


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class 	UpperCAmelCase_					:

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=99						,				__A=13						,				__A=7						,				__A=9						,				__A=True						,				__A=True						,				__A=False						,				__A=32						,				__A=5						,				__A=4						,				__A=37						,				__A=8						,				__A=0.1						,				__A=0.002						,				__A=1						,				__A=0						,				__A=0						,				__A=None						,				__A=None						,				):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				parent
     lowerCamelCase :					Union[str, Any]    =				batch_size
     lowerCamelCase :					List[Any]    =				encoder_seq_length
     lowerCamelCase :					Dict    =				decoder_seq_length
     # For common tests
     lowerCamelCase :					str    =				self.decoder_seq_length
     lowerCamelCase :					Optional[Any]    =				is_training
     lowerCamelCase :					Optional[Any]    =				use_attention_mask
     lowerCamelCase :					str    =				use_labels
     lowerCamelCase :					Optional[int]    =				vocab_size
     lowerCamelCase :					Any    =				hidden_size
     lowerCamelCase :					Optional[int]    =				num_hidden_layers
     lowerCamelCase :					List[str]    =				num_attention_heads
     lowerCamelCase :					Any    =				d_ff
     lowerCamelCase :					int    =				relative_attention_num_buckets
     lowerCamelCase :					Any    =				dropout_rate
     lowerCamelCase :					Any    =				initializer_factor
     lowerCamelCase :					Union[str, Any]    =				eos_token_id
     lowerCamelCase :					Optional[int]    =				pad_token_id
     lowerCamelCase :					List[str]    =				decoder_start_token_id
     lowerCamelCase :					str    =				None
     lowerCamelCase :					Any    =				decoder_layers






   def _snake_case    (		self ):






     """simple docstring"""





     return TaConfig.from_pretrained("google/umt5-base" )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A=None						,				__A=None						,				__A=None						,				__A=None						,				__A=None						,				):






     """simple docstring"""





     if attention_mask is None:
       lowerCamelCase :					Union[str, Any]    =				input_ids.ne(config.pad_token_id )
     if decoder_attention_mask is None:
       lowerCamelCase :					Dict    =				decoder_input_ids.ne(config.pad_token_id )
     if head_mask is None:
       lowerCamelCase :					Tuple    =				torch.ones(config.num_hidden_layers						,				config.num_attention_heads						,				device=__A )
     if decoder_head_mask is None:
       lowerCamelCase :					int    =				torch.ones(config.num_decoder_layers						,				config.num_attention_heads						,				device=__A )
     if cross_attn_head_mask is None:
       lowerCamelCase :					int    =				torch.ones(
           config.num_decoder_layers						,				config.num_attention_heads						,				device=__A )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				ids_tensor([self.batch_size, self.encoder_seq_length]						,				self.vocab_size )
     lowerCamelCase :					int    =				ids_tensor([self.batch_size, self.decoder_seq_length]						,				self.vocab_size )

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for NllbMoe the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     lowerCamelCase :					Dict    =				input_ids.clamp(self.pad_token_id + 1 )
     lowerCamelCase :					Dict    =				decoder_input_ids.clamp(self.pad_token_id + 1 )

     lowerCamelCase :					List[Any]    =				self.get_config()
     lowerCamelCase :					Any    =				config.num_attention_heads
     lowerCamelCase :					Optional[int]    =				self.prepare_inputs_dict(__A						,				__A						,				__A )
     return config, input_dict






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				self.prepare_config_and_inputs()
     return config, inputs_dict






   def _snake_case    (		self ):






     """simple docstring"""





     return TaConfig(
         vocab_size=166						,				d_model=self.hidden_size						,				d_ff=self.d_ff						,				d_kv=self.hidden_size // self.num_attention_heads						,				num_layers=self.num_hidden_layers						,				num_decoder_layers=self.decoder_layers						,				num_heads=self.num_attention_heads						,				relative_attention_num_buckets=self.relative_attention_num_buckets						,				dropout_rate=self.dropout_rate						,				initializer_factor=self.initializer_factor						,				eos_token_id=self.eos_token_id						,				bos_token_id=self.pad_token_id						,				pad_token_id=self.pad_token_id						,				decoder_start_token_id=self.decoder_start_token_id						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     return TaConfig(
         vocab_size=self.vocab_size						,				d_model=self.hidden_size						,				d_ff=self.d_ff						,				d_kv=self.hidden_size // self.num_attention_heads						,				num_layers=self.num_hidden_layers						,				num_decoder_layers=self.decoder_layers						,				num_heads=self.num_attention_heads						,				relative_attention_num_buckets=self.relative_attention_num_buckets						,				dropout_rate=self.dropout_rate						,				initializer_factor=self.initializer_factor						,				eos_token_id=self.eos_token_id						,				bos_token_id=self.pad_token_id						,				pad_token_id=self.pad_token_id						,				decoder_start_token_id=self.decoder_start_token_id						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				UMTaModel(config=__A )
     model.to(__A )
     model.eval()
     lowerCamelCase :					List[str]    =				model(
         input_ids=__A						,				decoder_input_ids=__A						,				attention_mask=__A						,				decoder_attention_mask=__A						,				)
     lowerCamelCase :					List[Any]    =				model(input_ids=__A						,				decoder_input_ids=__A )
     lowerCamelCase :					List[Any]    =				result.last_hidden_state
     lowerCamelCase :					Tuple    =				result.past_key_values
     lowerCamelCase :					Any    =				result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()						,				(self.batch_size, self.encoder_seq_length, self.hidden_size) )
     self.parent.assertEqual(decoder_output.size()						,				(self.batch_size, self.decoder_seq_length, self.hidden_size) )
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(__A )						,				config.num_layers )
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0] )						,				4 )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				UMTaModel(config=__A ).get_decoder().to(__A ).eval()
     # first forward pass
     lowerCamelCase :					int    =				model(__A						,				use_cache=__A )
     lowerCamelCase :					Optional[int]    =				model(__A )
     lowerCamelCase :					Optional[Any]    =				model(__A						,				use_cache=__A )

     self.parent.assertTrue(len(__A ) == len(__A ) )
     self.parent.assertTrue(len(__A ) == len(__A ) + 1 )

     lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase :					List[str]    =				ids_tensor((self.batch_size, 1)						,				config.vocab_size )

     # append to next input_ids and
     lowerCamelCase :					Dict    =				torch.cat([input_ids, next_tokens]						,				dim=-1 )

     lowerCamelCase :					Optional[Any]    =				model(__A )["last_hidden_state"]
     lowerCamelCase :					List[str]    =				model(__A						,				past_key_values=__A )["last_hidden_state"]

     # select random slice
     lowerCamelCase :					Tuple    =				ids_tensor((1,)						,				output_from_past.shape[-1] ).item()
     lowerCamelCase :					List[str]    =				output_from_no_past[:, -1, random_slice_idx].detach()
     lowerCamelCase :					str    =				output_from_past[:, 0, random_slice_idx].detach()

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






   def _snake_case    (		self						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				UMTaModel(config=__A ).to(__A ).half().eval()
     lowerCamelCase :					Any    =				model(**__A )["last_hidden_state"]
     self.parent.assertFalse(torch.isnan(__A ).any().item() )



@require_torch
class 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase		, UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Any     		=		(
       (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
   )
   __A :      str     		=		(UMTaForConditionalGeneration,) if is_torch_available() else ()
   __A :      int     		=		(
       {
           "conversational": UMTaForConditionalGeneration,
           "feature-extraction": UMTaModel,
           "summarization": UMTaForConditionalGeneration,
           "text2text-generation": UMTaForConditionalGeneration,
           "translation": UMTaForConditionalGeneration,
           "question-answering": UMTaForQuestionAnswering,
       }
       if is_torch_available()
       else {}
   )
   __A :      List[str]     		=		True
   __A :      Optional[int]     		=		False
   __A :      Optional[Any]     		=		False
   __A :      List[Any]     		=		True
   __A :      Optional[Any]     		=		True
   # The small UMT5 model needs higher percentages for CPU/MP tests
   __A :      Union[str, Any]     		=		[0.8, 0.9]






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				UMTaModelTester(self )






   @unittest.skip("Test has a segmentation fault on torch 1.8.0" )
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.model_tester.prepare_config_and_inputs()
     lowerCamelCase :					Union[str, Any]    =				UMTaModel(config_and_inputs[0] ).to(__A )
     with tempfile.TemporaryDirectory() as tmpdirname:
       torch.onnx.export(
           __A						,				(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,				F"""{tmpdirname}/t5_test.onnx"""						,				export_params=__A						,				opset_version=9						,				input_names=["input_ids", "decoder_input_ids"]						,				)






   @unittest.skipIf(torch_device == "cpu"						,				"Cant do half precision" )
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				["encoder_attentions", "decoder_attentions", "cross_attentions"]
     lowerCamelCase :					List[Any]    =				self.model_tester.prepare_config_and_inputs()
     lowerCamelCase :					str    =				config_and_inputs[0]
     lowerCamelCase :					Optional[Any]    =				UMTaForConditionalGeneration(__A ).eval()
     model.to(__A )

     lowerCamelCase :					Optional[int]    =				{
         "head_mask": torch.zeros(config.num_layers						,				config.num_heads						,				device=__A ),
         "decoder_head_mask": torch.zeros(config.num_decoder_layers						,				config.num_heads						,				device=__A ),
         "cross_attn_head_mask": torch.zeros(config.num_decoder_layers						,				config.num_heads						,				device=__A ),
     }

     for attn_name, (name, mask) in zip(__A						,				head_masking.items() ):
       lowerCamelCase :					int    =				{name: mask}
       # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
       if name == "head_mask":
         lowerCamelCase :					Optional[int]    =				torch.ones(
             config.num_decoder_layers						,				config.num_heads						,				device=__A )

       lowerCamelCase :					List[str]    =				model.generate(
           config_and_inputs[1]["input_ids"]						,				num_beams=1						,				max_length=3						,				output_attentions=__A						,				return_dict_in_generate=__A						,				**__A						,				)
       # We check the state of decoder_attentions and cross_attentions just from the last step
       lowerCamelCase :					List[Any]    =				out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
       self.assertEqual(sum([w.sum().item() for w in attn_weights] )						,				0.0 )






   @unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
   def _snake_case    (		self ):






     """simple docstring"""





     pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   @slow
   @unittest.skip(
       "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				UMTaForConditionalGeneration.from_pretrained("google/umt5-small"						,				return_dict=__A ).to(__A )
     lowerCamelCase :					List[str]    =				AutoTokenizer.from_pretrained("google/umt5-small"						,				use_fast=__A						,				legacy=__A )
     lowerCamelCase :					Any    =				[
         "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
         "No se como puedo <extra_id_0>.",
         "This is the reason why we <extra_id_0> them.",
         "The <extra_id_0> walks in <extra_id_1>, seats",
         "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
     ]
     lowerCamelCase :					Optional[int]    =				tokenizer(__A						,				return_tensors="pt"						,				padding=__A ).input_ids
     # fmt: off
     lowerCamelCase :					List[str]    =				torch.tensor(
         [
             [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
         ] )
     # fmt: on
     torch.testing.assert_allclose(__A						,				__A )

     lowerCamelCase :					str    =				model.generate(input_ids.to(__A ) )
     lowerCamelCase :					Tuple    =				[
         "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
         "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
         "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
         "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
         "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
     ]
     lowerCamelCase :					Any    =				tokenizer.batch_decode(__A )
     self.assertEqual(__A						,				__A )







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 	UpperCAmelCase_					:

   '''simple docstring'''




   def __init__(		self						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				parent
     lowerCamelCase :					Union[str, Any]    =				13
     lowerCamelCase :					Optional[Any]    =				7
     lowerCamelCase :					List[str]    =				True
     lowerCamelCase :					Optional[int]    =				True
     lowerCamelCase :					Union[str, Any]    =				True
     lowerCamelCase :					List[Any]    =				True
     lowerCamelCase :					Tuple    =				True
     lowerCamelCase :					Any    =				False
     lowerCamelCase :					int    =				False
     lowerCamelCase :					Tuple    =				False
     lowerCamelCase :					Union[str, Any]    =				2
     lowerCamelCase :					Dict    =				99
     lowerCamelCase :					Tuple    =				0
     lowerCamelCase :					Any    =				32
     lowerCamelCase :					List[Any]    =				2
     lowerCamelCase :					Tuple    =				4
     lowerCamelCase :					List[str]    =				0.1
     lowerCamelCase :					int    =				0.1
     lowerCamelCase :					int    =				512
     lowerCamelCase :					List[Any]    =				16
     lowerCamelCase :					Any    =				2
     lowerCamelCase :					Any    =				0.02
     lowerCamelCase :					List[str]    =				3
     lowerCamelCase :					Tuple    =				4
     lowerCamelCase :					int    =				"last"
     lowerCamelCase :					int    =				True
     lowerCamelCase :					Dict    =				None
     lowerCamelCase :					Tuple    =				0






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size )
     lowerCamelCase :					Tuple    =				random_attention_mask([self.batch_size, self.seq_length]						,				dtype=tf.floataa )

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

     lowerCamelCase :					str    =				None
     if self.use_token_type_ids:
       lowerCamelCase :					Optional[int]    =				ids_tensor([self.batch_size, self.seq_length]						,				self.n_langs )

     lowerCamelCase :					Dict    =				None
     lowerCamelCase :					Dict    =				None
     lowerCamelCase :					Tuple    =				None
     if self.use_labels:
       lowerCamelCase :					str    =				ids_tensor([self.batch_size]						,				self.type_sequence_label_size )
       lowerCamelCase :					Union[str, Any]    =				ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels )
       lowerCamelCase :					int    =				ids_tensor([self.batch_size]						,				2						,				dtype=tf.floataa )
       lowerCamelCase :					Union[str, Any]    =				ids_tensor([self.batch_size]						,				self.num_choices )

     lowerCamelCase :					List[Any]    =				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 _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				TFFlaubertModel(config=__A )
     lowerCamelCase :					Any    =				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     lowerCamelCase :					Dict    =				model(__A )

     lowerCamelCase :					Any    =				[input_ids, input_mask]
     lowerCamelCase :					Tuple    =				model(__A )
     self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size) )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					int    =				TFFlaubertWithLMHeadModel(__A )

     lowerCamelCase :					List[str]    =				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     lowerCamelCase :					int    =				model(__A )

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






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				TFFlaubertForQuestionAnsweringSimple(__A )

     lowerCamelCase :					Optional[int]    =				{"input_ids": input_ids, "lengths": input_lengths}

     lowerCamelCase :					Union[str, Any]    =				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 _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				TFFlaubertForSequenceClassification(__A )

     lowerCamelCase :					str    =				{"input_ids": input_ids, "lengths": input_lengths}

     lowerCamelCase :					Union[str, Any]    =				model(__A )

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






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.num_labels
     lowerCamelCase :					Optional[Any]    =				TFFlaubertForTokenClassification(config=__A )
     lowerCamelCase :					int    =				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase :					Union[str, Any]    =				model(__A )
     self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels) )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.num_choices
     lowerCamelCase :					Optional[Any]    =				TFFlaubertForMultipleChoice(config=__A )
     lowerCamelCase :					Tuple    =				tf.tile(tf.expand_dims(__A						,				1 )						,				(1, self.num_choices, 1) )
     lowerCamelCase :					int    =				tf.tile(tf.expand_dims(__A						,				1 )						,				(1, self.num_choices, 1) )
     lowerCamelCase :					List[str]    =				tf.tile(tf.expand_dims(__A						,				1 )						,				(1, self.num_choices, 1) )
     lowerCamelCase :					Optional[int]    =				{
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     lowerCamelCase :					Union[str, Any]    =				model(__A )
     self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices) )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.prepare_config_and_inputs()
     (
         (
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	(
         lowerCamelCase
     )    ,	
     ) :					Optional[Any]    =				config_and_inputs
     lowerCamelCase :					List[Any]    =				{
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
         "langs": token_type_ids,
         "lengths": input_lengths,
     }
     return config, inputs_dict



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

   '''simple docstring'''




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






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A ):






     """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 _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				TFFlaubertModelTester(self )
     lowerCamelCase :					Optional[int]    =				ConfigTester(self						,				config_class=__A						,				emb_dim=37 )






   def _snake_case    (		self ):






     """simple docstring"""





     self.config_tester.run_common_tests()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_model(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_lm_head(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_qa(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_sequence_classif(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_for_token_classification(*__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_for_multiple_choice(*__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowerCamelCase :					int    =				TFFlaubertModel.from_pretrained(__A )
       self.assertIsNotNone(__A )



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

   '''simple docstring'''




   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased" )

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

     lowerCamelCase :					Dict    =				model(__A )[0]
     lowerCamelCase :					List[str]    =				tf.TensorShape((1, 8, 512) )
     self.assertEqual(output.shape						,				__A )
     # compare the actual values for a slice.
     lowerCamelCase :					Tuple    =				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 ) )



