


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a=True       ,		_a="pt"		)					->							Any:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       {'''add_prefix_space''': True} if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__		) and not line.startswith(''' '''		) else {}
     lowercase_       :Optional[Any]     =       padding_side
     return tokenizer(
         [line]       ,		max_length=UpperCAmelCase__       ,		padding='''max_length''' if pad_to_max_length else None       ,		truncation=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__       ,		**UpperCAmelCase__       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None       ,		)					->							Dict:
     '''simple docstring'''





     lowercase_       :Tuple     =       input_ids.ne(UpperCAmelCase__		).any(dim=0		)
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])






class      UpperCamelCase					(      _a     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_="train"   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=""   ,							):
        super().__init__()
        lowercase_       :Union[str, Any]     =       Path(_A		).joinpath(type_path + '''.source'''		)
        lowercase_       :Optional[int]     =       Path(_A		).joinpath(type_path + '''.target'''		)
        lowercase_       :Optional[Any]     =       self.get_char_lens(self.src_file		)
        lowercase_       :Dict     =       max_source_length
        lowercase_       :Dict     =       max_target_length
        assert min(self.src_lens		) > 0, f"found empty line in {self.src_file}"
        lowercase_       :List[Any]     =       tokenizer
        lowercase_       :Any     =       prefix
        if n_obs is not None:
             lowercase_       :Union[str, Any]     =       self.src_lens[:n_obs]
        lowercase_       :Union[str, Any]     =       src_lang
        lowercase_       :List[str]     =       tgt_lang
   def __len__(     self		):
        return len(self.src_lens		)
   def __getitem__(     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[Any]     =       index + 1  # linecache starts at 1
        lowercase_       :Tuple     =       self.prefix + linecache.getline(str(self.src_file		)   ,							_A		).rstrip('''\n'''		)
        lowercase_       :Tuple     =       linecache.getline(str(self.tgt_file		)   ,							_A		).rstrip('''\n'''		)
        assert source_line, f"empty source line for index {index}"
        assert tgt_line, f"empty tgt line for index {index}"

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer   ,							_A		):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        lowercase_       :Optional[int]     =       (
            self.tokenizer.question_encoder if isinstance(self.tokenizer   ,							_A		) else self.tokenizer
        )
        lowercase_       :Optional[int]     =       self.tokenizer.generator if isinstance(self.tokenizer   ,							_A		) else self.tokenizer

        lowercase_       :Union[str, Any]     =       encode_line(_A   ,							_A   ,							self.max_source_length   ,							'''right'''		)
        lowercase_       :List[str]     =       encode_line(_A   ,							_A   ,							self.max_target_length   ,							'''right'''		)

        lowercase_       :int     =       source_inputs['''input_ids'''].squeeze()
        lowercase_       :int     =       target_inputs['''input_ids'''].squeeze()
        lowercase_       :Tuple     =       source_inputs['''attention_mask'''].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }
   @staticmethod
   def 		UpperCamelCase  (     UpperCamelCase_		):
        return [len(_A		) for x in Path(_A		).open().readlines()]




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :List[Any]     =       torch.stack([x['''input_ids'''] for x in batch]		)
        lowercase_       :int     =       torch.stack([x['''attention_mask'''] for x in batch]		)
        lowercase_       :str     =       torch.stack([x['''decoder_input_ids'''] for x in batch]		)
        lowercase_       :List[Any]     =       (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer   ,							_A		)
            else self.tokenizer.pad_token_id
        )
        lowercase_       :Optional[int]     =       (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer   ,							_A		)
            else self.tokenizer.pad_token_id
        )
        lowercase_       :Dict     =       trim_batch(_A   ,							_A		)
        lowercase_    ,      lowercase_       :List[Any]     =       trim_batch(_A   ,							_A   ,							attention_mask=_A		)
        lowercase_       :Optional[int]     =       {
            '''input_ids''': source_ids,
            '''attention_mask''': source_mask,
            '''decoder_input_ids''': y,
        }
        return batch
SCREAMING_SNAKE_CASE       :		Optional[int]         =				getLogger(__name__)




def     UpperCamelCase    (      _a		)					->							Dict:
     '''simple docstring'''





     return list(itertools.chain.from_iterable(UpperCAmelCase__		)		)




def     UpperCamelCase    (      _a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :str     =       get_git_info()
     save_json(UpperCAmelCase__       ,		os.path.join(UpperCAmelCase__       ,		'''git_log.json'''		)		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a=4       ,		**_a		)					->							Any:
     '''simple docstring'''





     with open(UpperCAmelCase__       ,		'''w'''		) as f:
          json.dump(UpperCAmelCase__       ,		UpperCAmelCase__       ,		indent=UpperCAmelCase__       ,		**UpperCAmelCase__		)




def     UpperCamelCase    (      _a		)					->							Tuple:
     '''simple docstring'''





     with open(UpperCAmelCase__		) as f:
          return json.load(UpperCAmelCase__		)




def     UpperCamelCase    (      )					->							int:
     '''simple docstring'''





     lowercase_       :Tuple     =       git.Repo(search_parent_directories=UpperCAmelCase__		)
     lowercase_       :Dict     =       {
         '''repo_id''': str(UpperCAmelCase__		),
         '''repo_sha''': str(repo.head.object.hexsha		),
         '''repo_branch''': str(repo.active_branch		),
         '''hostname''': str(socket.gethostname()		),
     }
     return repo_infos




def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     return list(map(UpperCAmelCase__       ,		UpperCAmelCase__		)		)




def     UpperCamelCase    (      _a       ,		_a		)					->							Tuple:
     '''simple docstring'''





     with open(UpperCAmelCase__       ,		'''wb'''		) as f:
          return pickle.dump(UpperCAmelCase__       ,		UpperCAmelCase__		)




def     UpperCamelCase    (      _a		)					->							List[str]:
     '''simple docstring'''





     def remove_articles(_a		):
          return re.sub(R'''\b(a|an|the)\b'''       ,		''' '''       ,		UpperCAmelCase__		)

     def white_space_fix(_a		):
          return " ".join(text.split()		)

     def remove_punc(_a		):
          lowercase_       :Optional[int]     =       set(string.punctuation		)
          return "".join(ch for ch in text if ch not in exclude		)

     def lower(_a		):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__		)		)		)		)




def     UpperCamelCase    (      _a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Tuple     =       normalize_answer(UpperCAmelCase__		).split()
     lowercase_       :Optional[int]     =       normalize_answer(UpperCAmelCase__		).split()
     lowercase_       :int     =       Counter(UpperCAmelCase__		) & Counter(UpperCAmelCase__		)
     lowercase_       :Tuple     =       sum(common.values()		)
     if num_same == 0:
          return 0
     lowercase_       :List[Any]     =       1.0 * num_same / len(UpperCAmelCase__		)
     lowercase_       :Optional[int]     =       1.0 * num_same / len(UpperCAmelCase__		)
     lowercase_       :Optional[Any]     =       (2 * precision * recall) / (precision + recall)
     return fa




def     UpperCamelCase    (      _a       ,		_a		)					->							Dict:
     '''simple docstring'''





     return normalize_answer(UpperCAmelCase__		) == normalize_answer(UpperCAmelCase__		)




def     UpperCamelCase    (      _a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     assert len(UpperCAmelCase__		) == len(UpperCAmelCase__		)
     lowercase_       :List[str]     =       0
     for hypo, pred in zip(UpperCAmelCase__       ,		UpperCAmelCase__		):
          em += exact_match_score(UpperCAmelCase__       ,		UpperCAmelCase__		)
     if len(UpperCAmelCase__		) > 0:
          em /= len(UpperCAmelCase__		)
     return {"em": em}




def     UpperCamelCase    (      _a		)					->							List[str]:
     '''simple docstring'''





     return model_prefix.startswith('''rag'''		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Dict:
     '''simple docstring'''





     lowercase_       :List[str]     =       {p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     lowercase_       :Optional[Any]     =       '''dropout_rate'''
     for p in extra_params:
          if getattr(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__		):
               if not hasattr(UpperCAmelCase__       ,		UpperCAmelCase__		) and not hasattr(UpperCAmelCase__       ,		equivalent_param[p]		):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCAmelCase__		)		)
                    delattr(UpperCAmelCase__       ,		UpperCAmelCase__		)
                    continue
               lowercase_       :Optional[Any]     =       p if hasattr(UpperCAmelCase__       ,		UpperCAmelCase__		) else equivalent_param[p]
               setattr(UpperCAmelCase__       ,		UpperCAmelCase__       ,		getattr(UpperCAmelCase__       ,		UpperCAmelCase__		)		)
               delattr(UpperCAmelCase__       ,		UpperCAmelCase__		)
     return hparams, config





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							):
        lowercase_       :Dict     =       parent
        lowercase_       :Optional[Any]     =       13
        lowercase_       :Optional[Any]     =       7
        lowercase_       :List[Any]     =       30
        lowercase_       :int     =       self.seq_length + self.mem_len
        lowercase_       :Any     =       15
        lowercase_       :Optional[Any]     =       True
        lowercase_       :List[Any]     =       True
        lowercase_       :Any     =       99
        lowercase_       :Optional[int]     =       [10, 50, 80]
        lowercase_       :Union[str, Any]     =       32
        lowercase_       :List[Any]     =       32
        lowercase_       :Tuple     =       4
        lowercase_       :Tuple     =       8
        lowercase_       :List[Any]     =       128
        lowercase_       :Any     =       2
        lowercase_       :Tuple     =       2
        lowercase_       :Dict     =       None
        lowercase_       :Optional[Any]     =       1
        lowercase_       :Optional[int]     =       0
        lowercase_       :List[str]     =       3
        lowercase_       :Optional[int]     =       self.vocab_size - 1
        lowercase_       :List[Any]     =       0.01
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
        lowercase_       :List[str]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       None
        if self.use_labels:
             lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       TransfoXLConfig(
            vocab_size=self.vocab_size   ,							mem_len=self.mem_len   ,							clamp_len=self.clamp_len   ,							cutoffs=self.cutoffs   ,							d_model=self.hidden_size   ,							d_embed=self.d_embed   ,							n_head=self.num_attention_heads   ,							d_head=self.d_head   ,							d_inner=self.d_inner   ,							div_val=self.div_val   ,							n_layer=self.num_hidden_layers   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.vocab_size - 1   ,							init_range=self.init_range   ,							num_labels=self.num_labels   ,							)

        return (config, input_ids_a, input_ids_a, lm_labels)
   def 		UpperCamelCase  (     self		):
        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :List[Any]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Dict     =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Optional[int]     =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        lowercase_    ,      lowercase_       :Optional[int]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_    ,      lowercase_       :Tuple     =       model([input_ids_a, mems_a]		).to_tuple()

        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        lowercase_    ,      lowercase_       :Union[str, Any]     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       TFTransfoXLForSequenceClassification(UpperCamelCase_		)
        lowercase_       :int     =       model(UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        ((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))       :Tuple     =       config_and_inputs
        lowercase_       :Dict     =       {'''input_ids''': input_ids_a}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   lowercase					:	Dict									=() if is_tf_available() else ()
   lowercase					:	List[str]									=(
       {
           """feature-extraction""": TFTransfoXLModel,
           """text-classification""": TFTransfoXLForSequenceClassification,
           """text-generation""": TFTransfoXLLMHeadModel,
           """zero-shot""": TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   lowercase					:	Optional[int]									=False
   lowercase					:	Tuple									=False
   lowercase					:	Dict									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

        return False
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       TFTransfoXLModelTester(self		)
        lowercase_       :str     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							d_embed=37		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :List[str]     =       [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             lowercase_       :Dict     =       model_class(UpperCamelCase_		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                  lowercase_       :str     =       model.get_output_embeddings()
                  assert isinstance(UpperCamelCase_   ,							tf.keras.layers.Layer		)
                  lowercase_       :Optional[int]     =       model.get_bias()
                  assert name is None
             else:
                  lowercase_       :List[Any]     =       model.get_output_embeddings()
                  assert x is None
                  lowercase_       :Dict     =       model.get_bias()
                  assert name is None
   def 		UpperCamelCase  (     self		):
        # TODO JP: Make TransfoXL XLA compliant
        pass
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Dict     =       TFTransfoXLModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




   @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''		)
   def 		UpperCamelCase  (     self		):
        pass






@require_tf
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @unittest.skip('''Skip test until #12651 is resolved.'''		)
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        lowercase_       :List[str]     =       tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,							dtype=tf.intaa		)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        lowercase_       :List[Any]     =       [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        lowercase_       :Any     =       model.generate(UpperCamelCase_   ,							max_length=200   ,							do_sample=UpperCamelCase_		)
        self.assertListEqual(output_ids[0].numpy().tolist()   ,							UpperCamelCase_		)

