





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			13
        SCREAMING_SNAKE_CASE__											=			7
        SCREAMING_SNAKE_CASE__											=			30
        SCREAMING_SNAKE_CASE__											=			self.seq_length + self.mem_len
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			99
        SCREAMING_SNAKE_CASE__											=			[10, 50, 80]
        SCREAMING_SNAKE_CASE__											=			32
        SCREAMING_SNAKE_CASE__											=			32
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			8
        SCREAMING_SNAKE_CASE__											=			1_28
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			self.vocab_size - 1
        SCREAMING_SNAKE_CASE__											=			0.01



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

        SCREAMING_SNAKE_CASE__											=			TransfoXLConfig(
            vocab_size=self.vocab_size   ,				mem_len=self.mem_len   ,				clamp_len=self.clamp_len   ,				cutoffs=self.cutoffs   ,				d_model=self.hidden_size   ,				d_embed=self.d_embed   ,				n_head=self.num_attention_heads   ,				d_head=self.d_head   ,				d_inner=self.d_inner   ,				div_val=self.div_val   ,				n_layer=self.num_hidden_layers   ,				eos_token_id=self.eos_token_id   ,				pad_token_id=self.vocab_size - 1   ,				init_range=self.init_range   ,				num_labels=self.num_labels   ,				)

        return (config, input_ids_a, input_ids_a, lm_labels)



    def  lowercase_					(       self     ):





        '''simple docstring'''

        random.seed(self.seed     )
        tf.random.set_seed(self.seed     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFTransfoXLModel(A_     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model(A_     ).to_tuple()

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids_a, '''mems''': mems_a}

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model(A_     ).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(hidden_states_a.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFTransfoXLLMHeadModel(A_     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model(A_     ).to_tuple()

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids_a, '''labels''': lm_labels}
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model(A_     ).to_tuple()

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model([input_ids_a, mems_a]     ).to_tuple()

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			model(A_     ).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)

        self.parent.assertEqual(lm_logits_a.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFTransfoXLForSequenceClassification(A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids_a}
        return config, inputs_dict





@require_tf
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    lowerCamelCase__    :		List[str]				=	() if is_tf_available() else ()
    lowerCamelCase__    :		List[Any]				=	(
        {
            """feature-extraction""": TFTransfoXLModel,
            """text-classification""": TFTransfoXLForSequenceClassification,
            """text-generation""": TFTransfoXLLMHeadModel,
            """zero-shot""": TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    lowerCamelCase__    :		Tuple				=	False
    lowerCamelCase__    :		Any				=	False
    lowerCamelCase__    :		Dict				=	False
    lowerCamelCase__    :		Optional[Any]				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if pipeline_test_casse_name == "TextGenerationPipelineTests":
            # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
            # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
            # tokenizer.
            return True

        return False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFTransfoXLModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				d_embed=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.model_tester.set_seed()
        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.model_tester.set_seed()
        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			[TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(A_     )
            assert isinstance(model.get_input_embeddings()   ,				tf.keras.layers.Layer     )
            if model_class in list_other_models_with_output_ebd:
                SCREAMING_SNAKE_CASE__											=			model.get_output_embeddings()
                assert isinstance(A_   ,				tf.keras.layers.Layer     )
                SCREAMING_SNAKE_CASE__											=			model.get_bias()
                assert name is None
            else:
                SCREAMING_SNAKE_CASE__											=			model.get_output_embeddings()
                assert x is None
                SCREAMING_SNAKE_CASE__											=			model.get_bias()
                assert name is None



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			TFTransfoXLModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )




    @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass





@require_tf
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @unittest.skip('''Skip test until #12651 is resolved.'''     )
    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''     )
        # fmt: off
        SCREAMING_SNAKE_CASE__											=			tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]   ,				dtype=tf.intaa     )  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        SCREAMING_SNAKE_CASE__											=			[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        SCREAMING_SNAKE_CASE__											=			model.generate(A_   ,				max_length=2_00   ,				do_sample=A_     )
        self.assertListEqual(output_ids[0].numpy().tolist()   ,				A_     )

'''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					:				List[str]  ,		__a					:				Any  ,		__a					:				int=9_9  ,		__a					:				Any=1_3  ,		__a					:				Tuple=7  ,		__a					:				Tuple=9  ,		__a					:				Tuple=True  ,		__a					:				Union[str, Any]=True  ,		__a					:				List[Any]=False  ,		__a					:				Optional[Any]=3_2  ,		__a					:				str=5  ,		__a					:				Optional[int]=4  ,		__a					:				Union[str, Any]=3_7  ,		__a					:				List[str]=8  ,		__a					:				Optional[int]=0.1  ,		__a					:				List[str]=0.0_0_2  ,		__a					:				List[Any]=1  ,		__a					:				str=0  ,		__a					:				Dict=0  ,		__a					:				int=None  ,		__a					:				List[Any]=None  ,		)				->						Tuple:
								__UpperCAmelCase      					=      parent
								__UpperCAmelCase      					=      batch_size
								__UpperCAmelCase      					=      encoder_seq_length
								__UpperCAmelCase      					=      decoder_seq_length
								# For common tests
								__UpperCAmelCase      					=      self.decoder_seq_length
								__UpperCAmelCase      					=      is_training
								__UpperCAmelCase      					=      use_attention_mask
								__UpperCAmelCase      					=      use_labels
								__UpperCAmelCase      					=      vocab_size
								__UpperCAmelCase      					=      hidden_size
								__UpperCAmelCase      					=      num_hidden_layers
								__UpperCAmelCase      					=      num_attention_heads
								__UpperCAmelCase      					=      d_ff
								__UpperCAmelCase      					=      relative_attention_num_buckets
								__UpperCAmelCase      					=      dropout_rate
								__UpperCAmelCase      					=      initializer_factor
								__UpperCAmelCase      					=      eos_token_id
								__UpperCAmelCase      					=      pad_token_id
								__UpperCAmelCase      					=      decoder_start_token_id
								__UpperCAmelCase      					=      None
								__UpperCAmelCase      					=      decoder_layers





							def    snake_case__    (   self					:				Union[str, Any]						)				->						int:
								return TaConfig.from_pretrained('''google/umt5-base'''						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				List[str]  ,		__a					:				str  ,		__a					:				Optional[int]  ,		__a					:				List[Any]=None  ,		__a					:				List[Any]=None  ,		__a					:				Any=None  ,		__a					:				str=None  ,		__a					:				Any=None  ,		)				->						List[Any]:
								if attention_mask is None:
									__UpperCAmelCase      					=      input_ids.ne(config.pad_token_id						)
								if decoder_attention_mask is None:
									__UpperCAmelCase      					=      decoder_input_ids.ne(config.pad_token_id						)
								if head_mask is None:
									__UpperCAmelCase      					=      torch.ones(config.num_hidden_layers  ,		config.num_attention_heads  ,		device=__a						)
								if decoder_head_mask is None:
									__UpperCAmelCase      					=      torch.ones(config.num_decoder_layers  ,		config.num_attention_heads  ,		device=__a						)
								if cross_attn_head_mask is None:
									__UpperCAmelCase      					=      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    snake_case__    (   self					:				List[str]						)				->						Dict:
								__UpperCAmelCase      					=      ids_tensor([self.batch_size, self.encoder_seq_length]  ,		self.vocab_size						)
								__UpperCAmelCase      					=      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      					=      input_ids.clamp(self.pad_token_id + 1						)
								__UpperCAmelCase      					=      decoder_input_ids.clamp(self.pad_token_id + 1						)

								__UpperCAmelCase      					=      self.get_config()
								__UpperCAmelCase      					=      config.num_attention_heads
								__UpperCAmelCase      					=      self.prepare_inputs_dict(__a  ,		__a  ,		__a						)
								return config, input_dict





							def    snake_case__    (   self					:				Union[str, Any]						)				->						Optional[Any]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.prepare_config_and_inputs()
								return config, inputs_dict





							def    snake_case__    (   self					:				int						)				->						Optional[int]:
								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    snake_case__    (   self					:				Optional[int]						)				->						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					:				int  ,		__a					:				Any  ,		__a					:				Union[str, Any]  ,		__a					:				List[Any]  ,		__a					:				Dict  ,		__a					:				Optional[Any]  ,		__a					:				int  ,		)				->						List[Any]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						)
								model.to(__a						)
								model.eval()
								__UpperCAmelCase      					=      model(
								    input_ids=__a  ,		decoder_input_ids=__a  ,		attention_mask=__a  ,		decoder_attention_mask=__a  ,		)
								__UpperCAmelCase      					=      model(input_ids=__a  ,		decoder_input_ids=__a						)
								__UpperCAmelCase      					=      result.last_hidden_state
								__UpperCAmelCase      					=      result.past_key_values
								__UpperCAmelCase      					=      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    snake_case__    (   self					:				List[str]  ,		__a					:				Any  ,		__a					:				Tuple  ,		__a					:				List[str]  ,		__a					:				Optional[Any]  ,		__a					:				Dict  ,		__a					:				Any  ,		)				->						Optional[Any]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						).get_decoder().to(__a						).eval()
								# first forward pass
								__UpperCAmelCase      					=      model(__a  ,		use_cache=__a						)
								__UpperCAmelCase      					=      model(__a						)
								__UpperCAmelCase      					=      model(__a  ,		use_cache=__a						)

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

								__UpperCAmelCase					,      __UpperCAmelCase      					=      outputs.to_tuple()

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

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

								__UpperCAmelCase      					=      model(__a						)['''last_hidden_state''']
								__UpperCAmelCase      					=      model(__a  ,		past_key_values=__a						)['''last_hidden_state''']

								# select random slice
								__UpperCAmelCase      					=      ids_tensor((1,)  ,		output_from_past.shape[-1]						).item()
								__UpperCAmelCase      					=      output_from_no_past[:, -1, random_slice_idx].detach()
								__UpperCAmelCase      					=      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    snake_case__    (   self					:				List[Any]  ,		__a					:				Union[str, Any]  ,		__a					:				Dict  ,		)				->						Optional[int]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						).to(__a						).half().eval()
								__UpperCAmelCase      					=      model(**__a						)['''last_hidden_state''']
								self.parent.assertFalse(torch.isnan(__a						).any().item()						)






@require_torch
class     A ( UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						unittest.TestCase ):
							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					:				Tuple						)				->						Optional[int]:
								__UpperCAmelCase      					=      UMTaModelTester(self						)





							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''						)
							def    snake_case__    (   self					:				str						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								__UpperCAmelCase      					=      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    snake_case__    (   self					:				Union[str, Any]						)				->						List[str]:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*__a						)





							def    snake_case__    (   self					:				List[Any]						)				->						str:
								__UpperCAmelCase      					=      ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								__UpperCAmelCase      					=      config_and_inputs[0]
								__UpperCAmelCase      					=      UMTaForConditionalGeneration(__a						).eval()
								model.to(__a						)

								__UpperCAmelCase      					=      {
								    '''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      					=      {name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
										__UpperCAmelCase      					=      torch.ones(
										    config.num_decoder_layers  ,		config.num_heads  ,		device=__a						)

									__UpperCAmelCase      					=      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      					=      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					:				Optional[int]						)				->						int:
								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    snake_case__    (   self					:				Any						)				->						int:
								__UpperCAmelCase      					=      UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''  ,		return_dict=__a						).to(__a						)
								__UpperCAmelCase      					=      AutoTokenizer.from_pretrained('''google/umt5-small'''  ,		use_fast=__a  ,		legacy=__a						)
								__UpperCAmelCase      					=      [
								    '''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      					=      tokenizer(__a  ,		return_tensors='''pt'''  ,		padding=__a						).input_ids
								# fmt: off
								__UpperCAmelCase      					=      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(__a  ,		__a						)

								__UpperCAmelCase      					=      model.generate(input_ids.to(__a						)						)
								__UpperCAmelCase      					=      [
								    '''<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      					=      tokenizer.batch_decode(__a						)
								self.assertEqual(__a  ,		__a						)
