


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:
      SCREAMING_SNAKE_CASE		:str											=       False


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    str      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    Optional[int]=1_4      ,			_lowerCAmelCase    :    str=7      ,			_lowerCAmelCase    :    int=True      ,			_lowerCAmelCase    :    List[str]=False      ,			_lowerCAmelCase    :    Tuple=True      ,			_lowerCAmelCase    :    Optional[int]=True      ,			_lowerCAmelCase    :    Union[str, Any]=True      ,			_lowerCAmelCase    :    Optional[int]=9_9      ,			_lowerCAmelCase    :    Optional[int]=3_2      ,			_lowerCAmelCase    :    Tuple=5      ,			_lowerCAmelCase    :    List[str]=3_7      ,			_lowerCAmelCase    :    int="gelu"      ,			_lowerCAmelCase    :    int=0.1      ,			_lowerCAmelCase    :    List[str]=0.1      ,			_lowerCAmelCase    :    Any=5_1_2      ,			_lowerCAmelCase    :    Union[str, Any]=1_6      ,			_lowerCAmelCase    :    int=2      ,			_lowerCAmelCase    :    Optional[Any]=3      ,			_lowerCAmelCase    :    int=4      ,			_lowerCAmelCase    :    Dict=None      ,			)				->	Union[str, Any]:




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



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	str:




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



  def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Union[str, Any]=False      ,			_lowerCAmelCase    :    str=False      ,			_lowerCAmelCase    :    str=False					)				->	str:




     """simple docstring"""
     snake_case_         =   ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size					)

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

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

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

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

     snake_case_         =   self.get_config(
         gradient_checkpointing=lowerCamelCase__      ,			scale_attn_by_inverse_layer_idx=lowerCamelCase__      ,			reorder_and_upcast_attn=lowerCamelCase__      ,			)

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



  def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Union[str, Any]=False      ,			_lowerCAmelCase    :    Tuple=False      ,			_lowerCAmelCase    :    List[str]=False					)				->	Dict:




     """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=lowerCamelCase__      ,			bos_token_id=self.bos_token_id      ,			eos_token_id=self.eos_token_id      ,			pad_token_id=self.pad_token_id      ,			gradient_checkpointing=lowerCamelCase__      ,			scale_attn_by_inverse_layer_idx=lowerCamelCase__      ,			reorder_and_upcast_attn=lowerCamelCase__      ,			)



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   self.get_config()
     snake_case_         =   3_0_0
     return config



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Tuple:




     """simple docstring"""
     (
         (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,        
     )         =   self.prepare_config_and_inputs()

     snake_case_         =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
     snake_case_         =   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    :    Dict      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Tuple      ,			_lowerCAmelCase    :    Tuple      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    Any      ,			*_lowerCAmelCase    :    int					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   True
     snake_case_         =   RwkvModel(config=lowerCamelCase__					)
     model.to(lowerCamelCase__					)
     model.eval()

     snake_case_         =   model(lowerCamelCase__					)

     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    :    Dict      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    List[str]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[int]      ,			*_lowerCAmelCase    :    str					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   RwkvForCausalLM(lowerCamelCase__					)
     model.to(lowerCamelCase__					)
     model.eval()

     snake_case_         =   model(lowerCamelCase__      ,			labels=lowerCamelCase__					)
     self.parent.assertEqual(result.loss.shape      ,			()					)
     self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size)					)



  def    lowerCAmelCase__					(       self    :    int      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    Optional[int]      ,			_lowerCAmelCase    :    int      ,			*_lowerCAmelCase    :    Union[str, Any]					)				->	str:




     """simple docstring"""
     snake_case_         =   RwkvModel(config=lowerCamelCase__					)
     model.to(lowerCamelCase__					)
     model.eval()

     snake_case_         =   model(lowerCamelCase__					)
     snake_case_         =   outputs.last_hidden_state

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

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

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



  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    List[str]      ,			_lowerCAmelCase    :    List[str]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[int]      ,			_lowerCAmelCase    :    Any      ,			*_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]=False					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   RwkvForCausalLM(lowerCamelCase__					)
     model.to(lowerCamelCase__					)
     if gradient_checkpointing:
        model.gradient_checkpointing_enable()

     snake_case_         =   model(lowerCamelCase__      ,			labels=lowerCamelCase__					)
     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    :    List[str]					)				->	Any:




     """simple docstring"""
     snake_case_         =   self.prepare_config_and_inputs()

     (
         (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,      (
         snake_case_  
     )      ,        
     )         =   config_and_inputs

     snake_case_         =   {"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   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE						      =    (
      {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
  )
  # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    False  # Rwkv does not support head masking



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   RwkvModelTester(self					)
     snake_case_         =   ConfigTester(
         self      ,			config_class=lowerCamelCase__      ,			n_embd=3_7      ,			common_properties=["hidden_size", "num_hidden_layers"]					)



  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    Any=None					)				->	Optional[Any]:




     """simple docstring"""



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

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

     snake_case_      ,      snake_case_         =   container

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

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



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Dict:




     """simple docstring"""
     self.config_tester.run_common_tests()



  def    lowerCAmelCase__					(       self    :    Any					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_rwkv_model(*lowerCamelCase__					)



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Any:




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_causl_lm(*lowerCamelCase__					)



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_state_equivalency(*lowerCamelCase__					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[str]:




     """simple docstring"""
     snake_case_      ,      snake_case_         =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        snake_case_         =   model_class(config=lowerCamelCase__					)
        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    :    List[Any]					)				->	str:




     """simple docstring"""
     snake_case_      ,      snake_case_         =   self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_         =   True

     snake_case_         =   getattr(self.model_tester      ,			"seq_length"      ,			lowerCamelCase__					)

     for model_class in self.all_model_classes:
        snake_case_         =   True
        snake_case_         =   False
        snake_case_         =   True
        snake_case_         =   model_class(lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()

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

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

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

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

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

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

        snake_case_         =   1
        self.assertEqual(out_len + added_hidden_states      ,			len(lowerCamelCase__					)					)

        snake_case_         =   outputs.attentions

        self.assertEqual(len(lowerCamelCase__					)      ,			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    :    str					)				->	Dict:




     """simple docstring"""



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






@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   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Tuple					)				->	List[Any]:




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



  def    lowerCAmelCase__					(       self    :    int					)				->	Dict:




     """simple docstring"""
     snake_case_         =   "Hello my name is Jasmine and I am a newbie to the"
     snake_case_         =   RwkvForCausalLM.from_pretrained(self.model_id					).to(lowerCamelCase__					)

     snake_case_         =   self.tokenizer("Hello my name is"      ,			return_tensors="pt"					).input_ids.to(lowerCamelCase__					)
     snake_case_         =   model.generate(lowerCamelCase__      ,			max_new_tokens=1_0					)
     snake_case_         =   self.tokenizer.decode(output[0].tolist()					)

     self.assertEqual(lowerCamelCase__      ,			lowerCamelCase__					)



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   "Hello my name is Jasmine and I am a newbie to the"

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

     snake_case_         =   model.generate(lowerCamelCase__      ,			max_new_tokens=1_0					)
     snake_case_         =   self.tokenizer.decode(output[0].tolist()					)

     self.assertEqual(lowerCamelCase__      ,			lowerCamelCase__					)








import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:Dict											=       {'''UserAgent''': UserAgent().random}
SCREAMING_SNAKE_CASE		:Dict											=       '''https://www.mywaifulist.moe/random'''



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:str							)->None:
   '''simple docstring'''






   snake_case_         =   requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							)
   with open(lowerCAmelCase_						,  "wb"							) as file:
      file.write(image.content							)



def      _lowerCAmelCase  (							)->tuple[str, str, str]:
   '''simple docstring'''






   snake_case_         =   BeautifulSoup(requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							).text						,  "html.parser"							)
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:title"}							).attrs["content"]
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:image"}							).attrs["content"]
   snake_case_         =   soup.find("p"						,  id="description"							).get_text()
   snake_case_      ,      snake_case_         =   os.path.splitext(os.path.basename(lowerCAmelCase_							)							)
   snake_case_         =   title.strip().replace(" "						,  "_"							)
   snake_case_         =   F'''{image_title}{image_extension}'''
   save_image(lowerCAmelCase_						,  lowerCAmelCase_							)
   return (title, description, image_title)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE		:Optional[Any]											=       random_anime_character()
   print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')




