

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				:







	def __init__(   self: Union[str, Any]  , __UpperCamelCase: List[str]  , __UpperCamelCase: Union[str, Any]=99  , __UpperCamelCase: Dict=13  , __UpperCamelCase: int=7  , __UpperCamelCase: Any=9  , __UpperCamelCase: Dict=True  , __UpperCamelCase: int=True  , __UpperCamelCase: Tuple=False  , __UpperCamelCase: Tuple=32  , __UpperCamelCase: Dict=5  , __UpperCamelCase: Optional[int]=4  , __UpperCamelCase: List[Any]=37  , __UpperCamelCase: Union[str, Any]=8  , __UpperCamelCase: Optional[Any]=0.1  , __UpperCamelCase: str=0.002  , __UpperCamelCase: str=1  , __UpperCamelCase: Any=0  , __UpperCamelCase: Union[str, Any]=0  , __UpperCamelCase: Dict=None  , __UpperCamelCase: Optional[Any]=None  , ):

						'''simple docstring'''





						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    encoder_seq_length
						__magic_name__							=    decoder_seq_length
						# For common tests
						__magic_name__							=    self.decoder_seq_length
						__magic_name__							=    is_training
						__magic_name__							=    use_attention_mask
						__magic_name__							=    use_labels
						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    d_ff
						__magic_name__							=    relative_attention_num_buckets
						__magic_name__							=    dropout_rate
						__magic_name__							=    initializer_factor
						__magic_name__							=    eos_token_id
						__magic_name__							=    pad_token_id
						__magic_name__							=    decoder_start_token_id
						__magic_name__							=    None
						__magic_name__							=    decoder_layers







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Any  , __UpperCamelCase: Any  , __UpperCamelCase: Union[str, Any]=None  , __UpperCamelCase: Dict=None  , __UpperCamelCase: Union[str, Any]=None  , __UpperCamelCase: Optional[int]=None  , __UpperCamelCase: List[Any]=None  , ):

						'''simple docstring'''





						if attention_mask is None:
											__magic_name__							=    input_ids.ne(config.pad_token_id      )
						if decoder_attention_mask is None:
											__magic_name__							=    decoder_input_ids.ne(config.pad_token_id      )
						if head_mask is None:
											__magic_name__							=    torch.ones(config.num_hidden_layers  , config.num_attention_heads  , device=UpperCAmelCase__      )
						if decoder_head_mask is None:
											__magic_name__							=    torch.ones(config.num_decoder_layers  , config.num_attention_heads  , device=UpperCAmelCase__      )
						if cross_attn_head_mask is None:
											__magic_name__							=    torch.ones(
											    config.num_decoder_layers  , config.num_attention_heads  , device=UpperCAmelCase__      )
						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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    ids_tensor([self.batch_size, self.encoder_seq_length]  , self.vocab_size      )
						__magic_name__							=    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
						__magic_name__							=    input_ids.clamp(self.pad_token_id + 1      )
						__magic_name__							=    decoder_input_ids.clamp(self.pad_token_id + 1      )

						__magic_name__							=    self.get_config()
						__magic_name__							=    config.num_attention_heads
						__magic_name__							=    self.prepare_inputs_dict(UpperCAmelCase__  , UpperCAmelCase__  , UpperCAmelCase__      )
						return config, input_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.prepare_config_and_inputs()
						return config, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return TaConfig(
						    vocab_size=1_66  , 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 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: Dict  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Dict  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Tuple  , ):

						'''simple docstring'''





						__magic_name__							=    UMTaModel(config=UpperCAmelCase__      )
						model.to(UpperCAmelCase__      )
						model.eval()
						__magic_name__							=    model(
						    input_ids=UpperCAmelCase__  , decoder_input_ids=UpperCAmelCase__  , attention_mask=UpperCAmelCase__  , decoder_attention_mask=UpperCAmelCase__  , )
						__magic_name__							=    model(input_ids=UpperCAmelCase__  , decoder_input_ids=UpperCAmelCase__      )
						__magic_name__							=    result.last_hidden_state
						__magic_name__							=    result.past_key_values
						__magic_name__							=    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(UpperCAmelCase__      )  , 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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: str  , __UpperCamelCase: Any  , __UpperCamelCase: int  , __UpperCamelCase: Tuple  , __UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						__magic_name__							=    UMTaModel(config=UpperCAmelCase__      ).get_decoder().to(UpperCAmelCase__      ).eval()
						# first forward pass
						__magic_name__							=    model(UpperCAmelCase__  , use_cache=UpperCAmelCase__      )
						__magic_name__							=    model(UpperCAmelCase__      )
						__magic_name__							=    model(UpperCAmelCase__  , use_cache=UpperCAmelCase__      )

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

						__magic_name__							=    outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						__magic_name__							=    ids_tensor((self.batch_size, 1)  , config.vocab_size      )

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

						__magic_name__							=    model(UpperCAmelCase__      )['''last_hidden_state''']
						__magic_name__							=    model(UpperCAmelCase__  , past_key_values=UpperCAmelCase__      )['''last_hidden_state''']

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: int  , ):

						'''simple docstring'''





						__magic_name__							=    UMTaModel(config=UpperCAmelCase__      ).to(UpperCAmelCase__      ).half().eval()
						__magic_name__							=    model(**UpperCAmelCase__      )['''last_hidden_state''']
						self.parent.assertFalse(torch.isnan(UpperCAmelCase__      ).any().item()      )




@require_torch
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   str       				=  (
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	_lowercase  :   str       				=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
	_lowercase  :   Any       				=  (
	    {
	        'conversational': UMTaForConditionalGeneration,
	        'feature-extraction': UMTaModel,
	        'summarization': UMTaForConditionalGeneration,
	        'text2text-generation': UMTaForConditionalGeneration,
	        'translation': UMTaForConditionalGeneration,
	        'question-answering': UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowercase  :   Dict       				=  True
	_lowercase  :   List[str]       				=  False
	_lowercase  :   Optional[int]       				=  False
	_lowercase  :   Optional[int]       				=  True
	_lowercase  :   List[str]       				=  True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	_lowercase  :   int       				=  [0.8, 0.9]







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    UMTaModelTester(self      )







	@unittest.skip('Test has a segmentation fault on torch 1.8.0'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						__magic_name__							=    UMTaModel(config_and_inputs[0]      ).to(UpperCAmelCase__      )
						with tempfile.TemporaryDirectory() as tmpdirname:
											torch.onnx.export(
											    UpperCAmelCase__  , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  , F'{tmpdirname}/t5_test.onnx'  , export_params=UpperCAmelCase__  , opset_version=9  , input_names=['input_ids', 'decoder_input_ids']  , )







	@unittest.skipIf(torch_device == 'cpu'  , 'Cant do half precision'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						__magic_name__							=    config_and_inputs[0]
						__magic_name__							=    UMTaForConditionalGeneration(UpperCAmelCase__      ).eval()
						model.to(UpperCAmelCase__      )

						__magic_name__							=    {
						    '''head_mask''': torch.zeros(config.num_layers  , config.num_heads  , device=UpperCAmelCase__      ),
						    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers  , config.num_heads  , device=UpperCAmelCase__      ),
						    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers  , config.num_heads  , device=UpperCAmelCase__      ),
						}

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

											__magic_name__							=    model.generate(
											    config_and_inputs[1]['input_ids']  , num_beams=1  , max_length=3  , output_attentions=UpperCAmelCase__  , return_dict_in_generate=UpperCAmelCase__  , **UpperCAmelCase__  , )
											# We check the state of decoder_attentions and cross_attentions just from the last step
											__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						pass




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







	@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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  , return_dict=UpperCAmelCase__      ).to(UpperCAmelCase__      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/umt5-small'  , use_fast=UpperCAmelCase__  , legacy=UpperCAmelCase__      )
						__magic_name__							=    [
						    '''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>.''',
						]
						__magic_name__							=    tokenizer(UpperCAmelCase__  , return_tensors='pt'  , padding=UpperCAmelCase__      ).input_ids
						# fmt: off
						__magic_name__							=    torch.tensor(
						    [
						        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
						    ]      )
						# fmt: on
						torch.testing.assert_allclose(UpperCAmelCase__  , UpperCAmelCase__      )

						__magic_name__							=    model.generate(input_ids.to(UpperCAmelCase__      )      )
						__magic_name__							=    [
						    '''<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>''',
						]
						__magic_name__							=    tokenizer.batch_decode(UpperCAmelCase__      )
						self.assertEqual(UpperCAmelCase__  , UpperCAmelCase__      )







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						with open(self.vocab_file  , 'w'  , encoding='utf-8'      ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




