UpperCAmelCase									=  hidden_dropout_prob
									_UpperCAmelCase									=  attention_probs_dropout_prob
									_UpperCAmelCase									=  max_position_embeddings
									_UpperCAmelCase									=  type_vocab_size
									_UpperCAmelCase									=  type_sequence_label_size
									_UpperCAmelCase									=  num_labels
									_UpperCAmelCase									=  num_choices
									_UpperCAmelCase									=  scope
									_UpperCAmelCase									=  vocab_size - 1
									_UpperCAmelCase									=  vocab_size - 1
									_UpperCAmelCase									=  vocab_size - 1







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    Dict=False					,					snake_case__					:    Union[str, Any]=False					,					snake_case__					:    Any=False   ):




									"""simple docstring"""


									_UpperCAmelCase									=  ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size   )

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

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

									_UpperCAmelCase									=  None
									if self.use_mc_token_ids:
															_UpperCAmelCase									=  ids_tensor([self.batch_size, self.num_choices]					,					self.seq_length   )

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

									_UpperCAmelCase									=  self.get_config(
									    gradient_checkpointing=snake_case__					,					scale_attn_by_inverse_layer_idx=snake_case__					,					reorder_and_upcast_attn=snake_case__					,					)

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







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    Dict=False					,					snake_case__					:    Dict=False					,					snake_case__					:    Tuple=False   ):




									"""simple docstring"""


									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=snake_case__					,					bos_token_id=self.bos_token_id					,					eos_token_id=self.eos_token_id					,					pad_token_id=self.pad_token_id					,					gradient_checkpointing=snake_case__					,					scale_attn_by_inverse_layer_idx=snake_case__					,					reorder_and_upcast_attn=snake_case__					,					)







			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.get_config()
									_UpperCAmelCase									=  300
									return config







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									(
									    (
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				
									)									=  self.prepare_config_and_inputs()

									_UpperCAmelCase									=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
									_UpperCAmelCase									=  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 UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    List[str]					,					snake_case__					:    Optional[int]					,					snake_case__					:    int					,					snake_case__					:    Optional[Any]					,					snake_case__					:    str					,					*snake_case__					:    Optional[int]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  True
									_UpperCAmelCase									=  RwkvModel(config=snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(snake_case__   )

									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 UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    str					,					snake_case__					:    Any					,					snake_case__					:    Any					,					snake_case__					:    Optional[Any]					,					snake_case__					:    List[Any]					,					*snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvForCausalLM(snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(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 UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    str					,					snake_case__					:    List[Any]					,					snake_case__					:    Tuple					,					snake_case__					:    List[Any]					,					snake_case__					:    Any					,					*snake_case__					:    Tuple   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvModel(config=snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(snake_case__   )
									_UpperCAmelCase									=  outputs.last_hidden_state

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

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

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







			def UpperCamelCase    ( self					:    Optional[int]					,					snake_case__					:    List[Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    str					,					snake_case__					:    List[str]					,					snake_case__					:    Union[str, Any]					,					*snake_case__					:    Optional[Any]					,					snake_case__					:    Optional[Any]=False   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvForCausalLM(snake_case__   )
									model.to(snake_case__   )
									if gradient_checkpointing:
															model.gradient_checkpointing_enable()

									_UpperCAmelCase									=  model(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)   )
									result.loss.backward()







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.prepare_config_and_inputs()

									(
									    (
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				
									)									=  config_and_inputs

									_UpperCAmelCase									=  {"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				( UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	unittest.TestCase				):
			snake_case_   :				List[str]				  =  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
			snake_case_   :				int				  =  (
			    {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
			)
			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
			snake_case_   :				Tuple				  =  False
			snake_case_   :				Dict				  =  False
			snake_case_   :				int				  =  False
			snake_case_   :				Dict				  =  False
			snake_case_   :				Any				  =  False  # Rwkv does not support head masking







			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvModelTester(self   )
									_UpperCAmelCase									=  ConfigTester(
									    self					,					config_class=snake_case__					,					n_embd=37					,					common_properties=["hidden_size", "num_hidden_layers"]   )







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    List[str]					,					snake_case__					:    Optional[int]					,					snake_case__					:    Tuple=None   ):




									"""simple docstring"""


									if isinstance(snake_case__					,					torch.Tensor   ):
															_UpperCAmelCase							,				_UpperCAmelCase									=  member.max().item(), member.min().item()
									elif isinstance(snake_case__					,					snake_case__   ) or isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase							,				_UpperCAmelCase									=  max(snake_case__   ), min(snake_case__   )

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

									_UpperCAmelCase							,				_UpperCAmelCase									=  container

									_UpperCAmelCase									=  (min_value >= expected_min) and (max_value <= expected_max)

									if not is_inside_interval:
															_UpperCAmelCase									=  "%s not found in %s" % (safe_repr(snake_case__   ), safe_repr(snake_case__   ))
															self.fail(self._formatMessage(snake_case__					,					snake_case__   )   )







			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									self.config_tester.run_common_tests()







			def UpperCamelCase    ( self					:    Tuple   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_rwkv_model(*snake_case__   )







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_causl_lm(*snake_case__   )







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_state_equivalency(*snake_case__   )







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase							,				_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_UpperCAmelCase									=  model_class(config=snake_case__   )
															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 UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase							,				_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase									=  True

									_UpperCAmelCase									=  getattr(self.model_tester					,					"seq_length"					,					snake_case__   )

									for model_class in self.all_model_classes:
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  False
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )
															_UpperCAmelCase									=  outputs.attentions
															self.assertEqual(len(snake_case__   )					,					self.model_tester.num_hidden_layers   )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )
															_UpperCAmelCase									=  outputs.attentions
															self.assertEqual(len(snake_case__   )					,					self.model_tester.num_hidden_layers   )

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

															# Check attention is always last and order is fine
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )

															_UpperCAmelCase									=  1
															self.assertEqual(out_len + added_hidden_states					,					len(snake_case__   )   )

															_UpperCAmelCase									=  outputs.attentions

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







			@slow
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_UpperCAmelCase									=  RwkvModel.from_pretrained(snake_case__   )
															self.assertIsNotNone(snake_case__   )






@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 UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "RWKV/rwkv-4-169m-pile"
									_UpperCAmelCase									=  AutoTokenizer.from_pretrained(self.model_id   )







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "Hello my name is Jasmine and I am a newbie to the"
									_UpperCAmelCase									=  RwkvForCausalLM.from_pretrained(self.model_id   ).to(snake_case__   )

									_UpperCAmelCase									=  self.tokenizer("Hello my name is"					,					return_tensors="pt"   ).input_ids.to(snake_case__   )
									_UpperCAmelCase									=  model.generate(snake_case__					,					max_new_tokens=10   )
									_UpperCAmelCase									=  self.tokenizer.decode(output[0].tolist()   )

									self.assertEqual(snake_case__					,					snake_case__   )







			def UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "Hello my name is Jasmine and I am a newbie to the"

									_UpperCAmelCase									=  self.tokenizer("Hello my name is"					,					return_tensors="pt"   ).input_ids.to(snake_case__   )
									_UpperCAmelCase									=  RwkvForCausalLM.from_pretrained(self.model_id					,					torch_dtype=torch.bfloataa   ).to(snake_case__   )

									_UpperCAmelCase									=  model.generate(snake_case__					,					max_new_tokens=10   )
									_UpperCAmelCase									=  self.tokenizer.decode(output[0].tolist()   )

									self.assertEqual(snake_case__					,					snake_case__   )




