'''simple docstring'''





import argparse
import os
import sys
import urllib.request
import zipfile


UpperCAmelCase							   =      ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
UpperCAmelCase							   =      {
    """CoLA""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4""",
    """SST""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8""",
    """MRPC""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc""",
    """QQP""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5""",
    """STS""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5""",
    """MNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce""",
    """SNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df""",
    """QNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601""",
    """RTE""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb""",
    """WNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf""",
    """diagnostic""": """https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D""",
}

UpperCAmelCase							   =      """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
UpperCAmelCase							   =      """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Optional[int]:
     print(f"""Downloading and extracting {task}..."""       )
     a  :    Dict				=						f"""{task}.zip"""
     urllib.request.urlretrieve(TASKaPATH[task]						,      lowerCAmelCase_       )
     with zipfile.ZipFile(lowerCAmelCase_       ) as zip_ref:
          zip_ref.extractall(lowerCAmelCase_       )
     os.remove(lowerCAmelCase_       )
     print('''\tCompleted!'''       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Any:
     print('''Processing MRPC...'''       )
     a  :    Any				=						os.path.join(lowerCAmelCase_						,      '''MRPC'''       )
     if not os.path.isdir(lowerCAmelCase_       ):
          os.mkdir(lowerCAmelCase_       )
     if path_to_data:
          a  :    str				=						os.path.join(lowerCAmelCase_						,      '''msr_paraphrase_train.txt'''       )
          a  :    List[str]				=						os.path.join(lowerCAmelCase_						,      '''msr_paraphrase_test.txt'''       )
     else:
          print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN       )
          a  :    List[str]				=						os.path.join(lowerCAmelCase_						,      '''msr_paraphrase_train.txt'''       )
          a  :    Dict				=						os.path.join(lowerCAmelCase_						,      '''msr_paraphrase_test.txt'''       )
          urllib.request.urlretrieve(lowerCAmelCase_						,      lowerCAmelCase_       )
          urllib.request.urlretrieve(lowerCAmelCase_						,      lowerCAmelCase_       )
     if not os.path.isfile(lowerCAmelCase_       ):
          raise ValueError(f"""Train data not found at {mrpc_train_file}"""       )
     if not os.path.isfile(lowerCAmelCase_       ):
          raise ValueError(f"""Test data not found at {mrpc_test_file}"""       )
     urllib.request.urlretrieve(TASKaPATH['''MRPC''']						,      os.path.join(lowerCAmelCase_						,      '''dev_ids.tsv'''       )       )

     a  :    Dict				=						[]
     with open(os.path.join(lowerCAmelCase_						,      '''dev_ids.tsv'''       )						,      encoding='''utf8'''       ) as ids_fh:
          for row in ids_fh:
               dev_ids.append(row.strip().split('''\t'''       )       )

     with open(lowerCAmelCase_						,      encoding='''utf8'''       ) as data_fh, open(
         os.path.join(lowerCAmelCase_						,      '''train.tsv'''       )						,      '''w'''						,      encoding='''utf8'''       ) as train_fh, open(os.path.join(lowerCAmelCase_						,      '''dev.tsv'''       )						,      '''w'''						,      encoding='''utf8'''       ) as dev_fh:
          a  :    List[str]				=						data_fh.readline()
          train_fh.write(lowerCAmelCase_       )
          dev_fh.write(lowerCAmelCase_       )
          for row in data_fh:
               a  :    Optional[Any]				=						row.strip().split('''\t'''       )
               if [ida, ida] in dev_ids:
                    dev_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)       )
               else:
                    train_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)       )

     with open(lowerCAmelCase_						,      encoding='''utf8'''       ) as data_fh, open(
         os.path.join(lowerCAmelCase_						,      '''test.tsv'''       )						,      '''w'''						,      encoding='''utf8'''       ) as test_fh:
          a  :    List[str]				=						data_fh.readline()
          test_fh.write('''index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'''       )
          for idx, row in enumerate(lowerCAmelCase_       ):
               a  :    Optional[int]				=						row.strip().split('''\t'''       )
               test_fh.write('''%d\t%s\t%s\t%s\t%s\n''' % (idx, ida, ida, sa, sa)       )
     print('''\tCompleted!'''       )







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Tuple:
     print('''Downloading and extracting diagnostic...'''       )
     if not os.path.isdir(os.path.join(lowerCAmelCase_						,      '''diagnostic'''       )       ):
          os.mkdir(os.path.join(lowerCAmelCase_						,      '''diagnostic'''       )       )
     a  :    List[str]				=						os.path.join(lowerCAmelCase_						,      '''diagnostic'''						,      '''diagnostic.tsv'''       )
     urllib.request.urlretrieve(TASKaPATH['''diagnostic''']						,      lowerCAmelCase_       )
     print('''\tCompleted!'''       )
     return







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	List[str]:
     a  :    str				=						task_names.split(''','''       )
     if "all" in task_names:
          a  :    str				=						TASKS
     else:
          a  :    int				=						[]
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(f"""Task {task_name} not found!"""       )
               tasks.append(lowerCAmelCase_       )
     return tasks







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Optional[Any]:
     a  :    int				=						argparse.ArgumentParser()
     parser.add_argument('''--data_dir'''						,      help='''directory to save data to'''						,      type=lowerCAmelCase_						,      default='''glue_data'''       )
     parser.add_argument(
         '''--tasks'''						,      help='''tasks to download data for as a comma separated string'''						,      type=lowerCAmelCase_						,      default='''all'''       )
     parser.add_argument(
         '''--path_to_mrpc'''						,      help='''path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'''						,      type=lowerCAmelCase_						,      default=''''''						,      )
     a  :    List[Any]				=						parser.parse_args(lowerCAmelCase_       )

     if not os.path.isdir(args.data_dir       ):
          os.mkdir(args.data_dir       )
     a  :    int				=						get_tasks(args.tasks       )

     for task in tasks:
          if task == "MRPC":
               format_mrpc(args.data_dir						,      args.path_to_mrpc       )
          elif task == "diagnostic":
               download_diagnostic(args.data_dir       )
          else:
               download_and_extract(lowerCAmelCase_						,      args.data_dir       )


if __name__ == "__main__":
  sys.exit(main(sys.argv[1:]))
import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

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






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=14							,     lowerCAmelCase_=7							,     lowerCAmelCase_=True							,     lowerCAmelCase_=False							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=99							,     lowerCAmelCase_=32							,     lowerCAmelCase_=5							,     lowerCAmelCase_=37							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=5_12							,     lowerCAmelCase_=16							,     lowerCAmelCase_=2							,     lowerCAmelCase_=3							,     lowerCAmelCase_=4							,     lowerCAmelCase_=None							,     )	->int:
        a  :    Optional[int]				=						parent
        a  :    Optional[Any]				=						batch_size
        a  :    Union[str, Any]				=						seq_length
        a  :    int				=						is_training
        a  :    List[Any]				=						use_token_type_ids
        a  :    Tuple				=						use_input_mask
        a  :    Optional[int]				=						use_labels
        a  :    List[str]				=						use_mc_token_ids
        a  :    str				=						vocab_size
        a  :    Any				=						hidden_size
        a  :    List[Any]				=						num_hidden_layers
        a  :    Optional[int]				=						intermediate_size
        a  :    Dict				=						hidden_act
        a  :    Tuple				=						hidden_dropout_prob
        a  :    str				=						attention_probs_dropout_prob
        a  :    Optional[Any]				=						max_position_embeddings
        a  :    Optional[int]				=						type_vocab_size
        a  :    Any				=						type_sequence_label_size
        a  :    Any				=						num_labels
        a  :    int				=						num_choices
        a  :    List[str]				=						scope
        a  :    List[Any]				=						vocab_size - 1
        a  :    Optional[Any]				=						vocab_size - 1
        a  :    Optional[Any]				=						vocab_size - 1
   def _lowercase(    self    )	->Optional[int]:
        return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''    )
   def _lowercase(    self							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False    )	->Tuple:
        a  :    int				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )

        a  :    List[str]				=						None
        if self.use_input_mask:
             a  :    Optional[int]				=						random_attention_mask([self.batch_size, self.seq_length]    )

        a  :    Union[str, Any]				=						None
        if self.use_token_type_ids:
             a  :    str				=						ids_tensor([self.batch_size, self.seq_length]							,     self.type_vocab_size    )

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

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

        a  :    Optional[int]				=						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 _lowercase(    self							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False    )	->Dict:
        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 _lowercase(    self    )	->Optional[Any]:
        a  :    Optional[Any]				=						self.get_config()
        a  :    Any				=						3_00
        return config
   def _lowercase(    self    )	->int:
        (
            (
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							
        )  :    Any				=						self.prepare_config_and_inputs()

        a  :    str				=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
        a  :    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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Optional[int]:
        a  :    List[str]				=						True
        a  :    Union[str, Any]				=						RwkvModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    Tuple				=						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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Optional[int]:
        a  :    List[Any]				=						RwkvForCausalLM(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    List[Any]				=						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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Tuple:
        a  :    int				=						RwkvModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    Dict				=						model(lowerCAmelCase_    )
        a  :    Union[str, Any]				=						outputs.last_hidden_state

        a  :    str				=						model(input_ids[:, :2]    )
        a  :    List[str]				=						outputs.last_hidden_state

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

        self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							,     dim=1    )							,     lowerCAmelCase_							,     atol=1e-5    )    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_							,     lowerCAmelCase_=False    )	->Dict:
        a  :    Dict				=						RwkvForCausalLM(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        if gradient_checkpointing:
             model.gradient_checkpointing_enable()

        a  :    List[Any]				=						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 _lowercase(    self    )	->Tuple:
        a  :    str				=						self.prepare_config_and_inputs()

        (
            (
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							
        )  :    Any				=						config_and_inputs

        a  :    List[str]				=						{'''input_ids''': input_ids}

        return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0       ,      reason='See https://github.com/huggingface/transformers/pull/24204'	)
@require_torch
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__       ,      snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Dict 			=   (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
   UpperCamelCase_				:						List[Any] 			=   (
       {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
   )
   # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
   UpperCamelCase_				:						Optional[Any] 			=   False
   UpperCamelCase_				:						Dict 			=   False
   UpperCamelCase_				:						Optional[int] 			=   False
   UpperCamelCase_				:						int 			=   False
   UpperCamelCase_				:						Optional[int] 			=   False  # Rwkv does not support head masking
   def _lowercase(    self    )	->List[str]:
        a  :    Optional[int]				=						RwkvModelTester(self    )
        a  :    Dict				=						ConfigTester(
            self							,     config_class=lowerCAmelCase_							,     n_embd=37							,     common_properties=['''hidden_size''', '''num_hidden_layers''']    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->Dict:
        if isinstance(lowerCAmelCase_							,     torch.Tensor    ):
             a			,							a  :    Union[str, Any]				=						member.max().item(), member.min().item()
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) or isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a			,							a  :    Optional[Any]				=						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'''    )

        a			,							a  :    Optional[int]				=						container

        a  :    Dict				=						(min_value >= expected_min) and (max_value <= expected_max)

        if not is_inside_interval:
             a  :    Tuple				=						'''%s not found in %s''' % (safe_repr(lowerCAmelCase_    ), safe_repr(lowerCAmelCase_    ))
             self.fail(self._formatMessage(lowerCAmelCase_							,     lowerCAmelCase_    )    )
   def _lowercase(    self    )	->str:
        self.config_tester.run_common_tests()
   def _lowercase(    self    )	->Tuple:
        a  :    int				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_rwkv_model(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Dict:
        a  :    Dict				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causl_lm(*lowerCAmelCase_    )
   def _lowercase(    self    )	->List[str]:
        a  :    str				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_state_equivalency(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[Any]:
        a			,							a  :    str				=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             a  :    List[str]				=						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 _lowercase(    self    )	->str:
        a			,							a  :    Optional[int]				=						self.model_tester.prepare_config_and_inputs_for_common()
        a  :    List[str]				=						True

        a  :    Optional[int]				=						getattr(self.model_tester							,     '''seq_length'''							,     lowerCAmelCase_    )

        for model_class in self.all_model_classes:
             a  :    Tuple				=						True
             a  :    str				=						False
             a  :    str				=						True
             a  :    Union[str, Any]				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Optional[int]				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    Dict				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    int				=						model(**lowerCAmelCase_    )
             a  :    str				=						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"]
             a  :    str				=						True
             a  :    Any				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Tuple				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    Tuple				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    Any				=						model(**lowerCAmelCase_    )
             a  :    Union[str, Any]				=						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]							,     )
             a  :    Optional[Any]				=						len(lowerCAmelCase_    )

             # Check attention is always last and order is fine
             a  :    List[str]				=						True
             a  :    Union[str, Any]				=						True
             a  :    Tuple				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Optional[Any]				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    int				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    List[Any]				=						model(**lowerCAmelCase_    )

             a  :    List[str]				=						1
             self.assertEqual(out_len + added_hidden_states							,     len(lowerCAmelCase_    )    )

             a  :    str				=						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 _lowercase(    self    )	->str:
        for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a  :    List[str]				=						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 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def _lowercase(    self    )	->List[Any]:
        a  :    List[Any]				=						'''RWKV/rwkv-4-169m-pile'''
        a  :    Union[str, Any]				=						AutoTokenizer.from_pretrained(self.model_id    )
   def _lowercase(    self    )	->Tuple:
        a  :    Tuple				=						'''Hello my name is Jasmine and I am a newbie to the'''
        a  :    List[Any]				=						RwkvForCausalLM.from_pretrained(self.model_id    ).to(lowerCAmelCase_    )

        a  :    Optional[int]				=						self.tokenizer('''Hello my name is'''							,     return_tensors='''pt'''    ).input_ids.to(lowerCAmelCase_    )
        a  :    Optional[int]				=						model.generate(lowerCAmelCase_							,     max_new_tokens=10    )
        a  :    Dict				=						self.tokenizer.decode(output[0].tolist()    )

        self.assertEqual(lowerCAmelCase_							,     lowerCAmelCase_    )


   def _lowercase(    self    )	->Union[str, Any]:
        a  :    Any				=						'''Hello my name is Jasmine and I am a newbie to the'''

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

        a  :    Optional[Any]				=						model.generate(lowerCAmelCase_							,     max_new_tokens=10    )
        a  :    Optional[Any]				=						self.tokenizer.decode(output[0].tolist()    )

        self.assertEqual(lowerCAmelCase_							,     lowerCAmelCase_    )