





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  A__       :





      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	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       parent
       UpperCamelCase     : List[Any]	     =       batch_size
       UpperCamelCase     : List[Any]	     =       encoder_seq_length
       UpperCamelCase     : Tuple	     =       decoder_seq_length
       # For common tests
       UpperCamelCase     : Tuple	     =       self.decoder_seq_length
       UpperCamelCase     : Any	     =       is_training
       UpperCamelCase     : int	     =       use_attention_mask
       UpperCamelCase     : Optional[int]	     =       use_labels
       UpperCamelCase     : List[Any]	     =       vocab_size
       UpperCamelCase     : Union[str, Any]	     =       hidden_size
       UpperCamelCase     : List[str]	     =       num_hidden_layers
       UpperCamelCase     : List[Any]	     =       num_attention_heads
       UpperCamelCase     : Any	     =       d_ff
       UpperCamelCase     : List[Any]	     =       relative_attention_num_buckets
       UpperCamelCase     : List[Any]	     =       dropout_rate
       UpperCamelCase     : Union[str, Any]	     =       initializer_factor
       UpperCamelCase     : List[str]	     =       eos_token_id
       UpperCamelCase     : Optional[Any]	     =       pad_token_id
       UpperCamelCase     : Optional[int]	     =       decoder_start_token_id
       UpperCamelCase     : Optional[int]	     =       None
       UpperCamelCase     : str	     =       decoder_layers





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_=None	,							A_=None	,							A_=None	,							A_=None	,							A_=None	,							):
       '''simple docstring'''




       if attention_mask is None:
        UpperCamelCase     : int	     =       input_ids.ne(config.pad_token_id       )
       if decoder_attention_mask is None:
        UpperCamelCase     : List[Any]	     =       decoder_input_ids.ne(config.pad_token_id       )
       if head_mask is None:
        UpperCamelCase     : Any	     =       torch.ones(config.num_hidden_layers	,							config.num_attention_heads	,							device=A_       )
       if decoder_head_mask is None:
        UpperCamelCase     : Dict	     =       torch.ones(config.num_decoder_layers	,							config.num_attention_heads	,							device=A_       )
       if cross_attn_head_mask is None:
        UpperCamelCase     : Union[str, 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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     : Dict	     =       self.get_config()
       UpperCamelCase     : Any	     =       config.num_attention_heads
       UpperCamelCase     : Optional[int]	     =       self.prepare_inputs_dict(A_	,							A_	,							A_       )
       return config, input_dict





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       self.prepare_config_and_inputs()
       return config, inputs_dict





      def 	__UpperCamelCase(			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 	__UpperCamelCase(			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 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModel(config=A_       )
       model.to(A_       )
       model.eval()
       UpperCamelCase     : int	     =       model(
           input_ids=A_	,							decoder_input_ids=A_	,							attention_mask=A_	,							decoder_attention_mask=A_	,							)
       UpperCamelCase     : str	     =       model(input_ids=A_	,							decoder_input_ids=A_       )
       UpperCamelCase     : Dict	     =       result.last_hidden_state
       UpperCamelCase     : Tuple	     =       result.past_key_values
       UpperCamelCase     : 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 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModel(config=A_       ).get_decoder().to(A_       ).eval()
       # first forward pass
       UpperCamelCase     : Optional[Any]	     =       model(A_	,							use_cache=A_       )
       UpperCamelCase     : List[Any]	     =       model(A_       )
       UpperCamelCase     : List[Any]	     =       model(A_	,							use_cache=A_       )

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

       UpperCamelCase     ,   UpperCamelCase     : int	     =       outputs.to_tuple()

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

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

       UpperCamelCase     : Union[str, Any]	     =       model(A_       )["last_hidden_state"]
       UpperCamelCase     : List[str]	     =       model(A_	,							past_key_values=A_       )["last_hidden_state"]

       # select random slice
       UpperCamelCase     : Optional[int]	     =       ids_tensor((1,)	,							output_from_past.shape[-1]       ).item()
       UpperCamelCase     : List[str]	     =       output_from_no_past[:, -1, random_slice_idx].detach()
       UpperCamelCase     : Dict	     =       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 	__UpperCamelCase(			self	,							A_	,							A_	,							):
       '''simple docstring'''




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



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModelTester(self       )





      @unittest.skip("Test has a segmentation fault on torch 1.8.0"       )
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self.model_tester.prepare_config_and_inputs()
       UpperCamelCase     : Optional[Any]	     =       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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
       UpperCamelCase     : Dict	     =       self.model_tester.prepare_config_and_inputs()
       UpperCamelCase     : Tuple	     =       config_and_inputs[0]
       UpperCamelCase     : Tuple	     =       UMTaForConditionalGeneration(A_       ).eval()
       model.to(A_       )

       UpperCamelCase     : 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()       ):
        UpperCamelCase     : Optional[int]	     =       {name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
         UpperCamelCase     : List[str]	     =       torch.ones(
             config.num_decoder_layers	,							config.num_heads	,							device=A_       )

        UpperCamelCase     : List[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
        UpperCamelCase     : 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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass



@require_torch
@require_sentencepiece
@require_tokenizers
class  A__       (	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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       UMTaForConditionalGeneration.from_pretrained("google/umt5-small"	,							return_dict=A_       ).to(A_       )
       UpperCamelCase     : List[str]	     =       AutoTokenizer.from_pretrained("google/umt5-small"	,							use_fast=A_	,							legacy=A_       )
       UpperCamelCase     : Dict	     =       [
           "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     : Optional[Any]	     =       tokenizer(A_	,							return_tensors="pt"	,							padding=A_       ).input_ids
       # fmt: off
       UpperCamelCase     : int	     =       torch.tensor(
           [
               [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
           ]       )
       # fmt: on
       torch.testing.assert_allclose(A_	,							A_       )

       UpperCamelCase     : str	     =       model.generate(input_ids.to(A_       )       )
       UpperCamelCase     : str	     =       [
           "<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     : List[Any]	     =       tokenizer.batch_decode(A_       )
       self.assertEqual(A_	,							A_       )







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowerCamelCase	:					Optional[int]			  =					"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
    pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
    pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=None					)						->			Optional[Any]:
 require_version(deps[pkg]							,       _lowerCAmelCase					)
