






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  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=99    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=9    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=37    ,	_lowerCAmelCase=8    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.002    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				parent
          lowerCAmelCase__   :Any      							=				batch_size
          lowerCAmelCase__   :Tuple      							=				encoder_seq_length
          lowerCAmelCase__   :str      							=				decoder_seq_length
          # For common tests
          lowerCAmelCase__   :Union[str, Any]      							=				self.decoder_seq_length
          lowerCAmelCase__   :List[Any]      							=				is_training
          lowerCAmelCase__   :Tuple      							=				use_attention_mask
          lowerCAmelCase__   :List[str]      							=				use_labels
          lowerCAmelCase__   :Tuple      							=				vocab_size
          lowerCAmelCase__   :Tuple      							=				hidden_size
          lowerCAmelCase__   :List[Any]      							=				num_hidden_layers
          lowerCAmelCase__   :Any      							=				num_attention_heads
          lowerCAmelCase__   :List[Any]      							=				d_ff
          lowerCAmelCase__   :Optional[int]      							=				relative_attention_num_buckets
          lowerCAmelCase__   :int      							=				dropout_rate
          lowerCAmelCase__   :Union[str, Any]      							=				initializer_factor
          lowerCAmelCase__   :List[str]      							=				eos_token_id
          lowerCAmelCase__   :Tuple      							=				pad_token_id
          lowerCAmelCase__   :List[Any]      							=				decoder_start_token_id
          lowerCAmelCase__   :Optional[int]      							=				None
          lowerCAmelCase__   :List[str]      							=				decoder_layers
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return TaConfig.from_pretrained("google/umt5-base"		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	):



          '''simple docstring'''




          if attention_mask is None:
              lowerCAmelCase__   :Optional[Any]      							=				input_ids.ne(config.pad_token_id		)
          if decoder_attention_mask is None:
              lowerCAmelCase__   :Dict      							=				decoder_input_ids.ne(config.pad_token_id		)
          if head_mask is None:
              lowerCAmelCase__   :Optional[int]      							=				torch.ones(config.num_hidden_layers    ,	config.num_attention_heads    ,	device=_lowerCAmelCase		)
          if decoder_head_mask is None:
              lowerCAmelCase__   :str      							=				torch.ones(config.num_decoder_layers    ,	config.num_attention_heads    ,	device=_lowerCAmelCase		)
          if cross_attn_head_mask is None:
              lowerCAmelCase__   :Tuple      							=				torch.ones(
                  config.num_decoder_layers    ,	config.num_attention_heads    ,	device=_lowerCAmelCase		)
          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		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				ids_tensor([self.batch_size, self.encoder_seq_length]    ,	self.vocab_size		)
          lowerCAmelCase__   :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
          lowerCAmelCase__   :Optional[Any]      							=				input_ids.clamp(self.pad_token_id + 1		)
          lowerCAmelCase__   :str      							=				decoder_input_ids.clamp(self.pad_token_id + 1		)

          lowerCAmelCase__   :Any      							=				self.get_config()
          lowerCAmelCase__   :Dict      							=				config.num_attention_heads
          lowerCAmelCase__   :Optional[Any]      							=				self.prepare_inputs_dict(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)
          return config, input_dict
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				self.prepare_config_and_inputs()
          return config, inputs_dict
      def 			snake_case_      (   self		):



          '''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		):



          '''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    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				UMTaModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :List[str]      							=				model(
              input_ids=_lowerCAmelCase    ,	decoder_input_ids=_lowerCAmelCase    ,	attention_mask=_lowerCAmelCase    ,	decoder_attention_mask=_lowerCAmelCase    ,	)
          lowerCAmelCase__   :Optional[Any]      							=				model(input_ids=_lowerCAmelCase    ,	decoder_input_ids=_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				result.last_hidden_state
          lowerCAmelCase__   :Any      							=				result.past_key_values
          lowerCAmelCase__   :Dict      							=				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(_lowerCAmelCase		)    ,	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    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				UMTaModel(config=_lowerCAmelCase		).get_decoder().to(_lowerCAmelCase		).eval()
          # first forward pass
          lowerCAmelCase__   :Optional[Any]      							=				model(_lowerCAmelCase    ,	use_cache=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				model(_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				model(_lowerCAmelCase    ,	use_cache=_lowerCAmelCase		)

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

          lowerCAmelCase__   :List[str]      							=				outputs.to_tuple()

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

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

          lowerCAmelCase__   :Any      							=				model(_lowerCAmelCase		)["""last_hidden_state"""]
          lowerCAmelCase__   :List[Any]      							=				model(_lowerCAmelCase    ,	past_key_values=_lowerCAmelCase		)["""last_hidden_state"""]

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

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



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				UMTaModel(config=_lowerCAmelCase		).to(_lowerCAmelCase		).half().eval()
          lowerCAmelCase__   :Union[str, Any]      							=				model(**_lowerCAmelCase		)["""last_hidden_state"""]
          self.parent.assertFalse(torch.isnan(_lowerCAmelCase		).any().item()		)







@require_torch
class 	_UpperCAmelCase  (  UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      A          =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
      A          =    (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      A          =    True
      A          =    False
      A          =    False
      A          =    True
      A          =    True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      A          =    [0.8, 0.9]
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				UMTaModelTester(self		)
      @unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.model_tester.prepare_config_and_inputs()
          lowerCAmelCase__   :Dict      							=				UMTaModel(config_and_inputs[0]		).to(_lowerCAmelCase		)
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  _lowerCAmelCase    ,	(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,	f'''{tmpdirname}/t5_test.onnx'''    ,	export_params=_lowerCAmelCase    ,	opset_version=9    ,	input_names=["input_ids", "decoder_input_ids"]    ,	)
      @unittest.skipIf(torch_device == "cpu"    ,	"Cant do half precision"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
          lowerCAmelCase__   :List[Any]      							=				self.model_tester.prepare_config_and_inputs()
          lowerCAmelCase__   :List[str]      							=				config_and_inputs[0]
          lowerCAmelCase__   :Union[str, Any]      							=				UMTaForConditionalGeneration(_lowerCAmelCase		).eval()
          model.to(_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				{
              """head_mask""": torch.zeros(config.num_layers    ,	config.num_heads    ,	device=_lowerCAmelCase		),
              """decoder_head_mask""": torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=_lowerCAmelCase		),
              """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=_lowerCAmelCase		),
          }

          for attn_name, (name, mask) in zip(_lowerCAmelCase    ,	head_masking.items()		):
              lowerCAmelCase__   :Union[str, Any]      							=				{name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                  lowerCAmelCase__   :Dict      							=				torch.ones(
                      config.num_decoder_layers    ,	config.num_heads    ,	device=_lowerCAmelCase		)

              lowerCAmelCase__   :int      							=				model.generate(
                  config_and_inputs[1]["input_ids"]    ,	num_beams=1    ,	max_length=3    ,	output_attentions=_lowerCAmelCase    ,	return_dict_in_generate=_lowerCAmelCase    ,	**_lowerCAmelCase    ,	)
              # We check the state of decoder_attentions and cross_attentions just from the last step
              lowerCAmelCase__   :Tuple      							=				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		):



          '''simple docstring'''
          pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      @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		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				UMTaForConditionalGeneration.from_pretrained("google/umt5-small"    ,	return_dict=_lowerCAmelCase		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				AutoTokenizer.from_pretrained("google/umt5-small"    ,	use_fast=_lowerCAmelCase    ,	legacy=_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				[
              """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__   :int      							=				tokenizer(_lowerCAmelCase    ,	return_tensors="pt"    ,	padding=_lowerCAmelCase		).input_ids
          # fmt: off
          lowerCAmelCase__   :Any      							=				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(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				model.generate(input_ids.to(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Dict      							=				[
              """<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__   :Optional[int]      							=				tokenizer.batch_decode(_lowerCAmelCase		)
          self.assertEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)











from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info


def 				snake_case__							(							UpperCAmelCase							:  Dict       ):
    return {key.lstrip("-"       ): value for key, value in zip(unknown_args[::2]    ,			unknown_args[1::2]       )}


def 				snake_case__							(							):
    lowerCAmelCase__   :str      							=				ArgumentParser(
        "HuggingFace Datasets CLI tool"    ,			usage="datasets-cli <command> [<args>]"    ,			allow_abbrev=UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				parser.add_subparsers(help="datasets-cli command helpers"       )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(UpperCAmelCase       )
    EnvironmentCommand.register_subcommand(UpperCAmelCase       )
    TestCommand.register_subcommand(UpperCAmelCase       )
    RunBeamCommand.register_subcommand(UpperCAmelCase       )
    DummyDataCommand.register_subcommand(UpperCAmelCase       )

    # Parse args
    lowerCAmelCase__							,lowerCAmelCase__   :int      							=				parser.parse_known_args()
    if not hasattr(UpperCAmelCase    ,			"func"       ):
        parser.print_help()
        exit(1       )
    lowerCAmelCase__   :Optional[int]      							=				parse_unknown_args(UpperCAmelCase       )

    # Run
    lowerCAmelCase__   :str      							=				args.func(UpperCAmelCase    ,			**UpperCAmelCase       )
    service.run()


if __name__ == "__main__":
 main()



