
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


lowercase  :					Optional[List[str]]						    =							None
lowercase  :					List[Any]						    =							"""<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase  :					Dict						    =							[
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]

@dataclass
class __snake_case		:
    _a		:						bool=				True
    _a		:						Optional[str]=				None
    # Automatically constructed
    _a		:						ClassVar[str]=				"PIL.Image.Image"
    _a		:						ClassVar[Any]=				pa.struct({"bytes": pa.binary(), "path": pa.string()}     )
    _a		:						str=				field(default="Image"					,	init=lowerCAmelCase					,	repr=lowerCAmelCase     )
    def __call__(  self      ):



           '''simple docstring'''







           return self.pa_type
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError("""To support encoding images, please install 'Pillow'."""      )

           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     int       	=      np.array(snake_case      )

           if isinstance(snake_case		,snake_case      ):
                  return {"path": value, "bytes": None}
           elif isinstance(snake_case		,snake_case      ):
                  return {"path": None, "bytes": value}
           elif isinstance(snake_case		,np.ndarray      ):
                  # convert the image array to PNG/TIFF bytes
                  return encode_np_array(snake_case      )
           elif isinstance(snake_case		,PIL.Image.Image      ):
                  # convert the PIL image to bytes (default format is PNG/TIFF)
                  return encode_pil_image(snake_case      )
           elif value.get("""path"""      ) is not None and os.path.isfile(value["""path"""]      ):
                  # we set "bytes": None to not duplicate the data if they're already available locally
                  return {"bytes": None, "path": value.get("""path"""      )}
           elif value.get("""bytes"""      ) is not None or value.get("""path"""      ) is not None:
                  # store the image bytes, and path is used to infer the image format using the file extension
                  return {"bytes": value.get("""bytes"""      ), "path": value.get("""path"""      )}
           else:
                  raise ValueError(
                      f"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if not self.decode:
                  raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""      )

           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError("""To support decoding images, please install 'Pillow'."""      )

           if token_per_repo_id is None:
                  lowercase							:     List[Any]       	=      {}

           lowercase	,			lowercase							:     Tuple       	=      value["""path"""], value["""bytes"""]
           if bytes_ is None:
                  if path is None:
                         raise ValueError(f"An image should have one of 'path' or 'bytes' but both are None in {value}."      )
                  else:
                         if is_local_path(snake_case      ):
                                lowercase							:     List[str]       	=      PIL.Image.open(snake_case      )
                         else:
                                lowercase							:     str       	=      path.split("""::"""      )[-1]
                                try:
                                       lowercase							:     Any       	=      string_to_dict(snake_case		,config.HUB_DATASETS_URL      )["""repo_id"""]
                                       lowercase							:     str       	=      token_per_repo_id.get(snake_case      )
                                except ValueError:
                                       lowercase							:     Optional[int]       	=      None
                                with xopen(snake_case		,"""rb"""		,use_auth_token=snake_case      ) as f:
                                       lowercase							:     Any       	=      BytesIO(f.read()      )
                                lowercase							:     int       	=      PIL.Image.open(bytes_      )
           else:
                  lowercase							:     str       	=      PIL.Image.open(BytesIO(bytes_      )      )
           image.load()  # to avoid "Too many open files" errors
           return image
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           from .features import Value

           return (
               self
               if self.decode
               else {
                   "bytes": Value("""binary"""      ),
                   "path": Value("""string"""      ),
               }
           )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if pa.types.is_string(storage.type      ):
                  lowercase							:     Dict       	=      pa.array([None] * len(snake_case      )		,type=pa.binary()      )
                  lowercase							:     List[str]       	=      pa.StructArray.from_arrays([bytes_array, storage]		,["""bytes""", """path"""]		,mask=storage.is_null()      )
           elif pa.types.is_binary(storage.type      ):
                  lowercase							:     Union[str, Any]       	=      pa.array([None] * len(snake_case      )		,type=pa.string()      )
                  lowercase							:     Optional[int]       	=      pa.StructArray.from_arrays([storage, path_array]		,["""bytes""", """path"""]		,mask=storage.is_null()      )
           elif pa.types.is_struct(storage.type      ):
                  if storage.type.get_field_index("""bytes"""      ) >= 0:
                         lowercase							:     str       	=      storage.field("""bytes"""      )
                  else:
                         lowercase							:     Tuple       	=      pa.array([None] * len(snake_case      )		,type=pa.binary()      )
                  if storage.type.get_field_index("""path"""      ) >= 0:
                         lowercase							:     int       	=      storage.field("""path"""      )
                  else:
                         lowercase							:     Optional[int]       	=      pa.array([None] * len(snake_case      )		,type=pa.string()      )
                  lowercase							:     Dict       	=      pa.StructArray.from_arrays([bytes_array, path_array]		,["""bytes""", """path"""]		,mask=storage.is_null()      )
           elif pa.types.is_list(storage.type      ):
                  lowercase							:     List[str]       	=      pa.array(
                      [encode_np_array(np.array(snake_case      )      )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]		,type=pa.binary()		,)
                  lowercase							:     Any       	=      pa.array([None] * len(snake_case      )		,type=pa.string()      )
                  lowercase							:     List[Any]       	=      pa.StructArray.from_arrays(
                      [bytes_array, path_array]		,["""bytes""", """path"""]		,mask=bytes_array.is_null()      )
           return array_cast(snake_case		,self.pa_type      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           @no_op_if_value_is_null
           def path_to_bytes(snake_case      ):
                  with xopen(snake_case		,"""rb"""      ) as f:
                         lowercase							:     int       	=      f.read()
                  return bytes_

           lowercase							:     List[str]       	=      pa.array(
               [
                   (path_to_bytes(x["""path"""]      ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
                   for x in storage.to_pylist()
               ]		,type=pa.binary()		,)
           lowercase							:     str       	=      pa.array(
               [os.path.basename(snake_case      ) if path is not None else None for path in storage.field("""path"""      ).to_pylist()]		,type=pa.string()		,)
           lowercase							:     List[Any]       	=      pa.StructArray.from_arrays([bytes_array, path_array]		,["""bytes""", """path"""]		,mask=bytes_array.is_null()      )
           return array_cast(snake_case		,self.pa_type      )







def 						_snake_case(  ) ->    List[str]:
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("""To support encoding images, please install 'Pillow'."""				)

       global _IMAGE_COMPRESSION_FORMATS
       if _IMAGE_COMPRESSION_FORMATS is None:
              PIL.Image.init()
              lowercase							:     Optional[int]       	=      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
       return _IMAGE_COMPRESSION_FORMATS







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    bytes:
       lowercase							:     Optional[Any]       	=      BytesIO()
       if image.format in list_image_compression_formats():
              lowercase							:     Union[str, Any]       	=      image.format
       else:
              lowercase							:     Dict       	=      """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
       image.save(SCREAMING_SNAKE_CASE__					,	format=SCREAMING_SNAKE_CASE__				)
       return buffer.getvalue()







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    dict:
       if hasattr(SCREAMING_SNAKE_CASE__					,	"""filename"""				) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE__				)}







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    dict:
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("""To support encoding images, please install 'Pillow'."""				)

       lowercase							:     Optional[int]       	=      array.dtype
       lowercase							:     Union[str, Any]       	=      dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
       lowercase							:     Tuple       	=      dtype.kind
       lowercase							:     Any       	=      dtype.itemsize

       lowercase							:     Any       	=      None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              lowercase							:     Optional[Any]       	=      np.dtype("""|u1"""				)
              if dtype_kind not in ["u", "i"]:
                     raise TypeError(
                         f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."				)
              if dtype is not dest_dtype:
                     warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"				)
    # Exact match
       elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
              lowercase							:     Dict       	=      dtype
       else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
              while dtype_itemsize >= 1:
                     lowercase							:     Optional[int]       	=      dtype_byteorder + dtype_kind + str(SCREAMING_SNAKE_CASE__				)
                     lowercase							:     int       	=      np.dtype(SCREAMING_SNAKE_CASE__				)
                     if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                            warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"				)
                            break
                     else:
                            dtype_itemsize //= 2

       if dest_dtype is None:
              raise TypeError(
                  f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"				)
       lowercase							:     Tuple       	=      PIL.Image.fromarray(array.astype(SCREAMING_SNAKE_CASE__				)				)
       return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE__				)}







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[dict]:
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("""To support encoding images, please install 'Pillow'."""				)

       if objs:
              lowercase	,			lowercase							:     Optional[int]       	=      first_non_null_value(SCREAMING_SNAKE_CASE__				)
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(SCREAMING_SNAKE_CASE__					,	np.ndarray				):
                     lowercase							:     str       	=      no_op_if_value_is_null(SCREAMING_SNAKE_CASE__				)
                     return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE__				) for obj in objs]
              elif isinstance(SCREAMING_SNAKE_CASE__					,	PIL.Image.Image				):
                     lowercase							:     List[str]       	=      no_op_if_value_is_null(SCREAMING_SNAKE_CASE__				)
                     return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE__				) for obj in objs]
              else:
                     return objs
       else:
              return objs






import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class       __lowerCAmelCase				:






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Union[str, Any]=True				,    _lowerCAmelCase:    str=99				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Dict=5				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    Optional[Any]=4				,    _lowerCAmelCase:    Any="gelu"				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Optional[int]=True				,    _lowerCAmelCase:    Any=5_12				,    _lowerCAmelCase:    Optional[Any]=16				,    _lowerCAmelCase:    str=2				,    _lowerCAmelCase:    Tuple=0.02				,    _lowerCAmelCase:    str=3				,    _lowerCAmelCase:    List[str]=4				,    _lowerCAmelCase:    Optional[Any]=None				,    ):
         lowercase			:List[str]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Dict							=  is_training
         lowercase			:Union[str, Any]							=  use_input_mask
         lowercase			:Optional[int]							=  use_token_type_ids
         lowercase			:Dict							=  use_labels
         lowercase			:Any							=  vocab_size
         lowercase			:List[Any]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Union[str, Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_multiple_size
         lowercase			:str							=  hidden_act
         lowercase			:List[Any]							=  hidden_dropout
         lowercase			:Optional[Any]							=  attention_dropout
         lowercase			:int							=  weight_tying
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:str							=  type_vocab_size
         lowercase			:Union[str, Any]							=  type_sequence_label_size
         lowercase			:Dict							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:Dict							=  num_choices
         lowercase			:Any							=  scope






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

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

         lowercase			:int							=  None
         if self.use_labels:
           lowercase			:Dict							=  ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels )

         lowercase			:Tuple							=  self.get_config()

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return GPTNeoXJapaneseConfig(
             vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_multiple_size=self.intermediate_multiple_size				,    hidden_act=self.hidden_act				,    hidden_dropout=self.hidden_dropout				,    attention_dropout=self.attention_dropout				,    weight_tying=self.weight_tying				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=_lowerCAmelCase				,    initializer_range=self.initializer_range				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Any							=  self.prepare_config_and_inputs()

         lowercase			:Tuple							=  True

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  GPTNeoXJapaneseModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Optional[int]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         lowercase			:Dict							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  GPTNeoXJapaneseModel(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:str							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Tuple							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    labels=_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  True
         lowercase			:List[str]							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()

         # first forward pass
         lowercase			:str							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )
         lowercase			:Tuple							=  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase			:Optional[int]							=  ids_tensor((self.batch_size, 3)				,    config.vocab_size )
         lowercase			:Tuple							=  ids_tensor((self.batch_size, 3)				,    vocab_size=2 )

         # append to next input_ids and
         lowercase			:Tuple							=  torch.cat([input_ids, next_tokens]				,    dim=-1 )
         lowercase			:List[Any]							=  torch.cat([input_mask, next_mask]				,    dim=-1 )

         lowercase			:Dict							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase )
         lowercase			:List[str]							=  output_from_no_past["hidden_states"][0]
         lowercase			:str							=  model(
             _lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase				,    )["hidden_states"][0]

         # select random slice
         lowercase			:Dict							=  ids_tensor((1,)				,    output_from_past.shape[-1] ).item()
         lowercase			:str							=  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase			:List[str]							=  output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(_lowerCAmelCase				,    _lowerCAmelCase				,    atol=1e-3 ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:str							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs
         lowercase			:Union[str, Any]							=  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       _a           =  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       _a           =  (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       _a           =  False
       _a           =  False
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  GPTNeoXJapaneseModelTester(self )
         lowercase			:List[str]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         # This regression test was failing with PyTorch < 1.3
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase			:Union[str, Any]							=  None

         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  "abeja/gpt-neox-japanese-2.7b"

         lowercase			:Optional[int]							=  ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

         lowercase			:int							=  [
             "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
             "100年後に必要とされる会社は、「人」が中心の会社です。",
             "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
             "国境の長いトンネルを抜けると、そこは雪国だった。",
             "美味しい日本食といえば、やっぱりお寿司ですよね。",
         ]

         lowercase			:List[str]							=  GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
         lowercase			:Tuple							=  GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )

         lowercase			:List[str]							=  []
         for prompt in prompts:
           lowercase			:Optional[int]							=  tokenizer(_lowerCAmelCase				,    return_tensors="pt" ).input_ids
           lowercase			:Union[str, Any]							=  model.generate(_lowerCAmelCase				,    max_length=50 )
           lowercase			:List[str]							=  tokenizer.batch_decode(_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase )
           predicted_outputs += generated_string
         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )



