





"""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      ,						)		->     Optional[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 )		->     str:
            return TaConfig.from_pretrained("""google/umt5-base""" )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						)		->     Optional[Any]:
            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=lowercase )
            if decoder_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            if cross_attn_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(
                      config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            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 )		->     int:
            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(lowercase      ,						lowercase      ,						lowercase )
            return config, input_dict





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





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

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

            lowerCAmelCase						,		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(lowercase )["""last_hidden_state"""]
            lowerCAmelCase  						=       model(lowercase      ,						past_key_values=lowercase )["""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(lowercase      ,						lowercase      ,						atol=1e-3 ) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       UMTaModel(config=lowercase ).to(lowercase ).half().eval()
            lowerCAmelCase  						=       model(**lowercase )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(lowercase ).any().item() )







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  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 )		->     str:
            lowerCAmelCase  						=       UMTaModelTester(self )





      @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       UMTaModel(config_and_inputs[0] ).to(lowercase )
            with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      lowercase      ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,						f'{tmpdirname}/t5_test.onnx'      ,						export_params=lowercase      ,						opset_version=9      ,						input_names=["""input_ids""", """decoder_input_ids"""]      ,						)





      @unittest.skipIf(torch_device == """cpu"""      ,						"""Cant do half precision""" )
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*lowercase )





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       config_and_inputs[0]
            lowerCAmelCase  						=       UMTaForConditionalGeneration(lowercase ).eval()
            model.to(lowercase )

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

            for attn_name, (name, mask) in zip(lowercase      ,						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=lowercase )

                  lowerCAmelCase  						=       model.generate(
                      config_and_inputs[1]["""input_ids"""]      ,						num_beams=1      ,						max_length=3      ,						output_attentions=lowercase      ,						return_dict_in_generate=lowercase      ,						**lowercase      ,						)
                  # 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 )		->     Union[str, Any]:
            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 )		->     Optional[Any]:
            lowerCAmelCase  						=       UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""      ,						return_dict=lowercase ).to(lowercase )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""google/umt5-small"""      ,						use_fast=lowercase      ,						legacy=lowercase )
            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(lowercase      ,						return_tensors="""pt"""      ,						padding=lowercase ).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(lowercase      ,						lowercase )

            lowerCAmelCase  						=       model.generate(input_ids.to(lowercase ) )
            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(lowercase )
            self.assertEqual(lowercase      ,						lowercase )












'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_									=				SqlDatasetReader(
										    '''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase,      keep_in_memory=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features''',      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],      )
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					snake_case_									=				features.copy() if features else default_expected_features
					snake_case_									=				(
					    Features({feature: Value(__UpperCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      features=__UpperCAmelCase,      cache_dir=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					with contextlib.closing(sqlitea.connect(__UpperCAmelCase	)	) as con:
										snake_case_									=				con.cursor()
										cur.execute('''SELECT * FROM dataset'''	)
										for row in cur:
															yield row


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=1	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=2	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					with pytest.raises(__UpperCAmelCase	):
										SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=0	).write()

