



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a_    :  Tuple						  =		logging.get_logger(__name__)

a_    :  Any						  =		'▁'

a_    :  int						  =		{'vocab_file': 'sentencepiece.bpe.model'}

a_    :  Union[str, Any]						  =		{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

a_    :  Union[str, Any]						  =		{
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
a_    :  List[Any]						  =		['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      VOCAB_FILES_NAMES
     _A      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A      =      PRETRAINED_VOCAB_FILES_MAP
     _A      =      ['input_ids', 'attention_mask']

     _A      =      []
     _A      =      []







     def __init__(self					,      __a					,      __a="<s>"					,      __a="</s>"					,      __a="</s>"					,      __a="<s>"					,      __a="<unk>"					,      __a="<pad>"					,      __a="<mask>"					,      __a=None					,      __a=None					,      __a=None					,      __a = None					,      __a=None					,      __a=False					,      **__a					,      ):






           '''simple docstring'''



           lowerCamelCase             =		AddedToken(__a					,      lstrip=__a					,      rstrip=__a     ) if isinstance(__a					,      __a     ) else mask_token

           lowerCamelCase             =		{} if sp_model_kwargs is None else sp_model_kwargs
           lowerCamelCase             =		legacy_behaviour

           super().__init__(
               bos_token=__a					,      eos_token=__a					,      unk_token=__a					,      sep_token=__a					,      cls_token=__a					,      pad_token=__a					,      mask_token=__a					,      tokenizer_file=__a					,      src_lang=__a					,      tgt_lang=__a					,      additional_special_tokens=__a					,      sp_model_kwargs=self.sp_model_kwargs					,      legacy_behaviour=__a					,      **__a					,      )

           lowerCamelCase             =		spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(str(__a     )     )
           lowerCamelCase             =		vocab_file
           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
           # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
           # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

           # Mimic fairseq token-to-id alignment for the first 4 token
           lowerCamelCase             =		{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           lowerCamelCase             =		1

           lowerCamelCase             =		len(self.sp_model     )
           lowerCamelCase             =		{
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a     )
           }
           lowerCamelCase             =		{v: k for k, v in self.lang_code_to_id.items()}
           lowerCamelCase             =		len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
           lowerCamelCase             =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           lowerCamelCase             =		list(self.lang_code_to_id.keys()     )

           if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 self._additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in self._additional_special_tokens]     )

           lowerCamelCase             =		src_lang if src_lang is not None else "eng_Latn"
           lowerCamelCase             =		self.lang_code_to_id[self._src_lang]
           lowerCamelCase             =		tgt_lang
           self.set_src_lang_special_tokens(self._src_lang     )







     def __getstate__(self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.__dict__.copy()
           lowerCamelCase             =		None
           lowerCamelCase             =		self.sp_model.serialized_model_proto()
           return state







     def __setstate__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		d

           # for backward compatibility
           if not hasattr(self					,      "sp_model_kwargs"     ):
                 lowerCamelCase             =		{}

           lowerCamelCase             =		spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self._src_lang







     @src_lang.setter
     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		new_src_lang
           self.set_src_lang_special_tokens(self._src_lang     )







     def     _a   (self					,      __a					,      __a = None					,      __a = False     ):






           '''simple docstring'''



           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__a					,      token_ids_a=__a					,      already_has_special_tokens=__a     )

           lowerCamelCase             =		[1] * len(self.prefix_tokens     )
           lowerCamelCase             =		[1] * len(self.suffix_tokens     )
           if token_ids_a is None:
                 return prefix_ones + ([0] * len(__a     )) + suffix_ones
           return prefix_ones + ([0] * len(__a     )) + ([0] * len(__a     )) + suffix_ones







     def     _a   (self					,      __a					,      __a = None     ):






           '''simple docstring'''



           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







     def     _a   (self					,      __a					,      __a = None     ):






           '''simple docstring'''



           lowerCamelCase             =		[self.sep_token_id]
           lowerCamelCase             =		[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep     ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      **__a     ):






           '''simple docstring'''



           if src_lang is None or tgt_lang is None:
                 raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
           lowerCamelCase             =		src_lang
           lowerCamelCase             =		self(__a					,      add_special_tokens=__a					,      return_tensors=__a					,      **__a     )
           lowerCamelCase             =		self.convert_tokens_to_ids(__a     )
           lowerCamelCase             =		tgt_lang_id
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		{self.convert_ids_to_tokens(__a     ): i for i in range(self.vocab_size     )}
           vocab.update(self.added_tokens_encoder     )
           return vocab







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           return self.sp_model.encode(__a					,      out_type=__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           lowerCamelCase             =		self.sp_model.PieceToId(__a     )

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		"".join(__a     ).replace(__a					,      " "     ).strip()
           return out_string







     def     _a   (self					,      __a					,      __a = None     ):






           '''simple docstring'''



           if not os.path.isdir(__a     ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
                 return
           lowerCamelCase             =		os.path.join(
               __a					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(__a     ) and os.path.isfile(self.vocab_file     ):
                 copyfile(self.vocab_file					,      __a     )
           elif not os.path.isfile(self.vocab_file     ):
                 with open(__a					,      "wb"     ) as fi:
                       lowerCamelCase             =		self.sp_model.serialized_model_proto()
                       fi.write(__a     )

           return (out_vocab_file,)







     def     _a   (self					,      __a					,      __a = "eng_Latn"					,      __a = None					,      __a = "fra_Latn"					,      **__a					,      ):






           '''simple docstring'''



           lowerCamelCase             =		src_lang
           lowerCamelCase             =		tgt_lang
           return super().prepare_seqaseq_batch(__a					,      __a					,      **__a     )







     def     _a   (self     ):






           '''simple docstring'''



           return self.set_src_lang_special_tokens(self.src_lang     )







     def     _a   (self     ):






           '''simple docstring'''



           return self.set_tgt_lang_special_tokens(self.tgt_lang     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.lang_code_to_id[src_lang]
           if self.legacy_behaviour:
                 lowerCamelCase             =		[]
                 lowerCamelCase             =		[self.eos_token_id, self.cur_lang_code]
           else:
                 lowerCamelCase             =		[self.cur_lang_code]
                 lowerCamelCase             =		[self.eos_token_id]







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.lang_code_to_id[lang]
           if self.legacy_behaviour:
                 lowerCamelCase             =		[]
                 lowerCamelCase             =		[self.eos_token_id, self.cur_lang_code]
           else:
                 lowerCamelCase             =		[self.cur_lang_code]
                 lowerCamelCase             =		[self.eos_token_id]




import unittest

from transformers import 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=7					,      __a=True					,      __a=True					,      __a=True					,      __a=99					,      __a=32					,      __a=5					,      __a=4					,      __a=37					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=5_12					,      __a=16					,      __a=2					,      __a=0.02					,      __a=3					,      __a=4					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		seq_length
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_token_type_ids
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		vocab_size
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		max_position_embeddings
           lowerCamelCase             =		type_vocab_size
           lowerCamelCase             =		type_sequence_label_size
           lowerCamelCase             =		initializer_range
           lowerCamelCase             =		num_labels
           lowerCamelCase             =		num_choices
           lowerCamelCase             =		scope
           lowerCamelCase             =		self.vocab_size - 1







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

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

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

           lowerCamelCase             =		OpenAIGPTConfig(
               vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=self.max_position_embeddings					,      pad_token_id=self.pad_token_id					,      )

           lowerCamelCase             =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,      2     )

           return (
               config,
               input_ids,
               head_mask,
               token_type_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModel(config=__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      head_mask=__a     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a     )
           lowerCamelCase             =		model(__a     )

           self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTDoubleHeadsModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.num_labels
           lowerCamelCase             =		OpenAIGPTForSequenceClassification(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				
           )             =		config_and_inputs
           lowerCamelCase             =		{
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "head_mask": head_mask,
           }

           return config, inputs_dict





@require_torch
class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      (
         (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
         if is_torch_available()
         else ()
     )
     _A      =      (
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
     _A      =      (
         {
             'feature-extraction': OpenAIGPTModel,
             'text-classification': OpenAIGPTForSequenceClassification,
             'text-generation': OpenAIGPTLMHeadModel,
             'zero-shot': OpenAIGPTForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                 # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                 # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                 # tiny config could not be created.
                 return True

           return False







     def     _a   (self					,      __a					,      __a					,      __a=False     ):






           '''simple docstring'''



           lowerCamelCase             =		super()._prepare_for_class(__a					,      __a					,      return_labels=__a     )

           if return_labels:
                 if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		torch.zeros(
                           self.model_tester.batch_size					,      dtype=torch.long					,      device=__a     )
           return inputs_dict







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModelTester(self     )
           lowerCamelCase             =		ConfigTester(self					,      config_class=__a					,      n_embd=37     )







     def     _a   (self     ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase             =		OpenAIGPTModel.from_pretrained(__a     )
                 self.assertIsNotNone(__a     )





@require_torch
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"     )
           model.to(__a     )
           lowerCamelCase             =		torch.tensor([[4_81, 47_35, 5_44]]					,      dtype=torch.long					,      device=__a     )  # the president is
           lowerCamelCase             =		[
               4_81,
               47_35,
               5_44,
               2_46,
               9_63,
               8_70,
               7_62,
               2_39,
               2_44,
               4_04_77,
               2_44,
               2_49,
               7_19,
               8_81,
               4_87,
               5_44,
               2_40,
               2_44,
               6_03,
               4_81,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           lowerCamelCase             =		model.generate(__a					,      do_sample=__a     )
           self.assertListEqual(output_ids[0].tolist()					,      __a     )