



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 			_lowerCAmelCase   :






			"""simple docstring"""





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







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





			def 		snake_case__			(				self	:       Optional[int]   )						->					List[str]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       str			,     lowercase	:       Dict			,     lowercase	:       int			,     lowercase	:       str=None			,     lowercase	:       Dict=None			,     lowercase	:       List[str]=None			,     lowercase	:       Any=None			,     lowercase	:       str=None			,     )						->					Optional[Any]:
									"""simple docstring"""







									if attention_mask is None:
															__lowercase					=				input_ids.ne(config.pad_token_id   )
									if decoder_attention_mask is None:
															__lowercase					=				decoder_input_ids.ne(config.pad_token_id   )
									if head_mask is None:
															__lowercase					=				torch.ones(config.num_hidden_layers			,     config.num_attention_heads			,     device=_A   )
									if decoder_head_mask is None:
															__lowercase					=				torch.ones(config.num_decoder_layers			,     config.num_attention_heads			,     device=_A   )
									if cross_attn_head_mask is None:
															__lowercase					=				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	:       Dict   )						->					List[Any]:
									"""simple docstring"""







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

									__lowercase					=				self.get_config()
									__lowercase					=				config.num_attention_heads
									__lowercase					=				self.prepare_inputs_dict(_A			,     _A			,     _A   )
									return config, input_dict





			def 		snake_case__			(				self	:       Optional[Any]   )						->					str:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]   )						->					Tuple:
									"""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	:       int   )						->					Any:
									"""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	:       str			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       Dict			,     lowercase	:       Union[str, Any]			,     lowercase	:       Any			,     lowercase	:       Optional[int]			,     )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				UMTaModel(config=_A   )
									model.to(_A   )
									model.eval()
									__lowercase					=				model(
									    input_ids=_A			,     decoder_input_ids=_A			,     attention_mask=_A			,     decoder_attention_mask=_A			,     )
									__lowercase					=				model(input_ids=_A			,     decoder_input_ids=_A   )
									__lowercase					=				result.last_hidden_state
									__lowercase					=				result.past_key_values
									__lowercase					=				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	:       List[Any]			,     lowercase	:       int			,     lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       List[Any]			,     lowercase	:       Any			,     lowercase	:       Optional[Any]			,     )						->					Union[str, Any]:
									"""simple docstring"""







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

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

									__lowercase	,			__lowercase					=				outputs.to_tuple()

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

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

									__lowercase					=				model(_A   )["""last_hidden_state"""]
									__lowercase					=				model(_A			,     past_key_values=_A   )["""last_hidden_state"""]

									# select random slice
									__lowercase					=				ids_tensor((1,)			,     output_from_past.shape[-1]   ).item()
									__lowercase					=				output_from_no_past[:, -1, random_slice_idx].detach()
									__lowercase					=				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	:       Optional[int]			,     lowercase	:       List[Any]			,     lowercase	:       Any			,     )						->					Dict:
									"""simple docstring"""







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





@require_torch
class 			_lowerCAmelCase   ( __lowercase    ,					__lowercase    ,					__lowercase    ,					unittest.TestCase			):






			"""simple docstring"""





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





			def 		snake_case__			(				self	:       Optional[int]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				UMTaModelTester(self   )





			@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""   )
			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									__lowercase					=				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	:       Any   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_A   )





			def 		snake_case__			(				self	:       Optional[int]   )						->					Any:
									"""simple docstring"""







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

									__lowercase					=				{
									    """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()   ):
															__lowercase					=				{name: mask}
															# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
															if name == "head_mask":
																					__lowercase					=				torch.ones(
																					    config.num_decoder_layers			,     config.num_heads			,     device=_A   )

															__lowercase					=				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
															__lowercase					=				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	:       List[str]   )						->					int:
									"""simple docstring"""







									pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 			_lowerCAmelCase   ( 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	:       List[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""			,     return_dict=_A   ).to(_A   )
									__lowercase					=				AutoTokenizer.from_pretrained("""google/umt5-small"""			,     use_fast=_A			,     legacy=_A   )
									__lowercase					=				[
									    """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>.""",
									]
									__lowercase					=				tokenizer(_A			,     return_tensors="""pt"""			,     padding=_A   ).input_ids
									# fmt: off
									__lowercase					=				torch.tensor(
									    [
									        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
									    ]   )
									# fmt: on
									torch.testing.assert_allclose(_A			,     _A   )

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ =						{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "LukeForEntityClassification",
			    "LukeForEntityPairClassification",
			    "LukeForEntitySpanClassification",
			    "LukeForMultipleChoice",
			    "LukeForQuestionAnswering",
			    "LukeForSequenceClassification",
			    "LukeForTokenClassification",
			    "LukeForMaskedLM",
			    "LukeModel",
			    "LukePreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





