





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   A      :
			'''simple docstring'''




			def __init__(      self       :   Optional[Any]							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   List[Any]=99							,     __lowerCAmelCase       :   Union[str, Any]=13							,     __lowerCAmelCase       :   List[Any]=7							,     __lowerCAmelCase       :   List[str]=9							,     __lowerCAmelCase       :   Any=True							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   Dict=False							,     __lowerCAmelCase       :   Tuple=32							,     __lowerCAmelCase       :   Any=5							,     __lowerCAmelCase       :   Dict=4							,     __lowerCAmelCase       :   Optional[int]=37							,     __lowerCAmelCase       :   List[str]=8							,     __lowerCAmelCase       :   Dict=0.1							,     __lowerCAmelCase       :   List[Any]=0.0_0_2							,     __lowerCAmelCase       :   int=1							,     __lowerCAmelCase       :   Optional[int]=0							,     __lowerCAmelCase       :   str=0							,     __lowerCAmelCase       :   Optional[Any]=None							,     __lowerCAmelCase       :   List[str]=None							,     )     ->     List[str]:







					"""simple docstring"""






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






			def        a_	(      self       :   int       )     ->     Dict:







					"""simple docstring"""






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






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Optional[Any]=None							,     __lowerCAmelCase       :   Optional[Any]=None							,     __lowerCAmelCase       :   str=None							,     __lowerCAmelCase       :   List[str]=None							,     __lowerCAmelCase       :   int=None							,     )     ->     Any:







					"""simple docstring"""






					if attention_mask is None:
							A__  				=       input_ids.ne(config.pad_token_id       )
					if decoder_attention_mask is None:
							A__  				=       decoder_input_ids.ne(config.pad_token_id       )
					if head_mask is None:
							A__  				=       torch.ones(config.num_hidden_layers							,     config.num_attention_heads							,     device=__lowerCAmelCase       )
					if decoder_head_mask is None:
							A__  				=       torch.ones(config.num_decoder_layers							,     config.num_attention_heads							,     device=__lowerCAmelCase       )
					if cross_attn_head_mask is None:
							A__  				=       torch.ones(
							    config.num_decoder_layers							,     config.num_attention_heads							,     device=__lowerCAmelCase       )
					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        a_	(      self       :   Optional[Any]       )     ->     Tuple:







					"""simple docstring"""






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

					A__  				=       self.get_config()
					A__  				=       config.num_attention_heads
					A__  				=       self.prepare_inputs_dict(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )
					return config, input_dict






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







					"""simple docstring"""






					A__				,      A__  				=       self.prepare_config_and_inputs()
					return config, inputs_dict






			def        a_	(      self       :   Dict       )     ->     List[str]:







					"""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        a_	(      self       :   Any       )     ->     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        a_	(      self       :   int							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Optional[Any]							,     )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       UMTaModel(config=__lowerCAmelCase       )
					model.to(__lowerCAmelCase       )
					model.eval()
					A__  				=       model(
					    input_ids=__lowerCAmelCase							,     decoder_input_ids=__lowerCAmelCase							,     attention_mask=__lowerCAmelCase							,     decoder_attention_mask=__lowerCAmelCase							,     )
					A__  				=       model(input_ids=__lowerCAmelCase							,     decoder_input_ids=__lowerCAmelCase       )
					A__  				=       result.last_hidden_state
					A__  				=       result.past_key_values
					A__  				=       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(__lowerCAmelCase       )							,     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        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   Tuple							,     )     ->     Optional[int]:







					"""simple docstring"""






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

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

					A__				,      A__  				=       outputs.to_tuple()

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

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

					A__  				=       model(__lowerCAmelCase       )["""last_hidden_state"""]
					A__  				=       model(__lowerCAmelCase							,     past_key_values=__lowerCAmelCase       )["""last_hidden_state"""]

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

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







			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   int							,     )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       UMTaModel(config=__lowerCAmelCase       ).to(__lowerCAmelCase       ).half().eval()
					A__  				=       model(**__lowerCAmelCase       )["""last_hidden_state"""]
					self.parent.assertFalse(torch.isnan(__lowerCAmelCase       ).any().item()       )


@require_torch
class   A      (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




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






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







					"""simple docstring"""






					A__  				=       UMTaModelTester(self       )






			@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""       )
			def        a_	(      self       :   Optional[int]       )     ->     str:







					"""simple docstring"""






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






			@unittest.skipIf(torch_device == """cpu"""							,     """Cant do half precision"""       )
			def        a_	(      self       :   Tuple       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*__lowerCAmelCase       )






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







					"""simple docstring"""






					A__  				=       ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
					A__  				=       self.model_tester.prepare_config_and_inputs()
					A__  				=       config_and_inputs[0]
					A__  				=       UMTaForConditionalGeneration(__lowerCAmelCase       ).eval()
					model.to(__lowerCAmelCase       )

					A__  				=       {
					    """head_mask""": torch.zeros(config.num_layers							,     config.num_heads							,     device=__lowerCAmelCase       ),
					    """decoder_head_mask""": torch.zeros(config.num_decoder_layers							,     config.num_heads							,     device=__lowerCAmelCase       ),
					    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers							,     config.num_heads							,     device=__lowerCAmelCase       ),
					}

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

							A__  				=       model.generate(
							    config_and_inputs[1]["""input_ids"""]							,     num_beams=1							,     max_length=3							,     output_attentions=__lowerCAmelCase							,     return_dict_in_generate=__lowerCAmelCase							,     **__lowerCAmelCase							,     )
							# We check the state of decoder_attentions and cross_attentions just from the last step
							A__  				=       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        a_	(      self       :   List[Any]       )     ->     Optional[int]:







					"""simple docstring"""






					pass


@require_torch
@require_sentencepiece
@require_tokenizers
class   A      (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        a_	(      self       :   str       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""							,     return_dict=__lowerCAmelCase       ).to(__lowerCAmelCase       )
					A__  				=       AutoTokenizer.from_pretrained("""google/umt5-small"""							,     use_fast=__lowerCAmelCase							,     legacy=__lowerCAmelCase       )
					A__  				=       [
					    """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>.""",
					]
					A__  				=       tokenizer(__lowerCAmelCase							,     return_tensors="""pt"""							,     padding=__lowerCAmelCase       ).input_ids
					# fmt: off
					A__  				=       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(__lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       model.generate(input_ids.to(__lowerCAmelCase       )       )
					A__  				=       [
					    """<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>""",
					]
					A__  				=       tokenizer.batch_decode(__lowerCAmelCase       )
					self.assertEqual(__lowerCAmelCase							,     __lowerCAmelCase       )










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   : Tuple			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Union[str, Any]          =   '''roberta'''






			def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=5_02_65							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=5_12							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   Dict=1e-12							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   Tuple=0							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Union[str, Any]=None							,     **__lowerCAmelCase       :   str							,     )     ->     str:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,     bos_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase       )

					A__  				=       vocab_size
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       hidden_act
					A__  				=       intermediate_size
					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       max_position_embeddings
					A__  				=       type_vocab_size
					A__  				=       initializer_range
					A__  				=       layer_norm_eps
					A__  				=       position_embedding_type
					A__  				=       use_cache
					A__  				=       classifier_dropout


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   int       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__  				=       {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__  				=       {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]       )



