'''simple docstring'''





import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase							   =      {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase							   =      {"""Helsinki-NLP/opus-mt-en-de""": 512}
UpperCAmelCase							   =      {}






class 							_SCREAMING_SNAKE_CASE(   _SCREAMING_SNAKE_CASE	):
   UpperCamelCase_				:						Tuple 			=   VOCAB_FILES_NAMES
   UpperCamelCase_				:						Optional[Any] 			=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase_				:						Any 			=   PRETRAINED_INIT_CONFIGURATION
   UpperCamelCase_				:						Any 			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase_				:						Optional[Any] 			=   ['input_ids', 'attention_mask']
   UpperCamelCase_				:						Optional[Any] 			=   re.compile('>>.+<<'	)  # type: re.Pattern
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_="<unk>"							,     lowerCAmelCase_="</s>"							,     lowerCAmelCase_="<pad>"							,     lowerCAmelCase_=5_12							,     lowerCAmelCase_ = None							,     lowerCAmelCase_=False							,     **lowerCAmelCase_							,     )	->str:
        a  :    Optional[Any]				=						{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
            source_lang=UpperCamelCase__							,     target_lang=UpperCamelCase__							,     unk_token=UpperCamelCase__							,     eos_token=UpperCamelCase__							,     pad_token=UpperCamelCase__							,     model_max_length=UpperCamelCase__							,     sp_model_kwargs=self.sp_model_kwargs							,     target_vocab_file=UpperCamelCase__							,     separate_vocabs=UpperCamelCase__							,     **UpperCamelCase__							,     )
        assert Path(UpperCamelCase__    ).exists(), F"""cannot find spm source {source_spm}"""

        a  :    Optional[Any]				=						separate_vocabs
        a  :    List[Any]				=						load_json(UpperCamelCase__    )
        if self.unk_token not in self.encoder:
             raise KeyError('''<unk> token must be in vocab'''    )
        assert self.pad_token in self.encoder

        if separate_vocabs:
             a  :    Optional[Any]				=						load_json(UpperCamelCase__    )
             a  :    Dict				=						{v: k for k, v in self.target_encoder.items()}
             a  :    Union[str, Any]				=						[]
        else:
             a  :    Optional[int]				=						{v: k for k, v in self.encoder.items()}
             a  :    Any				=						[k for k in self.encoder if k.startswith('''>>'''    ) and k.endswith('''<<'''    )]

        a  :    Union[str, Any]				=						source_lang
        a  :    str				=						target_lang
        a  :    List[str]				=						[source_spm, target_spm]

        # load SentencePiece model for pre-processing
        a  :    List[str]				=						load_spm(UpperCamelCase__							,     self.sp_model_kwargs    )
        a  :    Any				=						load_spm(UpperCamelCase__							,     self.sp_model_kwargs    )
        a  :    Any				=						self.spm_source
        a  :    Optional[int]				=						self.encoder

        # Multilingual target side: default to using first supported language code.

        self._setup_normalizer()
   def _lowercase(    self    )	->Optional[Any]:
        try:
             from sacremoses import MosesPunctNormalizer

             a  :    Optional[int]				=						MosesPunctNormalizer(self.source_lang    ).normalize
        except (ImportError, FileNotFoundError):
             warnings.warn('''Recommended: pip install sacremoses.'''    )
             a  :    Optional[Any]				=						lambda lowerCAmelCase_    : x
   def _lowercase(    self							,     lowerCAmelCase_    )	->Tuple:
        return self.punc_normalizer(UpperCamelCase__    ) if x else ""
   def _lowercase(    self							,     lowerCAmelCase_    )	->Any:
        return self.current_encoder.get(UpperCamelCase__							,     self.current_encoder[self.unk_token]    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->str:
        a  :    int				=						self.language_code_re.match(UpperCamelCase__    )
        a  :    Dict				=						[match.group(0    )] if match else []
        return code, self.language_code_re.sub(''''''							,     UpperCamelCase__    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Dict:
        a			,							a  :    List[Any]				=						self.remove_language_code(UpperCamelCase__    )
        a  :    Optional[int]				=						self.current_spm.encode(UpperCamelCase__							,     out_type=UpperCamelCase__    )
        return code + pieces
   def _lowercase(    self							,     lowerCAmelCase_    )	->Tuple:
        return self.decoder.get(UpperCamelCase__							,     self.unk_token    )
   def _lowercase(    self							,     lowerCAmelCase_							,     **lowerCAmelCase_    )	->Tuple:
        return super().batch_decode(UpperCamelCase__							,     **UpperCamelCase__    )
   def _lowercase(    self							,     lowerCAmelCase_							,     **lowerCAmelCase_    )	->Dict:
        return super().decode(UpperCamelCase__							,     **UpperCamelCase__    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->int:
        a  :    int				=						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        a  :    int				=						[]
        a  :    str				=						''''''
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  out_string += sp_model.decode_pieces(UpperCamelCase__    ) + token + " "
                  a  :    str				=						[]
             else:
                  current_sub_tokens.append(UpperCamelCase__    )
        out_string += sp_model.decode_pieces(UpperCamelCase__    )
        return out_string.strip()
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->List[Any]:
        if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]
   def _lowercase(    self    )	->Optional[Any]:
        a  :    Tuple				=						self.spm_source
        a  :    Dict				=						self.encoder
   def _lowercase(    self    )	->int:
        a  :    int				=						self.spm_target
        if self.separate_vocabs:
             a  :    List[str]				=						self.target_encoder
   @property
   def _lowercase(    self    )	->List[str]:
        return len(self.encoder    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None    )	->Dict:
        if not os.path.isdir(UpperCamelCase__    ):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
        a  :    List[Any]				=						[]

        if self.separate_vocabs:
             a  :    Optional[int]				=						os.path.join(
                 UpperCamelCase__							,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']							,     )
             a  :    Any				=						os.path.join(
                 UpperCamelCase__							,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']							,     )
             save_json(self.encoder							,     UpperCamelCase__    )
             save_json(self.target_encoder							,     UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )
        else:
             a  :    Union[str, Any]				=						os.path.join(
                 UpperCamelCase__							,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']    )
             save_json(self.encoder							,     UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )

        for spm_save_filename, spm_orig_path, spm_model in zip(
            [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]							,     self.spm_files							,     [self.spm_source, self.spm_target]							,     ):
             a  :    Union[str, Any]				=						os.path.join(
                 UpperCamelCase__							,     (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename    )
             if os.path.abspath(UpperCamelCase__    ) != os.path.abspath(UpperCamelCase__    ) and os.path.isfile(UpperCamelCase__    ):
                  copyfile(UpperCamelCase__							,     UpperCamelCase__    )
                  saved_files.append(UpperCamelCase__    )
             elif not os.path.isfile(UpperCamelCase__    ):
                  with open(UpperCamelCase__							,     '''wb'''    ) as fi:
                       a  :    Optional[Any]				=						spm_model.serialized_model_proto()
                       fi.write(UpperCamelCase__    )
                  saved_files.append(UpperCamelCase__    )

        return tuple(UpperCamelCase__    )
   def _lowercase(    self    )	->List[str]:
        return self.get_src_vocab()
   def _lowercase(    self    )	->Dict:
        return dict(self.encoder							,     **self.added_tokens_encoder    )
   def _lowercase(    self    )	->Optional[int]:
        return dict(self.target_encoder							,     **self.added_tokens_decoder    )
   def __getstate__(    self    )	->int:
        a  :    Any				=						self.__dict__.copy()
        state.update(
            {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}    )
        return state
   def __setstate__(    self							,     lowerCAmelCase_    )	->str:
        a  :    int				=						d

        # for backward compatibility
        if not hasattr(self							,     '''sp_model_kwargs'''    ):
             a  :    List[str]				=						{}

        a			,							a  :    Union[str, Any]				=						(load_spm(UpperCamelCase__							,     self.sp_model_kwargs    ) for f in self.spm_files)
        a  :    Any				=						self.spm_source
        self._setup_normalizer()
   def _lowercase(    self							,     *lowerCAmelCase_							,     **lowerCAmelCase_    )	->List[Any]:
        return 1
   def _lowercase(    self							,     lowerCAmelCase_    )	->Dict:
        a  :    Optional[int]				=						set(self.all_special_ids    )  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special
        return [1 if x in all_special_ids else 0 for x in seq]


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False    )	->Any:
        if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase__    )
        elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase__    ) + [1]
        else:
             return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	str:
     a  :    List[str]				=						sentencepiece.SentencePieceProcessor(**lowerCAmelCase_       )
     spm.Load(lowerCAmelCase_       )
     return spm







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Dict:







     with open(lowerCAmelCase_						,      '''w'''       ) as f:
          json.dump(lowerCAmelCase_						,      lowerCAmelCase_						,      indent=2       )







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	str:







     with open(lowerCAmelCase_						,      '''r'''       ) as f:
          return json.load(lowerCAmelCase_       )
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_    )