





"""simple docstring"""





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 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=99 ,		lowercase=13 ,		lowercase=7 ,		lowercase=9 ,		lowercase=True ,		lowercase=True ,		lowercase=False ,		lowercase=32 ,		lowercase=5 ,		lowercase=4 ,		lowercase=37 ,		lowercase=8 ,		lowercase=0.1 ,		lowercase=0.002 ,		lowercase=1 ,		lowercase=0 ,		lowercase=0 ,		lowercase=None ,		lowercase=None ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			encoder_seq_length
            lowerCAmelCase    		=			decoder_seq_length
            # For common tests
            lowerCAmelCase    		=			self.decoder_seq_length
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			use_attention_mask
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			num_attention_heads
            lowerCAmelCase    		=			d_ff
            lowerCAmelCase    		=			relative_attention_num_buckets
            lowerCAmelCase    		=			dropout_rate
            lowerCAmelCase    		=			initializer_factor
            lowerCAmelCase    		=			eos_token_id
            lowerCAmelCase    		=			pad_token_id
            lowerCAmelCase    		=			decoder_start_token_id
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			decoder_layers





      def        _snake_case     (			self					)						->	Tuple:
            return TaConfig.from_pretrained("""google/umt5-base"""					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		)						->	Tuple:
            if attention_mask is None:
                  lowerCAmelCase    		=			input_ids.ne(config.pad_token_id					)
            if decoder_attention_mask is None:
                  lowerCAmelCase    		=			decoder_input_ids.ne(config.pad_token_id					)
            if head_mask is None:
                  lowerCAmelCase    		=			torch.ones(config.num_hidden_layers ,		config.num_attention_heads ,		device=A__					)
            if decoder_head_mask is None:
                  lowerCAmelCase    		=			torch.ones(config.num_decoder_layers ,		config.num_attention_heads ,		device=A__					)
            if cross_attn_head_mask is None:
                  lowerCAmelCase    		=			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					)						->	Tuple:
            lowerCAmelCase    		=			ids_tensor([self.batch_size, self.encoder_seq_length] ,		self.vocab_size					)
            lowerCAmelCase    		=			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
            lowerCAmelCase    		=			input_ids.clamp(self.pad_token_id + 1					)
            lowerCAmelCase    		=			decoder_input_ids.clamp(self.pad_token_id + 1					)

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





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            return config, inputs_dict





      def        _snake_case     (			self					)						->	Dict:
            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					)						->	Tuple:
            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 ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			UMTaModel(config=A__					)
            model.to(A__					)
            model.eval()
            lowerCAmelCase    		=			model(
                input_ids=A__ ,		decoder_input_ids=A__ ,		attention_mask=A__ ,		decoder_attention_mask=A__ ,		)
            lowerCAmelCase    		=			model(input_ids=A__ ,		decoder_input_ids=A__					)
            lowerCAmelCase    		=			result.last_hidden_state
            lowerCAmelCase    		=			result.past_key_values
            lowerCAmelCase    		=			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 ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		)						->	str:
            lowerCAmelCase    		=			UMTaModel(config=A__					).get_decoder().to(A__					).eval()
            # first forward pass
            lowerCAmelCase    		=			model(A__ ,		use_cache=A__					)
            lowerCAmelCase    		=			model(A__					)
            lowerCAmelCase    		=			model(A__ ,		use_cache=A__					)

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

            lowerCAmelCase    		=			outputs.to_tuple()

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

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

            lowerCAmelCase    		=			model(A__					)['''last_hidden_state''']
            lowerCAmelCase    		=			model(A__ ,		past_key_values=A__					)['''last_hidden_state''']

            # select random slice
            lowerCAmelCase    		=			ids_tensor((1,) ,		output_from_past.shape[-1]					).item()
            lowerCAmelCase    		=			output_from_no_past[:, -1, random_slice_idx].detach()
            lowerCAmelCase    		=			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 ,		lowercase ,		lowercase ,		)						->	Dict:
            lowerCAmelCase    		=			UMTaModel(config=A__					).to(A__					).half().eval()
            lowerCAmelCase    		=			model(**A__					)['''last_hidden_state''']
            self.parent.assertFalse(torch.isnan(A__					).any().item()					)







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





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			UMTaModelTester(self					)





      @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""					)
      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase    		=			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					)						->	List[Any]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*A__					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase    		=			config_and_inputs[0]
            lowerCAmelCase    		=			UMTaForConditionalGeneration(A__					).eval()
            model.to(A__					)

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

                  lowerCAmelCase    		=			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
                  lowerCAmelCase    		=			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					)						->	int:
            pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase   (   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					)						->	str:
            lowerCAmelCase    		=			UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" ,		return_dict=A__					).to(A__					)
            lowerCAmelCase    		=			AutoTokenizer.from_pretrained("""google/umt5-small""" ,		use_fast=A__ ,		legacy=A__					)
            lowerCAmelCase    		=			[
                '''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>.''',
            ]
            lowerCAmelCase    		=			tokenizer(A__ ,		return_tensors="""pt""" ,		padding=A__					).input_ids
            # fmt: off
            lowerCAmelCase    		=			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__					)

            lowerCAmelCase    		=			model.generate(input_ids.to(A__					)					)
            lowerCAmelCase    		=			[
                '''<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>''',
            ]
            lowerCAmelCase    		=			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__)



