
"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						List[str]					       =   CodeGenTokenizer
				__UpperCamelCase      :						int					       =   CodeGenTokenizerFast
				__UpperCamelCase      :						List[str]					       =   True
				__UpperCamelCase      :						Optional[Any]					       =   {'''add_prefix_space''': True}
				__UpperCamelCase      :						str					       =   False



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											    """<|endoftext|>""",
											]
											SCREAMING_SNAKE_CASE__						:       Any           =   dict(zip(SCREAMING_SNAKE_CASE__   ,       range(len(SCREAMING_SNAKE_CASE__    )    )    )    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
											SCREAMING_SNAKE_CASE__						:       Any           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""merges_file"""]    )
											with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		fp.write(json.dumps(SCREAMING_SNAKE_CASE__    ) + """\n"""    )
											with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		fp.write("""\n""".join(SCREAMING_SNAKE_CASE__    )    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return CodeGenTokenizerFast.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """lower newer"""
											return input_text, output_text



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   CodeGenTokenizer(self.vocab_file   ,       self.merges_file   ,       **self.special_tokens_map    )
											SCREAMING_SNAKE_CASE__						:       int           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Dict           =   ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokens + [tokenizer.unk_token]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											if not self.test_rust_tokenizer:
																		return

											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   """lower newer"""

											# Testing tokenization
											SCREAMING_SNAKE_CASE__						:       List[str]           =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing conversion to ids without special tokens
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing conversion to ids with special tokens
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing the unknown token
											SCREAMING_SNAKE_CASE__						:       str           =   tokens + [rust_tokenizer.unk_token]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=15    )					->		List[str]:



											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )

																									# Simple input
																									SCREAMING_SNAKE_CASE__						:       Any           =   """This is a simple input"""
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   ["""This is a simple input 1""", """This is a simple input 2"""]
																									SCREAMING_SNAKE_CASE__						:       Dict           =   ("""This is a simple input""", """This is a pair""")
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
																									    ("""This is a simple input 1""", """This is a simple input 2"""),
																									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																									]

																									# Simple input tests
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Simple input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Simple input
																									self.assertRaises(
																									    SCREAMING_SNAKE_CASE__   ,       tokenizer_r.batch_encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       )

																									# Pair input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Pair input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Pair input
																									self.assertRaises(
																									    SCREAMING_SNAKE_CASE__   ,       tokenizer_r.batch_encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       pad_token="""<pad>"""    )

											# Simple input
											SCREAMING_SNAKE_CASE__						:       str           =   """This is a simple input"""
											SCREAMING_SNAKE_CASE__						:       int           =   ["""This is a simple input looooooooong""", """This is a simple input"""]
											SCREAMING_SNAKE_CASE__						:       str           =   ("""This is a simple input""", """This is a pair""")
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [
											    ("""This is a simple input loooooong""", """This is a simple input"""),
											    ("""This is a simple pair loooooong""", """This is a simple pair"""),
											]

											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.pad_token_id

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=30   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncate=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer(*SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=60   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncate=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )

											# s
											# test single string max_length padding
											self.assertEqual(out_s["""input_ids"""].shape[-1]   ,       30    )
											self.assertTrue(pad_token_id in out_s["""input_ids"""]    )
											self.assertTrue(0 in out_s["""attention_mask"""]    )

											# s2
											# test automatic padding
											self.assertEqual(out_sa["""input_ids"""].shape[-1]   ,       33    )
											# long slice doesn't have padding
											self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]    )
											self.assertFalse(0 in out_sa["""attention_mask"""][0]    )
											# short slice does have padding
											self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]    )
											self.assertTrue(0 in out_sa["""attention_mask"""][1]    )

											# p
											# test single pair max_length padding
											self.assertEqual(out_p["""input_ids"""].shape[-1]   ,       60    )
											self.assertTrue(pad_token_id in out_p["""input_ids"""]    )
											self.assertTrue(0 in out_p["""attention_mask"""]    )

											# p2
											# test automatic padding pair
											self.assertEqual(out_pa["""input_ids"""].shape[-1]   ,       52    )
											# long slice pair doesn't have padding
											self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]    )
											self.assertFalse(0 in out_pa["""attention_mask"""][0]    )
											# short slice pair does have padding
											self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]    )
											self.assertTrue(0 in out_pa["""attention_mask"""][1]    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   """$$$"""
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       bos_token=SCREAMING_SNAKE_CASE__   ,       add_bos_token=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """This is a simple input"""
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ["""This is a simple input 1""", """This is a simple input 2"""]

											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.bos_token_id

											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__    )

											self.assertEqual(out_s.input_ids[0]   ,       SCREAMING_SNAKE_CASE__    )
											self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer.decode(out_s.input_ids    )
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.batch_decode(out_sa.input_ids    )

											self.assertEqual(decode_s.split()[0]   ,       SCREAMING_SNAKE_CASE__    )
											self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )



				@slow
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""    )

											SCREAMING_SNAKE_CASE__						:       str           =   """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
											SCREAMING_SNAKE_CASE__						:       int           =   """\nif len_a > len_b:      result = a\nelse:      result = b"""

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer.encode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ["""^#""", re.escape("""<|endoftext|>"""    ), """^'''""", """^\"\"\"""", """\n\n\n"""]
											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer.decode(SCREAMING_SNAKE_CASE__   ,       truncate_before_pattern=SCREAMING_SNAKE_CASE__    )
											self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
 import numpy as np
 import tensorflow as tf

 from transformers import (
     TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     FlaubertConfig,
     TFFlaubertForMultipleChoice,
     TFFlaubertForQuestionAnsweringSimple,
     TFFlaubertForSequenceClassification,
     TFFlaubertForTokenClassification,
     TFFlaubertModel,
     TFFlaubertWithLMHeadModel,
 )







class   A:



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  Optional[Any]						,							)   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    13
       lowerCamelCase_		     =    7
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    99
       lowerCamelCase_		     =    0
       lowerCamelCase_		     =    32
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    4
       lowerCamelCase_		     =    0.1
       lowerCamelCase_		     =    0.1
       lowerCamelCase_		     =    512
       lowerCamelCase_		     =    16
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    0.02
       lowerCamelCase_		     =    3
       lowerCamelCase_		     =    4
       lowerCamelCase_		     =    'last'
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    0

 def 				a__ (				self					:  Dict  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )
       lowerCamelCase_		     =    random_attention_mask([self.batch_size, self.seq_length]						,							dtype=tf.floataa  )

       lowerCamelCase_		     =    None
       if self.use_input_lengths:
             lowerCamelCase_		     =    (
                 ids_tensor([self.batch_size]						,							vocab_size=2  ) + self.seq_length - 2
             )  # small variation of seq_length

       lowerCamelCase_		     =    None
       if self.use_token_type_ids:
             lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.n_langs  )

       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]						,							2						,							dtype=tf.floataa  )
             lowerCamelCase_		     =    ids_tensor([self.batch_size]						,							self.num_choices  )

       lowerCamelCase_		     =    FlaubertConfig(
           vocab_size=self.vocab_size						,							n_special=self.n_special						,							emb_dim=self.hidden_size						,							n_layers=self.num_hidden_layers						,							n_heads=self.num_attention_heads						,							dropout=self.hidden_dropout_prob						,							attention_dropout=self.attention_probs_dropout_prob						,							gelu_activation=self.gelu_activation						,							sinusoidal_embeddings=self.sinusoidal_embeddings						,							asm=self.asm						,							causal=self.causal						,							n_langs=self.n_langs						,							max_position_embeddings=self.max_position_embeddings						,							initializer_range=self.initializer_range						,							summary_type=self.summary_type						,							use_proj=self.use_proj						,							bos_token_id=self.bos_token_id						,							)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )

 def 				a__ (				self					:  int						,							A_					:  List[str]						,							A_					:  List[Any]						,							A_					:  str						,							A_					:  List[Any]						,							A_					:  int						,							A_					:  Tuple						,							A_					:  Optional[int]						,							A_					:  Optional[int]						,							A_					:  str						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModel(config=A_  )
       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       lowerCamelCase_		     =    model(A_  )

       lowerCamelCase_		     =    [input_ids, input_mask]
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)  )

 def 				a__ (				self					:  Tuple						,							A_					:  List[str]						,							A_					:  int						,							A_					:  List[Any]						,							A_					:  Any						,							A_					:  Any						,							A_					:  Dict						,							A_					:  str						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertWithLMHeadModel(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)  )

 def 				a__ (				self					:  str						,							A_					:  Tuple						,							A_					:  Any						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Dict						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							A_					:  Optional[int]						,							A_					:  List[Any]						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertForQuestionAnsweringSimple(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths}

       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.start_logits.shape						,							(self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape						,							(self.batch_size, self.seq_length)  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  List[Any]						,							A_					:  str						,							A_					:  List[str]						,							A_					:  Dict						,							A_					:  Optional[Any]						,							A_					:  Tuple						,							A_					:  str						,							A_					:  Optional[int]						,							A_					:  Tuple						,							)   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertForSequenceClassification(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths}

       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size)  )

 def 				a__ (				self					:  Dict						,							A_					:  Optional[Any]						,							A_					:  List[Any]						,							A_					:  int						,							A_					:  Any						,							A_					:  Union[str, Any]						,							A_					:  str						,							A_					:  Any						,							A_					:  Union[str, Any]						,							A_					:  List[str]						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.num_labels
       lowerCamelCase_		     =    TFFlaubertForTokenClassification(config=A_  )
       lowerCamelCase_		     =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels)  )

 def 				a__ (				self					:  List[Any]						,							A_					:  Optional[int]						,							A_					:  List[Any]						,							A_					:  Optional[int]						,							A_					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  int						,							A_					:  str						,							A_					:  Tuple						,							A_					:  str						,							)   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.num_choices
       lowerCamelCase_		     =    TFFlaubertForMultipleChoice(config=A_  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_choices)  )





 def 				a__ (				self					:  Union[str, Any]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				
       )		     =    config_and_inputs
       lowerCamelCase_		     =    {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict







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



 '''simple docstring'''


 UpperCamelCase						   =						(
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCamelCase						   =						(
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 UpperCamelCase						   =						(
     {
         '''feature-extraction''': TFFlaubertModel,
         '''fill-mask''': TFFlaubertWithLMHeadModel,
         '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
         '''text-classification''': TFFlaubertForSequenceClassification,
         '''token-classification''': TFFlaubertForTokenClassification,
         '''zero-shot''': TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						   =						False
 UpperCamelCase						   =						False

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							A_					:  str						,							A_					:  List[str]  )   ->	Optional[Any]:

       """simple docstring"""






       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'  )
       ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

       return False

 def 				a__ (				self					:  List[str]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModelTester(self  )
       lowerCamelCase_		     =    ConfigTester(self						,							config_class=A_						,							emb_dim=37  )

 def 				a__ (				self					:  List[str]  )   ->	str:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def 				a__ (				self					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*A_  )

 def 				a__ (				self					:  List[str]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*A_  )

 def 				a__ (				self					:  Dict  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*A_  )

 def 				a__ (				self					:  Union[str, Any]  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*A_  )

 def 				a__ (				self					:  List[Any]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_token_classification(*A_  )

 def 				a__ (				self					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_  )





 @slow
 def 				a__ (				self					:  Union[str, Any]  )   ->	Optional[int]:

       """simple docstring"""






       for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_		     =    TFFlaubertModel.from_pretrained(A_  )
             self.assertIsNotNone(A_  )







@require_tf
@require_sentencepiece
@require_tokenizers
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @slow
 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'  )

       lowerCamelCase_		     =    tf.convert_to_tensor(
           [[0, 158, 735, 2592, 1424, 6727, 82, 1]]						,							dtype=tf.intaa						,							)  # "J'aime flaubert !"

       lowerCamelCase_		     =    model(A_  )[0]
       lowerCamelCase_		     =    tf.TensorShape((1, 8, 512)  )
       self.assertEqual(output.shape						,							A_  )
       # compare the actual values for a slice.
       lowerCamelCase_		     =    tf.convert_to_tensor(
           [
               [
                   [-1.8768773, -1.566555, 0.27072418],
                   [-1.6920038, -0.5873505, 1.9329599],
                   [-2.9563985, -1.6993835, 1.7972052],
               ]
           ]						,							dtype=tf.floataa						,							)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()						,							expected_slice.numpy()						,							atol=1E-4  )  )



