






"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				UpperCamelCase_	:				Tuple										= """platform"""
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class      _lowercase :
						_a   :     str								=   PegasusConfig
						_a   :     List[Any]								=   {}
						_a   :     str								=   '''gelu'''


						def __init__(    self				:	Optional[int]							,   a				:	Tuple							,   a				:	List[Any]=1_3							,   a				:	Tuple=7							,   a				:	List[str]=True							,   a				:	Any=False							,   a				:	List[str]=9_9							,   a				:	int=3_2							,   a				:	Tuple=5							,   a				:	Optional[Any]=4							,   a				:	Optional[int]=3_7							,   a				:	Dict=0.1							,   a				:	Optional[Any]=0.1							,   a				:	Dict=2_0							,   a				:	List[str]=2							,   a				:	Tuple=1							,   a				:	int=0							,   ):





												"""simple docstring"""







												__snake_case      :    Optional[Any]  			=parent
												__snake_case      :    List[str]  			=batch_size
												__snake_case      :    Dict  			=seq_length
												__snake_case      :    Optional[int]  			=is_training
												__snake_case      :    List[Any]  			=use_labels
												__snake_case      :    Optional[Any]  			=vocab_size
												__snake_case      :    int  			=hidden_size
												__snake_case      :    Optional[Any]  			=num_hidden_layers
												__snake_case      :    Any  			=num_attention_heads
												__snake_case      :    Tuple  			=intermediate_size

												__snake_case      :    Union[str, Any]  			=hidden_dropout_prob
												__snake_case      :    str  			=attention_probs_dropout_prob
												__snake_case      :    Optional[Any]  			=max_position_embeddings
												__snake_case      :    Any  			=eos_token_id
												__snake_case      :    List[Any]  			=pad_token_id
												__snake_case      :    List[str]  			=bos_token_id


						def _UpperCamelCase						(    self				:	Dict						):





												"""simple docstring"""







												__snake_case      :    int  			=ids_tensor([self.batch_size, self.seq_length - 1]							,   self.vocab_size						).clip(3							,   self.vocab_size						)
												__snake_case      :    Union[str, Any]  			=np.expand_dims(np.array([self.eos_token_id] * self.batch_size						)							,   1						)
												__snake_case      :    List[Any]  			=np.concatenate([input_ids, eos_tensor]							,   axis=1						)

												__snake_case      :    str  			=ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

												__snake_case      :    Tuple  			=self.config_cls(
												    vocab_size=self.vocab_size							,   d_model=self.hidden_size							,   encoder_layers=self.num_hidden_layers							,   decoder_layers=self.num_hidden_layers							,   encoder_attention_heads=self.num_attention_heads							,   decoder_attention_heads=self.num_attention_heads							,   encoder_ffn_dim=self.intermediate_size							,   decoder_ffn_dim=self.intermediate_size							,   dropout=self.hidden_dropout_prob							,   attention_dropout=self.attention_probs_dropout_prob							,   max_position_embeddings=self.max_position_embeddings							,   eos_token_ids=[2]							,   bos_token_id=self.bos_token_id							,   pad_token_id=self.pad_token_id							,   decoder_start_token_id=self.pad_token_id							,   **self.config_updates							,   )
												__snake_case      :    List[Any]  			=prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)
												return config, inputs_dict


						def _UpperCamelCase						(    self				:	List[Any]							,   a				:	List[str]							,   a				:	Any							,   a				:	List[str]						):





												"""simple docstring"""







												__snake_case      :    Optional[Any]  			=2_0
												__snake_case      :    str  			=model_class_name(SCREAMING_SNAKE_CASE_						)

												__snake_case      :    Optional[int]  			=model.encode(inputs_dict['''input_ids''']						)

												__snake_case			,				__snake_case      :    Optional[int]  			=(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												__snake_case      :    Any  			=model.init_cache(decoder_input_ids.shape[0]							,   SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)
												__snake_case      :    Optional[Any]  			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,   dtype='''i4'''						)

												__snake_case      :    Tuple  			=jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,   )
												__snake_case      :    Optional[Any]  			=model.decode(
												    decoder_input_ids[:, :-1]							,   SCREAMING_SNAKE_CASE_							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_							,   past_key_values=SCREAMING_SNAKE_CASE_							,   decoder_position_ids=SCREAMING_SNAKE_CASE_							,   )

												__snake_case      :    Any  			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,   dtype='''i4'''						)
												__snake_case      :    int  			=model.decode(
												    decoder_input_ids[:, -1:]							,   SCREAMING_SNAKE_CASE_							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_							,   past_key_values=outputs_cache.past_key_values							,   decoder_position_ids=SCREAMING_SNAKE_CASE_							,   )

												__snake_case      :    Dict  			=model.decode(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)

												__snake_case      :    str  			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
												self.parent.assertTrue(diff < 1e-3							,   msg=f'''Max diff is {diff}'''						)







						def _UpperCamelCase						(    self				:	Dict							,   a				:	Dict							,   a				:	Union[str, Any]							,   a				:	Dict						):





												"""simple docstring"""







												__snake_case      :    int  			=2_0
												__snake_case      :    List[str]  			=model_class_name(SCREAMING_SNAKE_CASE_						)

												__snake_case      :    List[Any]  			=model.encode(inputs_dict['''input_ids''']						)

												__snake_case			,				__snake_case      :    Any  			=(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												__snake_case      :    int  			=jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
												    ]							,   axis=-1							,   )

												__snake_case      :    Tuple  			=model.init_cache(decoder_input_ids.shape[0]							,   SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)
												__snake_case      :    Dict  			=jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,   )

												__snake_case      :    Dict  			=model.decode(
												    decoder_input_ids[:, :-1]							,   SCREAMING_SNAKE_CASE_							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_							,   past_key_values=SCREAMING_SNAKE_CASE_							,   decoder_position_ids=SCREAMING_SNAKE_CASE_							,   )
												__snake_case      :    str  			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,   dtype='''i4'''						)
												__snake_case      :    Optional[Any]  			=model.decode(
												    decoder_input_ids[:, -1:]							,   SCREAMING_SNAKE_CASE_							,   past_key_values=outputs_cache.past_key_values							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_							,   decoder_position_ids=SCREAMING_SNAKE_CASE_							,   )

												__snake_case      :    Dict  			=model.decode(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_						)

												__snake_case      :    Union[str, Any]  			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
												self.parent.assertTrue(diff < 1e-3							,   msg=f'''Max diff is {diff}'''						)



def     __lowercase					(    a :				Optional[Any]       ,		a :				Dict       ,		a :				Tuple       ,		a :				Any=None       ,		a :				int=None       ,		)					->					Optional[int]:




						if attention_mask is None:
												__snake_case      :    List[str]  			=np.not_equal(__a       ,		config.pad_token_id ).astype(np.inta )
						if decoder_attention_mask is None:
												__snake_case      :    Optional[Any]  			=np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape       ,		dtype=np.inta ),
												        np.not_equal(decoder_input_ids[:, 1:]       ,		config.pad_token_id ).astype(np.inta ),
												    ]       ,		axis=-1       ,		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}


@require_flax
class      _lowercase (     __lowerCamelCase		,  unittest.TestCase						):
						_a   :     Optional[Any]								=   (
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						_a   :     Union[str, Any]								=   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						_a   :     Union[str, Any]								=   True
						_a   :     List[str]								=   False
						_a   :     Optional[Any]								=   False
						_a   :     Tuple								=   False


						def _UpperCamelCase						(    self				:	str						):





												"""simple docstring"""







												__snake_case      :    Union[str, Any]  			=FlaxPegasusModelTester(self						)
												__snake_case      :    Tuple  			=ConfigTester(self							,   config_class=SCREAMING_SNAKE_CASE_						)


						def _UpperCamelCase						(    self				:	Any						):





												"""simple docstring"""







												self.config_tester.run_common_tests()


						def _UpperCamelCase						(    self				:	Any						):





												"""simple docstring"""







												__snake_case			,				__snake_case      :    str  			=self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)


						def _UpperCamelCase						(    self				:	Dict						):





												"""simple docstring"""







												__snake_case			,				__snake_case      :    Optional[Any]  			=self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)


						def _UpperCamelCase						(    self				:	Any						):





												"""simple docstring"""







												__snake_case			,				__snake_case      :    Any  			=self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__						):
																								__snake_case      :    Any  			=self._prepare_for_class(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						)
																								__snake_case      :    str  			=model_class(SCREAMING_SNAKE_CASE_						)

																								@jax.jit
																								def encode_jitted(a				:	Tuple							,   a				:	Union[str, Any]=None							,   **a				:	Tuple						):
																														return model.encode(input_ids=SCREAMING_SNAKE_CASE_							,   attention_mask=SCREAMING_SNAKE_CASE_						)

																								with self.subTest('''JIT Enabled'''						):
																														__snake_case      :    int  			=encode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																								with self.subTest('''JIT Disabled'''						):
																														with jax.disable_jit():
																																				__snake_case      :    Union[str, Any]  			=encode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																								self.assertEqual(len(SCREAMING_SNAKE_CASE_						)							,   len(SCREAMING_SNAKE_CASE_						)						)
																								for jitted_output, output in zip(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						):
																														self.assertEqual(jitted_output.shape							,   output.shape						)


						def _UpperCamelCase						(    self				:	Optional[int]						):





												"""simple docstring"""







												__snake_case			,				__snake_case      :    Tuple  			=self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__						):
																								__snake_case      :    int  			=model_class(SCREAMING_SNAKE_CASE_						)
																								__snake_case      :    int  			=model.encode(inputs_dict['''input_ids''']							,   inputs_dict['''attention_mask''']						)

																								__snake_case      :    Union[str, Any]  			={
																								    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																								    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																								    '''encoder_outputs''': encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(a				:	Dict							,   a				:	Tuple							,   a				:	Union[str, Any]						):
																														return model.decode(
																														    decoder_input_ids=SCREAMING_SNAKE_CASE_							,   decoder_attention_mask=SCREAMING_SNAKE_CASE_							,   encoder_outputs=SCREAMING_SNAKE_CASE_							,   )

																								with self.subTest('''JIT Enabled'''						):
																														__snake_case      :    List[Any]  			=decode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																								with self.subTest('''JIT Disabled'''						):
																														with jax.disable_jit():
																																				__snake_case      :    Union[str, Any]  			=decode_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

																								self.assertEqual(len(SCREAMING_SNAKE_CASE_						)							,   len(SCREAMING_SNAKE_CASE_						)						)
																								for jitted_output, output in zip(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_						):
																														self.assertEqual(jitted_output.shape							,   output.shape						)


						@slow
						def _UpperCamelCase						(    self				:	Tuple						):





												"""simple docstring"""







												for model_class_name in self.all_model_classes:
																		__snake_case      :    str  			=model_class_name.from_pretrained('''google/pegasus-large'''							,   from_pt=SCREAMING_SNAKE_CASE_						)
																		__snake_case      :    Dict  			=np.ones((1, 1)						)
																		__snake_case      :    int  			=model(SCREAMING_SNAKE_CASE_						)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







						@slow
						def _UpperCamelCase						(    self				:	List[str]						):





												"""simple docstring"""







												__snake_case      :    Tuple  			=FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''						)
												__snake_case      :    Any  			=PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''						)

												__snake_case      :    List[Any]  			=[
												    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
												    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ''',
												]

												__snake_case      :    List[Any]  			=[
												    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
												    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
												]

												__snake_case      :    Dict  			=tokenizer(SCREAMING_SNAKE_CASE_							,   return_tensors='''np'''							,   truncation=SCREAMING_SNAKE_CASE_							,   max_length=5_1_2							,   padding=SCREAMING_SNAKE_CASE_						)
												__snake_case      :    Optional[int]  			=model.generate(**SCREAMING_SNAKE_CASE_							,   num_beams=2						).sequences
												__snake_case      :    Union[str, Any]  			=tokenizer.batch_decode(SCREAMING_SNAKE_CASE_							,   skip_special_tokens=SCREAMING_SNAKE_CASE_						)
												assert tgt_text == decoded










"""simple docstring"""





import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class      _lowercase :


      def __init__(    self				:	str							,   a				:	List[str]							,   a				:	Any=2							,   a				:	int=8							,   a				:	List[str]=True							,   a				:	Union[str, Any]=True							,   a				:	Union[str, Any]=True							,   a				:	Optional[int]=True							,   a				:	Union[str, Any]=9_9							,   a				:	Dict=1_6							,   a				:	Union[str, Any]=5							,   a				:	int=2							,   a				:	List[str]=3_6							,   a				:	int="gelu"							,   a				:	Optional[int]=0.0							,   a				:	Any=0.0							,   a				:	Optional[Any]=5_1_2							,   a				:	Tuple=1_6							,   a				:	Dict=2							,   a				:	Union[str, Any]=0.0_2							,   a				:	Dict=3							,   a				:	Union[str, Any]=4							,   a				:	Optional[int]=None							,   ):





            """simple docstring"""







            __snake_case      :    Any  			=parent
            __snake_case      :    int  			=batch_size
            __snake_case      :    Dict  			=seq_length
            __snake_case      :    Any  			=is_training
            __snake_case      :    Optional[int]  			=use_input_mask
            __snake_case      :    List[Any]  			=use_token_type_ids
            __snake_case      :    List[str]  			=use_labels
            __snake_case      :    Optional[Any]  			=vocab_size
            __snake_case      :    Optional[Any]  			=hidden_size
            __snake_case      :    Optional[Any]  			=num_hidden_layers
            __snake_case      :    Any  			=num_attention_heads
            __snake_case      :    Optional[int]  			=intermediate_size
            __snake_case      :    Dict  			=hidden_act
            __snake_case      :    List[str]  			=hidden_dropout_prob
            __snake_case      :    List[Any]  			=attention_probs_dropout_prob
            __snake_case      :    List[str]  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Tuple  			=type_sequence_label_size
            __snake_case      :    int  			=initializer_range
            __snake_case      :    Any  			=num_labels
            __snake_case      :    List[str]  			=num_choices
            __snake_case      :    Union[str, Any]  			=scope


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

            __snake_case      :    int  			=None
            if self.use_input_mask:
                  __snake_case      :    Optional[int]  			=random_attention_mask([self.batch_size, self.seq_length]						)

            __snake_case      :    Optional[Any]  			=None
            if self.use_token_type_ids:
                  __snake_case      :    Optional[int]  			=ids_tensor([self.batch_size, self.seq_length]							,   self.type_vocab_size						)

            __snake_case      :    str  			=None
            __snake_case      :    int  			=None
            __snake_case      :    int  			=None
            if self.use_labels:
                  __snake_case      :    int  			=ids_tensor([self.batch_size]							,   self.type_sequence_label_size						)
                  __snake_case      :    str  			=ids_tensor([self.batch_size, self.seq_length]							,   self.num_labels						)
                  __snake_case      :    Optional[Any]  			=ids_tensor([self.batch_size]							,   self.num_choices						)

            __snake_case      :    List[str]  			=self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return MraConfig(
                vocab_size=self.vocab_size							,   hidden_size=self.hidden_size							,   num_hidden_layers=self.num_hidden_layers							,   num_attention_heads=self.num_attention_heads							,   intermediate_size=self.intermediate_size							,   hidden_act=self.hidden_act							,   hidden_dropout_prob=self.hidden_dropout_prob							,   attention_probs_dropout_prob=self.attention_probs_dropout_prob							,   max_position_embeddings=self.max_position_embeddings							,   type_vocab_size=self.type_vocab_size							,   is_decoder=a							,   initializer_range=self.initializer_range							,   )


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    int  			=self.get_config()
            __snake_case      :    Optional[Any]  			=3_0_0
            return config


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Tuple  			=self.prepare_config_and_inputs()

            __snake_case      :    Dict  			=True
            __snake_case      :    Any  			=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
            __snake_case      :    Dict  			=ids_tensor([self.batch_size, self.seq_length]							,   vocab_size=2						)

            return (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	List[Any]							,   a				:	int							,   a				:	Optional[int]							,   a				:	List[Any]							,   a				:	Tuple							,   a				:	Tuple							,   a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=MraModel(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Union[str, Any]  			=model(a							,   attention_mask=a							,   token_type_ids=a						)
            __snake_case      :    Any  			=model(a							,   token_type_ids=a						)
            __snake_case      :    int  			=model(a						)
            self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)						)


      def _UpperCamelCase						(    self				:	str							,   a				:	Tuple							,   a				:	Any							,   a				:	int							,   a				:	Optional[int]							,   a				:	Union[str, Any]							,   a				:	int							,   a				:	Tuple							,   a				:	Optional[Any]							,   a				:	Optional[int]							,   ):





            """simple docstring"""







            __snake_case      :    Any  			=True
            __snake_case      :    Union[str, Any]  			=MraModel(a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Union[str, Any]  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   )
            __snake_case      :    List[Any]  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   encoder_hidden_states=a							,   )
            __snake_case      :    List[str]  			=model(a							,   attention_mask=a							,   token_type_ids=a						)
            self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)						)


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	Any							,   a				:	List[Any]							,   a				:	int							,   a				:	str							,   a				:	Optional[Any]							,   a				:	List[str]							,   a				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=MraForMaskedLM(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    str  			=model(a							,   attention_mask=a							,   token_type_ids=a							,   labels=a						)
            self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.vocab_size)						)


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Optional[int]							,   a				:	int							,   a				:	List[str]							,   a				:	int							,   a				:	Any							,   a				:	Union[str, Any]							,   a				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=MraForQuestionAnswering(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Dict  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   start_positions=a							,   end_positions=a							,   )
            self.parent.assertEqual(result.start_logits.shape							,   (self.batch_size, self.seq_length)						)
            self.parent.assertEqual(result.end_logits.shape							,   (self.batch_size, self.seq_length)						)


      def _UpperCamelCase						(    self				:	Dict							,   a				:	Dict							,   a				:	Dict							,   a				:	Optional[Any]							,   a				:	int							,   a				:	int							,   a				:	List[str]							,   a				:	Any						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.num_labels
            __snake_case      :    Optional[Any]  			=MraForSequenceClassification(a						)
            model.to(a						)
            model.eval()
            __snake_case      :    str  			=model(a							,   attention_mask=a							,   token_type_ids=a							,   labels=a						)
            self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Tuple							,   a				:	Any							,   a				:	Optional[int]							,   a				:	Any							,   a				:	int							,   a				:	str							,   a				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.num_labels
            __snake_case      :    Optional[int]  			=MraForTokenClassification(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    List[str]  			=model(a							,   attention_mask=a							,   token_type_ids=a							,   labels=a						)
            self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.num_labels)						)


      def _UpperCamelCase						(    self				:	str							,   a				:	Tuple							,   a				:	Tuple							,   a				:	Optional[Any]							,   a				:	Dict							,   a				:	Dict							,   a				:	Dict							,   a				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.num_choices
            __snake_case      :    Optional[int]  			=MraForMultipleChoice(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    List[str]  			=input_ids.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    Tuple  			=token_type_ids.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    Any  			=input_mask.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    int  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   labels=a							,   )
            self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_choices)						)







      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Optional[int]  			=config_and_inputs
            __snake_case      :    Tuple  			={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict



@require_torch
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Union[str, Any]								=   (
          (
              MraModel,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _a   :     int								=   False
      _a   :     Union[str, Any]								=   False
      _a   :     Dict								=   False
      _a   :     Dict								=   False

      _a   :     Any								=   ()


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=MraModelTester(self						)
            __snake_case      :    List[Any]  			=ConfigTester(self							,   config_class=a							,   hidden_size=3_7						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            self.config_tester.run_common_tests()


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  __snake_case      :    Any  			=type
                  self.model_tester.create_and_check_model(*a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*a						)


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    str  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*a						)


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*a						)


      @slow
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __snake_case      :    Tuple  			=MraModel.from_pretrained(a						)
                  self.assertIsNotNone(a						)







      @unittest.skip(reason='''MRA does not output attentions'''						)
      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            return



@require_torch
class      _lowercase (     unittest.TestCase						):


      @slow
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    str  			=MraModel.from_pretrained('''uw-madison/mra-base-512-4'''						)
            __snake_case      :    Optional[int]  			=torch.arange(2_5_6						).unsqueeze(0						)

            with torch.no_grad():
                  __snake_case      :    List[str]  			=model(a						)[0]

            __snake_case      :    Any  			=torch.Size((1, 2_5_6, 7_6_8)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    int  			=torch.tensor(
                [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]						)

            self.assertTrue(torch.allclose(output[:, :3, :3]							,   a							,   atol=1e-4						)						)


      @slow
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Any  			=MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''						)
            __snake_case      :    Union[str, Any]  			=torch.arange(2_5_6						).unsqueeze(0						)

            with torch.no_grad():
                  __snake_case      :    Optional[int]  			=model(a						)[0]

            __snake_case      :    Union[str, Any]  			=5_0_2_6_5

            __snake_case      :    List[str]  			=torch.Size((1, 2_5_6, vocab_size)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    str  			=torch.tensor(
                [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]						)

            self.assertTrue(torch.allclose(output[:, :3, :3]							,   a							,   atol=1e-4						)						)







      @slow
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''						)
            __snake_case      :    Optional[int]  			=torch.arange(4_0_9_6						).unsqueeze(0						)

            with torch.no_grad():
                  __snake_case      :    Tuple  			=model(a						)[0]

            __snake_case      :    Optional[int]  			=5_0_2_6_5

            __snake_case      :    Tuple  			=torch.Size((1, 4_0_9_6, vocab_size)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    List[str]  			=torch.tensor(
                [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]						)

            self.assertTrue(torch.allclose(output[:, :3, :3]							,   a							,   atol=1e-4						)						)


