
'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output









"""simple docstring"""


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 :    Union[str, Any]      =			False






class __a :



  def __init__(self	:		str					,      lowercase_	:		Dict					,      lowercase_	:		str=14					,      lowercase_	:		List[str]=7					,      lowercase_	:		Any=True					,      lowercase_	:		List[str]=False					,      lowercase_	:		int=True					,      lowercase_	:		List[str]=True					,      lowercase_	:		str=True					,      lowercase_	:		Union[str, Any]=99					,      lowercase_	:		str=32					,      lowercase_	:		List[Any]=5					,      lowercase_	:		List[str]=37					,      lowercase_	:		str="gelu"					,      lowercase_	:		Any=0.1					,      lowercase_	:		Dict=0.1					,      lowercase_	:		List[Any]=512					,      lowercase_	:		Any=16					,      lowercase_	:		Union[str, Any]=2					,      lowercase_	:		Tuple=3					,      lowercase_	:		Optional[int]=4					,      lowercase_	:		int=None					,      )		->				Tuple:
   __A						:							Any      =						parent
   __A						:							int      =						batch_size
   __A						:							int      =						seq_length
   __A						:							int      =						is_training
   __A						:							int      =						use_token_type_ids
   __A						:							Union[str, Any]      =						use_input_mask
   __A						:							Any      =						use_labels
   __A						:							Union[str, Any]      =						use_mc_token_ids
   __A						:							List[Any]      =						vocab_size
   __A						:							List[str]      =						hidden_size
   __A						:							List[Any]      =						num_hidden_layers
   __A						:							Union[str, Any]      =						intermediate_size
   __A						:							Union[str, Any]      =						hidden_act
   __A						:							int      =						hidden_dropout_prob
   __A						:							Optional[Any]      =						attention_probs_dropout_prob
   __A						:							List[str]      =						max_position_embeddings
   __A						:							List[Any]      =						type_vocab_size
   __A						:							Tuple      =						type_sequence_label_size
   __A						:							Union[str, Any]      =						num_labels
   __A						:							Tuple      =						num_choices
   __A						:							int      =						scope
   __A						:							Union[str, Any]      =						vocab_size - 1
   __A						:							int      =						vocab_size - 1
   __A						:							Optional[Any]      =						vocab_size - 1



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )



  def     A_					(self	:		Dict					,      lowercase_	:		str=False					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		int=False )		->				List[str]:
   __A						:							List[Any]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )

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

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

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

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

   __A						:							Any      =						self.get_config(
       gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )

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



  def     A_					(self	:		Optional[int]					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		Dict=False					,      lowercase_	:		List[str]=False )		->				Any:
   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=lowercase_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )



  def     A_					(self	:		Optional[int] )		->				Dict:
   __A						:							Any      =						self.get_config()
   __A						:							Any      =						300
   return config



  def     A_					(self	:		Tuple )		->				Dict:
   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							Tuple      =						self.prepare_config_and_inputs()

   __A						:							Tuple      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						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     A_					(self	:		Any					,      lowercase_	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str					,      *lowercase_	:		Any )		->				Tuple:
   __A						:							List[str]      =						True
   __A						:							str      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							List[Any]      =						model(lowercase_ )

   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     A_					(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[Any]					,      *lowercase_	:		int )		->				Union[str, Any]:
   __A						:							Any      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Union[str, Any]      =						model(lowercase_					,      labels=lowercase_ )
   self.parent.assertEqual(result.loss.shape					,      () )
   self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size) )



  def     A_					(self	:		str					,      lowercase_	:		List[Any]					,      lowercase_	:		Dict					,      lowercase_	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Dict					,      *lowercase_	:		Optional[Any] )		->				Union[str, Any]:
   __A						:							Any      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Tuple      =						model(lowercase_ )
   __A						:							str      =						outputs.last_hidden_state

   __A						:							Optional[int]      =						model(input_ids[:, :2] )
   __A						:							Dict      =						outputs.last_hidden_state

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

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



  def     A_					(self	:		List[str]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      *lowercase_	:		Dict					,      lowercase_	:		List[Any]=False )		->				str:
   __A						:							Tuple      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   __A						:							Optional[int]      =						model(lowercase_					,      labels=lowercase_ )
   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     A_					(self	:		int )		->				Tuple:
   __A						:							Union[str, Any]      =						self.prepare_config_and_inputs()

   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							int      =						config_and_inputs

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



  def     A_					(self	:		List[str] )		->				Any:
   __A						:							List[str]      =						RwkvModelTester(self )
   __A						:							List[Any]      =						ConfigTester(
       self					,      config_class=lowercase_					,      n_embd=37					,      common_properties=['hidden_size', 'num_hidden_layers'] )



  def     A_					(self	:		int					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[Any]=None )		->				Tuple:
   if isinstance(lowercase_					,      torch.Tensor ):
    __A     ,       __A						:							str      =						member.max().item(), member.min().item()
   elif isinstance(lowercase_					,      lowercase_ ) or isinstance(lowercase_					,      lowercase_ ):
    __A     ,       __A						:							List[str]      =						max(lowercase_ ), min(lowercase_ )

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

   __A     ,       __A						:							Optional[Any]      =						container

   __A						:							List[str]      =						(min_value >= expected_min) and (max_value <= expected_max)

   if not is_inside_interval:
    __A						:							Optional[int]      =						'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
    self.fail(self._formatMessage(lowercase_					,      lowercase_ ) )



  def     A_					(self	:		int )		->				Optional[Any]:
   self.config_tester.run_common_tests()



  def     A_					(self	:		Tuple )		->				str:
   __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_rwkv_model(*lowercase_ )



  def     A_					(self	:		List[Any] )		->				Union[str, Any]:
   __A						:							Any      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_causl_lm(*lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_state_equivalency(*lowercase_ )



  def     A_					(self	:		List[str] )		->				int:
   __A     ,       __A						:							Optional[Any]      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A						:							Optional[int]      =						model_class(config=lowercase_ )
    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     A_					(self	:		str )		->				Any:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   __A						:							Optional[Any]      =						True

   __A						:							List[str]      =						getattr(self.model_tester					,      'seq_length'					,      lowercase_ )

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

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

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A						:							Optional[int]      =						True
    __A						:							Dict      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							Any      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Any      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							List[Any]      =						model(**lowercase_ )
    __A						:							Optional[Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

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

    # Check attention is always last and order is fine
    __A						:							Any      =						True
    __A						:							str      =						True
    __A						:							List[str]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[Any]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Union[str, Any]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Dict      =						model(**lowercase_ )

    __A						:							Dict      =						1
    self.assertEqual(out_len + added_hidden_states					,      len(lowercase_ ) )

    __A						:							Optional[Any]      =						outputs.attentions

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



  @slow
  def     A_					(self	:		Optional[Any] )		->				Any:
   for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A						:							Optional[int]      =						RwkvModel.from_pretrained(lowercase_ )
    self.assertIsNotNone(lowercase_ )






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



  def     A_					(self	:		str )		->				Optional[Any]:
   __A						:							Optional[int]      =						'RWKV/rwkv-4-169m-pile'
   __A						:							Tuple      =						AutoTokenizer.from_pretrained(self.model_id )



  def     A_					(self	:		Optional[Any] )		->				str:
   __A						:							int      =						'Hello my name is Jasmine and I am a newbie to the'
   __A						:							str      =						RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

   __A						:							Union[str, Any]      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							List[str]      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							List[str]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )



  def     A_					(self	:		Any )		->				Optional[Any]:
   __A						:							Optional[int]      =						'Hello my name is Jasmine and I am a newbie to the'

   __A						:							Any      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							int      =						RwkvForCausalLM.from_pretrained(self.model_id					,      torch_dtype=torch.bfloataa ).to(lowercase_ )

   __A						:							str      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							Union[str, Any]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )





