






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       ,A__       ,A__=99       ,A__=13       ,A__=7       ,A__=9       ,A__=True       ,A__=True       ,A__=False       ,A__=32       ,A__=5       ,A__=4       ,A__=37       ,A__=8       ,A__=0.1       ,A__=0.0_02       ,A__=1       ,A__=0       ,A__=0       ,A__=None       ,A__=None       ,):
						_A	:				Optional[Any]   =      parent
						_A	:				Any   =      batch_size
						_A	:				List[str]   =      encoder_seq_length
						_A	:				Tuple   =      decoder_seq_length
						# For common tests
						_A	:				Optional[Any]   =      self.decoder_seq_length
						_A	:				Any   =      is_training
						_A	:				int   =      use_attention_mask
						_A	:				int   =      use_labels
						_A	:				List[Any]   =      vocab_size
						_A	:				Tuple   =      hidden_size
						_A	:				str   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Any   =      d_ff
						_A	:				List[Any]   =      relative_attention_num_buckets
						_A	:				str   =      dropout_rate
						_A	:				Union[str, Any]   =      initializer_factor
						_A	:				Union[str, Any]   =      eos_token_id
						_A	:				Dict   =      pad_token_id
						_A	:				Any   =      decoder_start_token_id
						_A	:				Union[str, Any]   =      None
						_A	:				int   =      decoder_layers







		def  A__      (					self		):
						return TaConfig.from_pretrained('''google/umt5-base'''		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
						if attention_mask is None:
										_A	:				Optional[int]   =      input_ids.ne(config.pad_token_id		)
						if decoder_attention_mask is None:
										_A	:				Dict   =      decoder_input_ids.ne(config.pad_token_id		)
						if head_mask is None:
										_A	:				List[str]   =      torch.ones(config.num_hidden_layers       ,config.num_attention_heads       ,device=A__		)
						if decoder_head_mask is None:
										_A	:				Union[str, Any]   =      torch.ones(config.num_decoder_layers       ,config.num_attention_heads       ,device=A__		)
						if cross_attn_head_mask is None:
										_A	:				Any   =      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  A__      (					self		):
						_A	:				List[str]   =      ids_tensor([self.batch_size, self.encoder_seq_length]       ,self.vocab_size		)
						_A	:				Union[str, Any]   =      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	:				Tuple   =      input_ids.clamp(self.pad_token_id + 1		)
						_A	:				Tuple   =      decoder_input_ids.clamp(self.pad_token_id + 1		)

						_A	:				Optional[int]   =      self.get_config()
						_A	:				Optional[int]   =      config.num_attention_heads
						_A	:				List[str]   =      self.prepare_inputs_dict(A__       ,A__       ,A__		)
						return config, input_dict







		def  A__      (					self		):
						_A  ,				_A	:				str   =      self.prepare_config_and_inputs()
						return config, inputs_dict







		def  A__      (					self		):
						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  A__      (					self		):
						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       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
						_A	:				int   =      UMTaModel(config=A__		)
						model.to(A__		)
						model.eval()
						_A	:				Tuple   =      model(
						    input_ids=A__       ,decoder_input_ids=A__       ,attention_mask=A__       ,decoder_attention_mask=A__       ,)
						_A	:				Any   =      model(input_ids=A__       ,decoder_input_ids=A__		)
						_A	:				str   =      result.last_hidden_state
						_A	:				int   =      result.past_key_values
						_A	:				List[str]   =      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  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
						_A	:				List[Any]   =      UMTaModel(config=A__		).get_decoder().to(A__		).eval()
						# first forward pass
						_A	:				Dict   =      model(A__       ,use_cache=A__		)
						_A	:				Union[str, Any]   =      model(A__		)
						_A	:				List[Any]   =      model(A__       ,use_cache=A__		)

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

						_A  ,				_A	:				Tuple   =      outputs.to_tuple()

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

						# append to next input_ids and
						_A	:				List[Any]   =      torch.cat([input_ids, next_tokens]       ,dim=-1		)

						_A	:				int   =      model(A__		)['''last_hidden_state''']
						_A	:				Optional[Any]   =      model(A__       ,past_key_values=A__		)['''last_hidden_state''']

						# select random slice
						_A	:				Tuple   =      ids_tensor((1,)       ,output_from_past.shape[-1]		).item()
						_A	:				Any   =      output_from_no_past[:, -1, random_slice_idx].detach()
						_A	:				str   =      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  A__      (					self       ,A__       ,A__       ,):
						_A	:				int   =      UMTaModel(config=A__		).to(A__		).half().eval()
						_A	:				List[str]   =      model(**A__		)['''last_hidden_state''']
						self.parent.assertFalse(torch.isnan(A__		).any().item()		)






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







		def  A__      (					self		):
						_A	:				int   =      UMTaModelTester(self		)







		@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''		)
		def  A__      (					self		):
						_A	:				Optional[int]   =      self.model_tester.prepare_config_and_inputs()
						_A	:				int   =      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  A__      (					self		):
						_A	:				Optional[int]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*A__		)







		def  A__      (					self		):
						_A	:				Optional[int]   =      ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
						_A	:				List[Any]   =      self.model_tester.prepare_config_and_inputs()
						_A	:				str   =      config_and_inputs[0]
						_A	:				Optional[Any]   =      UMTaForConditionalGeneration(A__		).eval()
						model.to(A__		)

						_A	:				Optional[Any]   =      {
						    '''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()		):
										_A	:				Dict   =      {name: mask}
										# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
										if name == "head_mask":
														_A	:				Union[str, Any]   =      torch.ones(
														    config.num_decoder_layers       ,config.num_heads       ,device=A__		)

										_A	:				str   =      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
										_A	:				Union[str, Any]   =      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		):
						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  A__      (					self		):
						_A	:				Union[str, Any]   =      UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''       ,return_dict=A__		).to(A__		)
						_A	:				Optional[Any]   =      AutoTokenizer.from_pretrained('''google/umt5-small'''       ,use_fast=A__       ,legacy=A__		)
						_A	:				Tuple   =      [
						    '''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	:				Union[str, Any]   =      tokenizer(A__       ,return_tensors='''pt'''       ,padding=A__		).input_ids
						# fmt: off
						_A	:				int   =      torch.tensor(
						    [
						        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
						    ]		)
						# fmt: on
						torch.testing.assert_allclose(A__       ,A__		)

						_A	:				str   =      model.generate(input_ids.to(A__		)		)
						_A	:				Optional[int]   =      [
						    '''<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	:				List[str]   =      tokenizer.batch_decode(A__		)
						self.assertEqual(A__       ,A__		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase			:	Any								={
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[Any]								=[
	    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MegatronBertForCausalLM',
	    'MegatronBertForMaskedLM',
	    'MegatronBertForMultipleChoice',
	    'MegatronBertForNextSentencePrediction',
	    'MegatronBertForPreTraining',
	    'MegatronBertForQuestionAnswering',
	    'MegatronBertForSequenceClassification',
	    'MegatronBertForTokenClassification',
	    'MegatronBertModel',
	    'MegatronBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	_UpperCamelCase			:	Optional[int]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



