
'''simple docstring'''


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__lowercase							:   Optional[Any]					=     logging.get_logger(__name__)

__lowercase							:   List[Any]					=     {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__lowercase							:   str					=     {
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

__lowercase							:   Tuple					=     {
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					with open(__UpperCAmelCase			,       '''r'''			,       encoding='''utf-8'''				) as f:
										lowerCamelCase_					:      Tuple     =		json.loads(f.read()				)

					lowerCamelCase_					:      Optional[Any]     =		collections.OrderedDict()
					lowerCamelCase_					:      Union[str, Any]     =		collections.OrderedDict()
					lowerCamelCase_					:      List[str]     =		collections.OrderedDict()
					with open(__UpperCAmelCase			,       '''r'''			,       encoding='''utf-8'''				) as f:
										lowerCamelCase_					:      Optional[int]     =		f.readlines()
					lowerCamelCase_					:      int     =		[[t.rstrip('''\n'''				)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''				).split(''','''				) for t in token]
					for idx, b in enumerate(__UpperCAmelCase				):
										lowerCamelCase_					:      Optional[int]     =		b
										lowerCamelCase_					:      Any     =		idx
										for wd in b:
															lowerCamelCase_					:      Optional[int]     =		idx

					return vocab, raw_vocab, ids_to_tokens, emoji

class 	__lowercase				(  _snake_case    ):
			lowerCamelCase			:		Tuple	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		Optional[int]	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		List[Any]	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Optional[Any]	      =			['input_ids', 'attention_mask']

			def __init__(self			,   A			,   A			,   A="<|endoftext|>"			,   A="<|endoftext|>"			,   A="<|startoftext|>"			,   A="<|endoftext|>"			,   A=False			,   **A			,   )     ->   Union[str, Any]:
								super().__init__(
								    unk_token=A			,   pad_token=A			,   bos_token=A			,   eos_token=A			,   do_clean_text=A			,   **A			,   )
								if not os.path.isfile(A      ):
													raise ValueError(
													    F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
													    ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''      )
								if not os.path.isfile(A      ):
													raise ValueError(
													    F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
													    ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''      )
								lowerCamelCase_					:      Tuple     =		do_clean_text
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Dict     =		load_vocab_and_emoji(A			,   A      )
								lowerCamelCase_					:      Dict     =		SubWordJapaneseTokenizer(
								    vocab=self.vocab			,   ids_to_tokens=self.ids_to_tokens			,   emoji=self.emoji      )

			@property
			def        UpperCAmelCase__ (self      )     ->   List[Any]:
								return len(self.raw_vocab      )

			def        UpperCAmelCase__ (self      )     ->   Dict:
								return dict(self.raw_vocab			,   **self.added_tokens_encoder      )

			def        UpperCAmelCase__ (self			,   A      )     ->   str:
								return self.subword_tokenizer.tokenize(A			,   clean=self.do_clean_text      )

			def        UpperCAmelCase__ (self			,   A      )     ->   List[str]:
								return self.vocab.get(A			,   self.vocab.get(self.unk_token      )      )

			def        UpperCAmelCase__ (self			,   A      )     ->   List[Any]:
								return self.subword_tokenizer.convert_id_to_token(A      )

			def        UpperCAmelCase__ (self			,   A      )     ->   List[Any]:
								lowerCamelCase_					:      Any     =		''''''.join(A      ).strip()
								return out_string

			def        UpperCAmelCase__ (self			,   A      )     ->   Any:
								lowerCamelCase_					:      Tuple     =		[]
								for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(A			,   add_special_tokens=A      ) + [self.eos_token_id]      )

								if len(A      ) > self.model_max_length:
													lowerCamelCase_					:      Optional[Any]     =		input_ids[-self.model_max_length :]
								return input_ids






			def        UpperCAmelCase__ (self			,   A			,   A = None      )     ->   List[Any]:
								lowerCamelCase_					:      List[str]     =		0
								if os.path.isdir(A      ):
													lowerCamelCase_					:      str     =		os.path.join(
													    A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
													lowerCamelCase_					:      Optional[Any]     =		os.path.join(
													    A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']      )
								else:
													lowerCamelCase_					:      Any     =		(
													    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
													)
													lowerCamelCase_					:      Optional[Any]     =		(
													    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
													)
								with open(A			,   '''w'''			,   encoding='''utf-8'''      ) as writer:
													for token_index, token in self.ids_to_tokens.items():
																		if index != token_index:
																							logger.warning(
																							    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																							    ''' Please check that the vocabulary is not corrupted!'''      )
																							lowerCamelCase_					:      Dict     =		token_index
																		writer.write(''','''.join(A      ) + '''\n'''      )
																		index += 1
								with open(A			,   '''w'''			,   encoding='''utf-8'''      ) as writer:
													json.dump(self.emoji			,   A      )
								return vocab_file, emoji_file

class 	__lowercase				(  _snake_case    ):

			def __init__(self			,   A			,   A			,   A      )     ->   Optional[Any]:
								lowerCamelCase_					:      Union[str, Any]     =		vocab  # same as swe
								lowerCamelCase_					:      Optional[Any]     =		ids_to_tokens  # same as bpe
								lowerCamelCase_					:      Optional[Any]     =		emoji
								lowerCamelCase_					:      List[str]     =		np.max([len(A      ) for w in self.vocab.keys()]      )
								lowerCamelCase_					:      Optional[int]     =		re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'''      )
								lowerCamelCase_					:      int     =		re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'''      )
								lowerCamelCase_					:      Union[str, Any]     =		re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'''      )
								lowerCamelCase_					:      str     =		re.compile(
								    R'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''      )
								lowerCamelCase_					:      Optional[int]     =		re.compile(
								    R'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''      )
								lowerCamelCase_					:      str     =		re.compile(
								    R'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'''      )
								lowerCamelCase_					:      Union[str, Any]     =		'''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
								lowerCamelCase_					:      Optional[Any]     =		'''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
								lowerCamelCase_					:      Dict     =		str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks}      )

			def __len__(self      )     ->   Any:
								return len(self.ids_to_tokens      )

			def        UpperCAmelCase__ (self			,   A      )     ->   List[str]:
								lowerCamelCase_					:      Optional[Any]     =		self.content_repattera.sub('''<URL>'''			,   A      )
								lowerCamelCase_					:      str     =		self.content_repattera.sub('''<EMAIL>'''			,   A      )
								lowerCamelCase_					:      Optional[Any]     =		self.content_repattera.sub('''<TEL>'''			,   A      )
								lowerCamelCase_					:      Optional[Any]     =		self.content_repattera.sub('''<DATE>'''			,   A      )
								lowerCamelCase_					:      Dict     =		self.content_repattera.sub('''<DATE>'''			,   A      )
								lowerCamelCase_					:      Union[str, Any]     =		self.content_repattera.sub('''<PRICE>'''			,   A      )
								lowerCamelCase_					:      List[str]     =		content.translate(self.content_transa      )
								while "<BLOCK><BLOCK>" in content:
													lowerCamelCase_					:      List[str]     =		content.replace('''<BLOCK><BLOCK>'''			,   '''<BLOCK>'''      )
								return content

			def        UpperCAmelCase__ (self			,   A			,   A=False      )     ->   Optional[int]:
								lowerCamelCase_					:      Union[str, Any]     =		text.replace(''' '''			,   '''<SP>'''      )
								lowerCamelCase_					:      List[Any]     =		text.replace('''　'''			,   '''<SP>'''      )
								lowerCamelCase_					:      str     =		text.replace('''\r\n'''			,   '''<BR>'''      )
								lowerCamelCase_					:      Optional[Any]     =		text.replace('''\n'''			,   '''<BR>'''      )
								lowerCamelCase_					:      Union[str, Any]     =		text.replace('''\r'''			,   '''<BR>'''      )
								lowerCamelCase_					:      int     =		text.replace('''\t'''			,   '''<TAB>'''      )
								lowerCamelCase_					:      Any     =		text.replace('''—'''			,   '''ー'''      )
								lowerCamelCase_					:      Any     =		text.replace('''−'''			,   '''ー'''      )
								for k, v in self.emoji["emoji"].items():
													if k in text:
																		lowerCamelCase_					:      Union[str, Any]     =		text.replace(A			,   A      )
								if clean:
													lowerCamelCase_					:      List[Any]     =		self.clean_text(A      )

								def check_simbol(A      ):
													lowerCamelCase_					:      Tuple     =		x.encode()
													if len(A      ) == 1 and len(A      ) == 2:
																		lowerCamelCase_					:      List[str]     =		(int(e[0]      ) << 8) + int(e[1]      )
																		if (
																		    (c >= 0xc2a1 and c <= 0xc2bf)
																		    or (c >= 0xc780 and c <= 0xc783)
																		    or (c >= 0xcab9 and c <= 0xcbbf)
																		    or (c >= 0xcc80 and c <= 0xcda2)
																		):
																							return True
													return False

								def checkuae(A      ):
													lowerCamelCase_					:      List[Any]     =		x.encode()
													if len(A      ) == 1 and len(A      ) == 3:
																		lowerCamelCase_					:      Tuple     =		(int(e[0]      ) << 1_6) + (int(e[1]      ) << 8) + int(e[2]      )
																		if c >= 0xe2_8080 and c <= 0xe2_b07f:
																							return True
													return False

								lowerCamelCase_					:      Optional[int]     =		0
								lowerCamelCase_					:      Optional[Any]     =		[]
								while pos < len(A      ):
													lowerCamelCase_					:      Dict     =		min(len(A      )			,   pos + self.maxlen + 1      ) if text[pos] == '''<''' else pos + 3
													lowerCamelCase_					:      List[Any]     =		[]  # (token_id, token, pos)
													for e in range(A			,   A			,   -1      ):
																		lowerCamelCase_					:      Union[str, Any]     =		text[pos:e]
																		if wd in self.vocab:
																							if wd[0] == "<" and len(A      ) > 2:
																												lowerCamelCase_					:      List[Any]     =		[(self.vocab[wd], wd, e)]
																												break
																							else:
																												candidates.append((self.vocab[wd], wd, e)      )
													if len(A      ) > 0:
																		# the smallest token_id is adopted
																		lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		sorted(A			,   key=lambda A      : x[0]      )[0]
																		result.append(A      )
																		lowerCamelCase_					:      Union[str, Any]     =		e
													else:
																		lowerCamelCase_					:      Tuple     =		pos + 1
																		lowerCamelCase_					:      Optional[int]     =		text[pos:end]
																		if check_simbol(A      ):
																							result.append('''<KIGOU>'''      )
																		elif checkuae(A      ):
																							result.append('''<U2000U2BFF>'''      )
																		else:
																							for i in wd.encode('''utf-8'''      ):
																												result.append('''<|byte%d|>''' % i      )
																		lowerCamelCase_					:      List[Any]     =		end
								return result






			def        UpperCAmelCase__ (self			,   A			,   A="\n"      )     ->   str:
								lowerCamelCase_					:      Union[str, Any]     =		[]
								lowerCamelCase_					:      Tuple     =		[]
								lowerCamelCase_					:      str     =		self.ids_to_tokens[index][0]
								if word[:6] == "<|byte" and word[-2:] == "|>":
													byte_tokens.append(int(word[6:-2]      )      )
								else:
													if len(A      ) > 0:
																		words.append(bytearray(A      ).decode('''utf-8'''			,   errors='''replace'''      )      )
																		lowerCamelCase_					:      List[Any]     =		[]
													if word[:7] == "<|emoji" and word[-2:] == "|>":
																		words.append(self.emoji['''emoji_inv'''][word]      )
													elif word == "<SP>":
																		words.append(''' '''      )
													elif word == "<BR>":
																		words.append(A      )
													elif word == "<TAB>":
																		words.append('''\t'''      )
													elif word == "<BLOCK>":
																		words.append('''▀'''      )
													elif word == "<KIGOU>":
																		words.append('''ǀ'''      )
													elif word == "<U2000U2BFF>":
																		words.append('''‖'''      )
													else:
																		words.append(A      )
								if len(A      ) > 0:
													words.append(bytearray(A      ).decode('''utf-8'''			,   errors='''replace'''      )      )
								lowerCamelCase_					:      str     =		''''''.join(A      )
								return text




'''simple docstring'''


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 	__lowercase				:

			def __init__(self			,   A			,   ):
								lowerCamelCase_					:      int     =		parent
								lowerCamelCase_					:      Dict     =		1_3
								lowerCamelCase_					:      Any     =		7
								lowerCamelCase_					:      Dict     =		3_0
								lowerCamelCase_					:      Optional[Any]     =		self.seq_length + self.mem_len
								lowerCamelCase_					:      Tuple     =		1_5
								lowerCamelCase_					:      Tuple     =		True
								lowerCamelCase_					:      List[Any]     =		True
								lowerCamelCase_					:      Dict     =		9_9
								lowerCamelCase_					:      Any     =		[1_0, 5_0, 8_0]
								lowerCamelCase_					:      List[str]     =		3_2
								lowerCamelCase_					:      Tuple     =		3_2
								lowerCamelCase_					:      Optional[Any]     =		4
								lowerCamelCase_					:      Union[str, Any]     =		8
								lowerCamelCase_					:      Dict     =		1_2_8
								lowerCamelCase_					:      Dict     =		2
								lowerCamelCase_					:      Tuple     =		2
								lowerCamelCase_					:      Any     =		None
								lowerCamelCase_					:      Optional[Any]     =		1
								lowerCamelCase_					:      Union[str, Any]     =		0
								lowerCamelCase_					:      str     =		3
								lowerCamelCase_					:      Dict     =		self.vocab_size - 1
								lowerCamelCase_					:      Dict     =		0.01

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )
								lowerCamelCase_					:      Optional[int]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

								lowerCamelCase_					:      Union[str, Any]     =		None
								if self.use_labels:
													lowerCamelCase_					:      Tuple     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

								lowerCamelCase_					:      str     =		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			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      int     =		TFTransfoXLModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

								lowerCamelCase_,      lowerCamelCase_					:      Any     =		model(A      ).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			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      List[Any]     =		TFTransfoXLLMHeadModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Optional[int]     =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		model(A      ).to_tuple()

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

								lowerCamelCase_					:      List[str]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

								lowerCamelCase_,      lowerCamelCase_					:      str     =		model(A      ).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			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		TFTransfoXLForSequenceClassification(A      )
								lowerCamelCase_					:      Optional[int]     =		model(A      )
								self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.prepare_config_and_inputs()
								((lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_))					:      List[Any]     =		config_and_inputs
								lowerCamelCase_					:      Tuple     =		{'''input_ids''': input_ids_a}
								return config, inputs_dict

@require_tf
class 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Any	      =			(
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			lowerCamelCase			:		Tuple	      =			() if is_tf_available() else ()
			lowerCamelCase			:		Any	      =			(
			    {
			        "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
			lowerCamelCase			:		str	      =			False
			lowerCamelCase			:		Optional[int]	      =			False
			lowerCamelCase			:		Dict	      =			False
			lowerCamelCase			:		Optional[int]	      =			False

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A			,   A      ):
								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      ):
								lowerCamelCase_					:      Optional[Any]     =		TFTransfoXLModelTester(self      )
								lowerCamelCase_					:      Optional[Any]     =		ConfigTester(self			,   config_class=A			,   d_embed=3_7      )

			def        UpperCAmelCase__ (self      ):
								self.config_tester.run_common_tests()

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      List[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*A      )

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      Optional[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase_					:      List[Any]     =		[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      Optional[Any]     =		model_class(A      )
													assert isinstance(model.get_input_embeddings()			,   tf.keras.layers.Layer      )
													if model_class in list_other_models_with_output_ebd:
																		lowerCamelCase_					:      str     =		model.get_output_embeddings()
																		assert isinstance(A			,   tf.keras.layers.Layer      )
																		lowerCamelCase_					:      int     =		model.get_bias()
																		assert name is None
													else:
																		lowerCamelCase_					:      Tuple     =		model.get_output_embeddings()
																		assert x is None
																		lowerCamelCase_					:      int     =		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]:
													lowerCamelCase_					:      Optional[Any]     =		TFTransfoXLModel.from_pretrained(A      )
													self.assertIsNotNone(A      )






			@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 	__lowercase				(  unittest.TestCase    ):

			@unittest.skip('''Skip test until #12651 is resolved.'''      )
			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
								# fmt: off
								lowerCamelCase_					:      Optional[Any]     =		tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]			,   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
								lowerCamelCase_					:      Union[str, Any]     =		[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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>

								lowerCamelCase_					:      Optional[Any]     =		model.generate(A			,   max_length=2_0_0			,   do_sample=A      )
								self.assertListEqual(output_ids[0].numpy().tolist()			,   A      )


