





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



									"""simple docstring"""



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


							def      _snake_case  (							self    :Any		)   ->   str:



									"""simple docstring"""



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


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



									"""simple docstring"""



									if attention_mask is None:
											SCREAMING_SNAKE_CASE__			=      input_ids.ne(config.pad_token_id		)
									if decoder_attention_mask is None:
											SCREAMING_SNAKE_CASE__			=      decoder_input_ids.ne(config.pad_token_id		)
									if head_mask is None:
											SCREAMING_SNAKE_CASE__			=      torch.ones(config.num_hidden_layers							,				config.num_attention_heads							,				device=__A		)
									if decoder_head_mask is None:
											SCREAMING_SNAKE_CASE__			=      torch.ones(config.num_decoder_layers							,				config.num_attention_heads							,				device=__A		)
									if cross_attn_head_mask is None:
											SCREAMING_SNAKE_CASE__			=      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    :Any		)   ->   Tuple:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      self.get_config()
									SCREAMING_SNAKE_CASE__			=      config.num_attention_heads
									SCREAMING_SNAKE_CASE__			=      self.prepare_inputs_dict(__A							,				__A							,				__A		)
									return config, input_dict


							def      _snake_case  (							self    :str		)   ->   Dict:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Any		)   ->   Optional[Any]:



									"""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    :Dict		)   ->   Optional[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    :Tuple							,				__A    :List[str]							,				__A    :Optional[Any]							,				__A    :Tuple							,				__A    :Any							,				__A    :Optional[Any]							,				__A    :int							,				)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      UMTaModel(config=__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__			=      model(
									    input_ids=__A							,				decoder_input_ids=__A							,				attention_mask=__A							,				decoder_attention_mask=__A							,				)
									SCREAMING_SNAKE_CASE__			=      model(input_ids=__A							,				decoder_input_ids=__A		)
									SCREAMING_SNAKE_CASE__			=      result.last_hidden_state
									SCREAMING_SNAKE_CASE__			=      result.past_key_values
									SCREAMING_SNAKE_CASE__			=      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    :Optional[int]							,				__A    :List[Any]							,				__A    :str							,				__A    :str							,				__A    :Dict							,				__A    :Optional[int]							,				__A    :Any							,				)   ->   Optional[Any]:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      outputs.to_tuple()

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

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

									SCREAMING_SNAKE_CASE__			=      model(__A		)["""last_hidden_state"""]
									SCREAMING_SNAKE_CASE__			=      model(__A							,				past_key_values=__A		)["""last_hidden_state"""]

									# select random slice
									SCREAMING_SNAKE_CASE__			=      ids_tensor((1,)							,				output_from_past.shape[-1]		).item()
									SCREAMING_SNAKE_CASE__			=      output_from_no_past[:, -1, random_slice_idx].detach()
									SCREAMING_SNAKE_CASE__			=      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    :str							,				__A    :Union[str, Any]							,				__A    :Optional[int]							,				)   ->   Dict:



									"""simple docstring"""



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




@require_torch
class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					(
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							lowerCamelCase_           =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
							lowerCamelCase_           =					(
							    {
							        "conversational": UMTaForConditionalGeneration,
							        "feature-extraction": UMTaModel,
							        "summarization": UMTaForConditionalGeneration,
							        "text2text-generation": UMTaForConditionalGeneration,
							        "translation": UMTaForConditionalGeneration,
							        "question-answering": UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase_           =					True
							lowerCamelCase_           =					False
							lowerCamelCase_           =					False
							lowerCamelCase_           =					True
							lowerCamelCase_           =					True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							lowerCamelCase_           =					[0.8, 0.9]


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      UMTaModelTester(self		)


							@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""		)
							def      _snake_case  (							self    :Any		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__			=      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    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*__A		)


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



									"""simple docstring"""



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

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

											SCREAMING_SNAKE_CASE__			=      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
											SCREAMING_SNAKE_CASE__			=      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[Any]		)   ->   int:



									"""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      _snake_case  (							self    :Any		)   ->   List[str]:



									"""simple docstring"""



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

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











from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
		SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
		return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError("""Input Error: Molar mass values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)