


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 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=9_9  ,							SCREAMING_SNAKE_CASE__       : int=1_3  ,							SCREAMING_SNAKE_CASE__       : List[str]=7  ,							SCREAMING_SNAKE_CASE__       : List[str]=9  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : str=3_2  ,							SCREAMING_SNAKE_CASE__       : Any=5  ,							SCREAMING_SNAKE_CASE__       : Any=4  ,							SCREAMING_SNAKE_CASE__       : List[str]=3_7  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=8  ,							SCREAMING_SNAKE_CASE__       : int=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.002  ,							SCREAMING_SNAKE_CASE__       : Tuple=1  ,							SCREAMING_SNAKE_CASE__       : Tuple=0  ,							SCREAMING_SNAKE_CASE__       : int=0  ,							SCREAMING_SNAKE_CASE__       : Any=None  ,							SCREAMING_SNAKE_CASE__       : Dict=None  ,							)	->    List[str]:
             a_      :	Tuple							=						parent
             a_      :	Tuple							=						batch_size
             a_      :	Optional[int]							=						encoder_seq_length
             a_      :	str							=						decoder_seq_length
             # For common tests
             a_      :	Optional[Any]							=						self.decoder_seq_length
             a_      :	Union[str, Any]							=						is_training
             a_      :	Union[str, Any]							=						use_attention_mask
             a_      :	Tuple							=						use_labels
             a_      :	List[Any]							=						vocab_size
             a_      :	List[Any]							=						hidden_size
             a_      :	str							=						num_hidden_layers
             a_      :	Tuple							=						num_attention_heads
             a_      :	List[str]							=						d_ff
             a_      :	Dict							=						relative_attention_num_buckets
             a_      :	Dict							=						dropout_rate
             a_      :	Optional[int]							=						initializer_factor
             a_      :	Dict							=						eos_token_id
             a_      :	str							=						pad_token_id
             a_      :	int							=						decoder_start_token_id
             a_      :	Optional[Any]							=						None
             a_      :	int							=						decoder_layers
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Union[str, Any]:
             return TaConfig.from_pretrained('google/umt5-base'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							)	->    Optional[int]:
             if attention_mask is None:
                    a_      :	int							=						input_ids.ne(config.pad_token_id  )
             if decoder_attention_mask is None:
                    a_      :	Any							=						decoder_input_ids.ne(config.pad_token_id  )
             if head_mask is None:
                    a_      :	Dict							=						torch.ones(config.num_hidden_layers  ,							config.num_attention_heads  ,							device=SCREAMING_SNAKE_CASE__  )
             if decoder_head_mask is None:
                    a_      :	Optional[int]							=						torch.ones(config.num_decoder_layers  ,							config.num_attention_heads  ,							device=SCREAMING_SNAKE_CASE__  )
             if cross_attn_head_mask is None:
                    a_      :	str							=						torch.ones(
                        config.num_decoder_layers  ,							config.num_attention_heads  ,							device=SCREAMING_SNAKE_CASE__  )
             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  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	List[str]							=						ids_tensor([self.batch_size, self.encoder_seq_length]  ,							self.vocab_size  )
             a_      :	str							=						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
             a_      :	Dict							=						input_ids.clamp(self.pad_token_id + 1  )
             a_      :	Tuple							=						decoder_input_ids.clamp(self.pad_token_id + 1  )

             a_      :	Dict							=						self.get_config()
             a_      :	List[str]							=						config.num_attention_heads
             a_      :	Dict							=						self.prepare_inputs_dict(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             return config, input_dict
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             a_							,	a_      :	List[str]							=						self.prepare_config_and_inputs()
             return config, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Optional[Any]:
             return TaConfig(
                 vocab_size=1_6_6  ,							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  SCREAMING_SNAKE_CASE							( self       : int  )	->    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  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    str:
             a_      :	List[Any]							=						UMTaModel(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	int							=						model(
                 input_ids=SCREAMING_SNAKE_CASE__  ,							decoder_input_ids=SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						model(input_ids=SCREAMING_SNAKE_CASE__  ,							decoder_input_ids=SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						result.last_hidden_state
             a_      :	Dict							=						result.past_key_values
             a_      :	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(SCREAMING_SNAKE_CASE__  )  ,							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  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    int:
             a_      :	List[Any]							=						UMTaModel(config=SCREAMING_SNAKE_CASE__  ).get_decoder().to(SCREAMING_SNAKE_CASE__  ).eval()
             # first forward pass
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )

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

             a_							,	a_      :	int							=						outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             a_      :	List[str]							=						ids_tensor((self.batch_size, 1)  ,							config.vocab_size  )

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

             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )['last_hidden_state']

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

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

      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    Optional[Any]:
             a_      :	int							=						UMTaModel(config=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  ).half().eval()
             a_      :	Dict							=						model(**SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE__  ).any().item()  )
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		(
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      snake_case__       :  Union[str, Any]       =		(UMTaForConditionalGeneration,) if is_torch_available() else ()
      snake_case__       :  Optional[Any]       =		(
          {
              '''conversational''': UMTaForConditionalGeneration,
              '''feature-extraction''': UMTaModel,
              '''summarization''': UMTaForConditionalGeneration,
              '''text2text-generation''': UMTaForConditionalGeneration,
              '''translation''': UMTaForConditionalGeneration,
              '''question-answering''': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       :  str       =		True
      snake_case__       :  Dict       =		False
      snake_case__       :  Any       =		False
      snake_case__       :  Optional[Any]       =		True
      snake_case__       :  List[Any]       =		True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      snake_case__       :  Optional[Any]       =		[0.8, 0.9]
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             a_      :	Optional[int]							=						UMTaModelTester(self  )
      @unittest.skip('Test has a segmentation fault on torch 1.8.0'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Dict:
             a_      :	Tuple							=						self.model_tester.prepare_config_and_inputs()
             a_      :	Union[str, Any]							=						UMTaModel(config_and_inputs[0]  ).to(SCREAMING_SNAKE_CASE__  )
             with tempfile.TemporaryDirectory() as tmpdirname:
                    torch.onnx.export(
                        SCREAMING_SNAKE_CASE__  ,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,							F"""{tmpdirname}/t5_test.onnx"""  ,							export_params=SCREAMING_SNAKE_CASE__  ,							opset_version=9  ,							input_names=['input_ids', 'decoder_input_ids']  ,							)
      @unittest.skipIf(torch_device == 'cpu'  ,							'Cant do half precision'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[Any]:
             a_      :	List[Any]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             a_      :	Union[str, Any]							=						['encoder_attentions', 'decoder_attentions', 'cross_attentions']
             a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs()
             a_      :	Optional[int]							=						config_and_inputs[0]
             a_      :	Optional[Any]							=						UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE__  ).eval()
             model.to(SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						{
                 'head_mask': torch.zeros(config.num_layers  ,							config.num_heads  ,							device=SCREAMING_SNAKE_CASE__  ),
                 'decoder_head_mask': torch.zeros(config.num_decoder_layers  ,							config.num_heads  ,							device=SCREAMING_SNAKE_CASE__  ),
                 'cross_attn_head_mask': torch.zeros(config.num_decoder_layers  ,							config.num_heads  ,							device=SCREAMING_SNAKE_CASE__  ),
             }

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

                    a_      :	Any							=						model.generate(
                        config_and_inputs[1]['input_ids']  ,							num_beams=1  ,							max_length=3  ,							output_attentions=SCREAMING_SNAKE_CASE__  ,							return_dict_in_generate=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  ,							)
                    # We check the state of decoder_attentions and cross_attentions just from the last step
                    a_      :	List[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  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Tuple:
             pass
@require_torch
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       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  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    str:
             a_      :	Tuple							=						UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,							return_dict=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						AutoTokenizer.from_pretrained('google/umt5-small'  ,							use_fast=SCREAMING_SNAKE_CASE__  ,							legacy=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						[
                 '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>.',
             ]
             a_      :	Optional[Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ,							padding=SCREAMING_SNAKE_CASE__  ).input_ids
             # fmt: off
             a_      :	str							=						torch.tensor(
                 [
                     [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                     [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                     [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                     [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                     [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
                 ]  )
             # fmt: on
             torch.testing.assert_allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						model.generate(input_ids.to(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						[
                 '<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>',
             ]
             a_      :	str							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )
             self.assertEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )






UpperCAmelCase_   :				Optional[int]						      = '\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'

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


