


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							:		List[Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Dict=9_9       ,       __magic_name__							:		Any=1_3       ,       __magic_name__							:		Tuple=7       ,       __magic_name__							:		List[Any]=9       ,       __magic_name__							:		Dict=True       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		List[str]=False       ,       __magic_name__							:		Tuple=3_2       ,       __magic_name__							:		Tuple=5       ,       __magic_name__							:		str=4       ,       __magic_name__							:		Optional[int]=3_7       ,       __magic_name__							:		Union[str, Any]=8       ,       __magic_name__							:		Optional[Any]=0.1       ,       __magic_name__							:		int=0.002       ,       __magic_name__							:		List[Any]=1       ,       __magic_name__							:		List[Any]=0       ,       __magic_name__							:		str=0       ,       __magic_name__							:		int=None       ,       __magic_name__							:		Optional[Any]=None       ,       ):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		List[str]=None       ,       __magic_name__							:		List[str]=None       ,       __magic_name__							:		Any=None       ,       __magic_name__							:		Optional[Any]=None       ,       ):







						"""simple docstring"""







						if attention_mask is None:
									UpperCamelCase         =					input_ids.ne(config.pad_token_id						)
						if decoder_attention_mask is None:
									UpperCamelCase         =					decoder_input_ids.ne(config.pad_token_id						)
						if head_mask is None:
									UpperCamelCase         =					torch.ones(config.num_hidden_layers       ,       config.num_attention_heads       ,       device=__magic_name__						)
						if decoder_head_mask is None:
									UpperCamelCase         =					torch.ones(config.num_decoder_layers       ,       config.num_attention_heads       ,       device=__magic_name__						)
						if cross_attn_head_mask is None:
									UpperCamelCase         =					torch.ones(
									    config.num_decoder_layers       ,       config.num_attention_heads       ,       device=__magic_name__						)
						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   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







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

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




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						return TaConfig(
						    vocab_size=1_6_6       ,       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   lowerCamelCase_					(						self							:		str						):







						"""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   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		Any       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					UMTaModel(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(
						    input_ids=__magic_name__       ,       decoder_input_ids=__magic_name__       ,       attention_mask=__magic_name__       ,       decoder_attention_mask=__magic_name__       ,       )
						UpperCamelCase         =					model(input_ids=__magic_name__       ,       decoder_input_ids=__magic_name__						)
						UpperCamelCase         =					result.last_hidden_state
						UpperCamelCase         =					result.past_key_values
						UpperCamelCase         =					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(__magic_name__						)       ,       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   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		List[Any]       ,       ):







						"""simple docstring"""







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

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

						UpperCamelCase  ,  UpperCamelCase         =					outputs.to_tuple()

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

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

						UpperCamelCase         =					model(__magic_name__						)["""last_hidden_state"""]
						UpperCamelCase         =					model(__magic_name__       ,       past_key_values=__magic_name__						)["""last_hidden_state"""]

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

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





			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		int       ,       ):







						"""simple docstring"""







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




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




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







						"""simple docstring"""







						UpperCamelCase         =					UMTaModelTester(self						)




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







						"""simple docstring"""







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




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







						"""simple docstring"""







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




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







						"""simple docstring"""







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

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

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

									UpperCamelCase         =					model.generate(
									    config_and_inputs[1]["""input_ids"""]       ,       num_beams=1       ,       max_length=3       ,       output_attentions=__magic_name__       ,       return_dict_in_generate=__magic_name__       ,       **__magic_name__       ,       )
									# We check the state of decoder_attentions and cross_attentions just from the last step
									UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Optional[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   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""       ,       return_dict=__magic_name__						).to(__magic_name__						)
						UpperCamelCase         =					AutoTokenizer.from_pretrained("""google/umt5-small"""       ,       use_fast=__magic_name__       ,       legacy=__magic_name__						)
						UpperCamelCase         =					[
						    """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>.""",
						]
						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""pt"""       ,       padding=__magic_name__						).input_ids
						# fmt: off
						UpperCamelCase         =					torch.tensor(
						    [
						        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
						    ]						)
						# fmt: on
						torch.testing.assert_allclose(__magic_name__       ,       __magic_name__						)

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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




