"""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 						__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_0_2    ,					a__=1    ,					a__=0    ,					a__=0    ,					a__=None    ,					a__=None    ,					):
       _lowerCAmelCase						:	List[str]        =					parent
       _lowerCAmelCase						:	List[str]        =					batch_size
       _lowerCAmelCase						:	Optional[int]        =					encoder_seq_length
       _lowerCAmelCase						:	Optional[int]        =					decoder_seq_length
       # For common tests
       _lowerCAmelCase						:	List[str]        =					self.decoder_seq_length
       _lowerCAmelCase						:	List[Any]        =					is_training
       _lowerCAmelCase						:	str        =					use_attention_mask
       _lowerCAmelCase						:	Union[str, Any]        =					use_labels
       _lowerCAmelCase						:	int        =					vocab_size
       _lowerCAmelCase						:	str        =					hidden_size
       _lowerCAmelCase						:	Any        =					num_hidden_layers
       _lowerCAmelCase						:	str        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					d_ff
       _lowerCAmelCase						:	Union[str, Any]        =					relative_attention_num_buckets
       _lowerCAmelCase						:	Dict        =					dropout_rate
       _lowerCAmelCase						:	str        =					initializer_factor
       _lowerCAmelCase						:	List[str]        =					eos_token_id
       _lowerCAmelCase						:	Tuple        =					pad_token_id
       _lowerCAmelCase						:	Optional[Any]        =					decoder_start_token_id
       _lowerCAmelCase						:	Tuple        =					None
       _lowerCAmelCase						:	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:
         _lowerCAmelCase						:	Optional[int]        =					input_ids.ne(config.pad_token_id  )
       if decoder_attention_mask is None:
         _lowerCAmelCase						:	Tuple        =					decoder_input_ids.ne(config.pad_token_id  )
       if head_mask is None:
         _lowerCAmelCase						:	str        =					torch.ones(config.num_hidden_layers    ,					config.num_attention_heads    ,					device=_lowerCamelCase  )
       if decoder_head_mask is None:
         _lowerCAmelCase						:	Optional[int]        =					torch.ones(config.num_decoder_layers    ,					config.num_attention_heads    ,					device=_lowerCamelCase  )
       if cross_attn_head_mask is None:
         _lowerCAmelCase						:	List[str]        =					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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					ids_tensor([self.batch_size, self.encoder_seq_length]    ,					self.vocab_size  )
       _lowerCAmelCase						:	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
       _lowerCAmelCase						:	str        =					input_ids.clamp(self.pad_token_id + 1  )
       _lowerCAmelCase						:	Optional[Any]        =					decoder_input_ids.clamp(self.pad_token_id + 1  )

       _lowerCAmelCase						:	List[str]        =					self.get_config()
       _lowerCAmelCase						:	List[str]        =					config.num_attention_heads
       _lowerCAmelCase						:	List[Any]        =					self.prepare_inputs_dict(_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase  )
       return config, input_dict






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[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__    ,					):
       _lowerCAmelCase						:	Dict        =					UMTaModel(config=_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       _lowerCAmelCase						:	Optional[Any]        =					model(
           input_ids=_lowerCamelCase    ,					decoder_input_ids=_lowerCamelCase    ,					attention_mask=_lowerCamelCase    ,					decoder_attention_mask=_lowerCamelCase    ,					)
       _lowerCAmelCase						:	List[Any]        =					model(input_ids=_lowerCamelCase    ,					decoder_input_ids=_lowerCamelCase  )
       _lowerCAmelCase						:	List[str]        =					result.last_hidden_state
       _lowerCAmelCase						:	List[Any]        =					result.past_key_values
       _lowerCAmelCase						:	int        =					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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					):
       _lowerCAmelCase						:	List[str]        =					UMTaModel(config=_lowerCamelCase  ).get_decoder().to(_lowerCamelCase  ).eval()
       # first forward pass
       _lowerCAmelCase						:	Any        =					model(_lowerCamelCase    ,					use_cache=_lowerCamelCase  )
       _lowerCAmelCase						:	Optional[int]        =					model(_lowerCamelCase  )
       _lowerCAmelCase						:	Dict        =					model(_lowerCamelCase    ,					use_cache=_lowerCamelCase  )

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

       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					outputs.to_tuple()

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

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

       _lowerCAmelCase						:	Any        =					model(_lowerCamelCase  )["""last_hidden_state"""]
       _lowerCAmelCase						:	Optional[int]        =					model(_lowerCamelCase    ,					past_key_values=_lowerCamelCase  )["""last_hidden_state"""]

       # select random slice
       _lowerCAmelCase						:	Tuple        =					ids_tensor((1,)    ,					output_from_past.shape[-1]  ).item()
       _lowerCAmelCase						:	Optional[Any]        =					output_from_no_past[:, -1, random_slice_idx].detach()
       _lowerCAmelCase						:	List[Any]        =					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    __A  (		self    ,					a__    ,					a__    ,					):
       _lowerCAmelCase						:	Tuple        =					UMTaModel(config=_lowerCamelCase  ).to(_lowerCamelCase  ).half().eval()
       _lowerCAmelCase						:	int        =					model(**_lowerCamelCase  )["""last_hidden_state"""]
       self.parent.assertFalse(torch.isnan(_lowerCamelCase  ).any().item()  )




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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					UMTaModelTester(self  )






     @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs()
       _lowerCAmelCase						:	List[Any]        =					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    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_lowerCamelCase  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       _lowerCAmelCase						:	str        =					config_and_inputs[0]
       _lowerCAmelCase						:	Union[str, Any]        =					UMTaForConditionalGeneration(_lowerCamelCase  ).eval()
       model.to(_lowerCamelCase  )

       _lowerCAmelCase						:	Union[str, Any]        =					{
           """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						:	int        =					{name: mask}
         # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
         if name == "head_mask":
           _lowerCAmelCase						:	str        =					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						:	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 						__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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,					return_dict=_lowerCamelCase  ).to(_lowerCamelCase  )
       _lowerCAmelCase						:	str        =					AutoTokenizer.from_pretrained("""google/umt5-small"""    ,					use_fast=_lowerCamelCase    ,					legacy=_lowerCamelCase  )
       _lowerCAmelCase						:	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>.""",
       ]
       _lowerCAmelCase						:	Optional[Any]        =					tokenizer(_lowerCamelCase    ,					return_tensors="""pt"""    ,					padding=_lowerCamelCase  ).input_ids
       # fmt: off
       _lowerCAmelCase						:	str        =					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(_lowerCamelCase    ,					_lowerCamelCase  )

       _lowerCAmelCase						:	List[Any]        =					model.generate(input_ids.to(_lowerCamelCase  )  )
       _lowerCAmelCase						:	List[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>""",
       ]
       _lowerCAmelCase						:	int        =					tokenizer.batch_decode(_lowerCamelCase  )
       self.assertEqual(_lowerCamelCase    ,					_lowerCamelCase  )





"""simple docstring"""




_a				:		Any	   =       '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a				:		List[Any]	   =       [{'type': 'code', 'content': INSTALL_CONTENT}]
_a				:		Union[str, Any]	   =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




