'''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:
 snake_case__	:     str      =    False






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=1_4 ,						snake_case_=7 ,						snake_case_=True ,						snake_case_=False ,						snake_case_=True ,						snake_case_=True ,						snake_case_=True ,						snake_case_=9_9 ,						snake_case_=3_2 ,						snake_case_=5 ,						snake_case_=3_7 ,						snake_case_="gelu" ,						snake_case_=0.1 ,						snake_case_=0.1 ,						snake_case_=5_1_2 ,						snake_case_=1_6 ,						snake_case_=2 ,						snake_case_=3 ,						snake_case_=4 ,						snake_case_=None ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = parent
       UpperCAmelCase_				:  List[str]  = batch_size
       UpperCAmelCase_				:  int  = seq_length
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_token_type_ids
       UpperCAmelCase_				:  Optional[Any]  = use_input_mask
       UpperCAmelCase_				:  int  = use_labels
       UpperCAmelCase_				:  Optional[Any]  = use_mc_token_ids
       UpperCAmelCase_				:  Optional[Any]  = vocab_size
       UpperCAmelCase_				:  Any  = hidden_size
       UpperCAmelCase_				:  Optional[int]  = num_hidden_layers
       UpperCAmelCase_				:  str  = intermediate_size
       UpperCAmelCase_				:  int  = hidden_act
       UpperCAmelCase_				:  Union[str, Any]  = hidden_dropout_prob
       UpperCAmelCase_				:  Optional[Any]  = attention_probs_dropout_prob
       UpperCAmelCase_				:  Any  = max_position_embeddings
       UpperCAmelCase_				:  str  = type_vocab_size
       UpperCAmelCase_				:  Dict  = type_sequence_label_size
       UpperCAmelCase_				:  Union[str, Any]  = num_labels
       UpperCAmelCase_				:  str  = num_choices
       UpperCAmelCase_				:  Any  = scope
       UpperCAmelCase_				:  int  = vocab_size - 1
       UpperCAmelCase_				:  str  = vocab_size - 1
       UpperCAmelCase_				:  Union[str, Any]  = vocab_size - 1


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size		)

       UpperCAmelCase_				:  Union[str, Any]  = None
       if self.use_input_mask:
            UpperCAmelCase_				:  Optional[int]  = random_attention_mask([self.batch_size, self.seq_length]		)

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

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

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

       UpperCAmelCase_				:  List[str]  = 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 ,						snake_case_=False ,						snake_case_=False ,						snake_case_=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		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.get_config()
       UpperCAmelCase_				:  int  = 3_0_0
       return config


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       (
           (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     
       )				:  Tuple  = self.prepare_config_and_inputs()

       UpperCAmelCase_				:  Tuple  = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       UpperCAmelCase_				:  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 _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = True
       UpperCAmelCase_				:  Dict  = RwkvModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()

       UpperCAmelCase_				:  List[Any]  = 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 ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = RwkvForCausalLM(snake_case_		)
       model.to(snake_case_		)
       model.eval()

       UpperCAmelCase_				:  Tuple  = 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 ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = RwkvModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()

       UpperCAmelCase_				:  Any  = model(snake_case_		)
       UpperCAmelCase_				:  int  = outputs.last_hidden_state

       UpperCAmelCase_				:  Union[str, Any]  = model(input_ids[:, :2]		)
       UpperCAmelCase_				:  List[Any]  = outputs.last_hidden_state

       # Using the state computed on the first inputs, we will get the same output
       UpperCAmelCase_				:  Dict  = model(input_ids[:, 2:] ,						state=outputs.state		)
       UpperCAmelCase_				:  Union[str, Any]  = 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 ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_ ,						snake_case_=False		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = RwkvForCausalLM(snake_case_		)
       model.to(snake_case_		)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       UpperCAmelCase_				:  Any  = 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		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.prepare_config_and_inputs()

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

       UpperCAmelCase_				:  List[Any]  = {'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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = RwkvModelTester(self		)
       UpperCAmelCase_				:  Union[str, Any]  = ConfigTester(
           self ,						config_class=snake_case_ ,						n_embd=3_7 ,						common_properties=['hidden_size', 'num_hidden_layers']		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=None		):




       '''simple docstring'''

       if isinstance(snake_case_ ,						torch.Tensor		):
            UpperCAmelCase_  ,     UpperCAmelCase_				:  str  = member.max().item(), member.min().item()
       elif isinstance(snake_case_ ,						snake_case_		) or isinstance(snake_case_ ,						snake_case_		):
            UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = 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_				:  Dict  = container

       UpperCAmelCase_				:  Optional[Any]  = (min_value >= expected_min) and (max_value <= expected_max)

       if not is_inside_interval:
            UpperCAmelCase_				:  int  = '%s not found in %s' % (safe_repr(snake_case_		), safe_repr(snake_case_		))
            self.fail(self._formatMessage(snake_case_ ,						snake_case_		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_rwkv_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_causl_lm(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_state_equivalency(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  str  = 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		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Union[str, Any]  = True

       UpperCAmelCase_				:  List[Any]  = getattr(self.model_tester ,						'seq_length' ,						snake_case_		)

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  int  = True
            UpperCAmelCase_				:  Any  = False
            UpperCAmelCase_				:  Union[str, Any]  = True
            UpperCAmelCase_				:  List[Any]  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            UpperCAmelCase_				:  Any  = self._prepare_for_class(snake_case_ ,						snake_case_		)
            UpperCAmelCase_				:  List[Any]  = inputs['input_ids'].shape[0]
            with torch.no_grad():
                 UpperCAmelCase_				:  Any  = model(**snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = 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_				:  Any  = True
            UpperCAmelCase_				:  Any  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            UpperCAmelCase_				:  List[Any]  = self._prepare_for_class(snake_case_ ,						snake_case_		)
            UpperCAmelCase_				:  Tuple  = inputs['input_ids'].shape[0]
            with torch.no_grad():
                 UpperCAmelCase_				:  List[Any]  = model(**snake_case_		)
            UpperCAmelCase_				:  Optional[int]  = 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_				:  Any  = len(snake_case_		)

            # Check attention is always last and order is fine
            UpperCAmelCase_				:  Dict  = True
            UpperCAmelCase_				:  Optional[Any]  = True
            UpperCAmelCase_				:  Any  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            UpperCAmelCase_				:  Tuple  = self._prepare_for_class(snake_case_ ,						snake_case_		)
            UpperCAmelCase_				:  Any  = inputs['input_ids'].shape[0]
            with torch.no_grad():
                 UpperCAmelCase_				:  Tuple  = model(**snake_case_		)

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

            UpperCAmelCase_				:  str  = 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		):




       '''simple docstring'''

       for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  int  = 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 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = 'RWKV/rwkv-4-169m-pile'
       UpperCAmelCase_				:  int  = AutoTokenizer.from_pretrained(self.model_id		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = 'Hello my name is Jasmine and I am a newbie to the'
       UpperCAmelCase_				:  List[Any]  = RwkvForCausalLM.from_pretrained(self.model_id		).to(snake_case_		)

       UpperCAmelCase_				:  str  = self.tokenizer('Hello my name is' ,						return_tensors='pt'		).input_ids.to(snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = model.generate(snake_case_ ,						max_new_tokens=1_0		)
       UpperCAmelCase_				:  Optional[int]  = self.tokenizer.decode(output[0].tolist()		)

       self.assertEqual(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 'Hello my name is Jasmine and I am a newbie to the'

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

       UpperCAmelCase_				:  Any  = model.generate(snake_case_ ,						max_new_tokens=1_0		)
       UpperCAmelCase_				:  Tuple  = self.tokenizer.decode(output[0].tolist()		)

       self.assertEqual(snake_case_ ,						snake_case_		)







'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )