




'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
						_lowerCamelCase							:						str								=  'platform'

						import jax
						import jax.numpy as jnp

						from transformers import AutoTokenizer
						from transformers.models.mbart.modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    shift_tokens_right,
						)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							lowercase_=None						,							):



		'''simple docstring'''







		if attention_mask is None:
				_snake_case	       =       np.where(input_ids != config.pad_token_id						,							1						,							0  )
		if decoder_attention_mask is None:
				_snake_case	       =       np.where(decoder_input_ids != config.pad_token_id						,							1						,							0  )
		if head_mask is None:
				_snake_case	       =       np.ones((config.encoder_layers, config.encoder_attention_heads)  )
		if decoder_head_mask is None:
				_snake_case	       =       np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		if cross_attn_head_mask is None:
				_snake_case	       =       np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		}





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=13,  __UpperCamelCase=7,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=99,  __UpperCamelCase=16,  __UpperCamelCase=2,  __UpperCamelCase=4,  __UpperCamelCase=4,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=32,  __UpperCamelCase=2,  __UpperCamelCase=1,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0.02,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       seq_length
							_snake_case	       =       is_training
							_snake_case	       =       use_labels
							_snake_case	       =       vocab_size
							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       num_attention_heads
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       max_position_embeddings
							_snake_case	       =       eos_token_id
							_snake_case	       =       pad_token_id
							_snake_case	       =       bos_token_id
							_snake_case	       =       decoder_start_token_id
							_snake_case	       =       initializer_range






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size ),  3,  self.vocab_size )
							_snake_case	       =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),  dtype=np.intaa )),  -1 )

							_snake_case	       =       shift_tokens_right(__UpperCamelCase,  1 )

							_snake_case	       =       MBartConfig(
							    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_id=self.eos_token_id,  bos_token_id=self.bos_token_id,  pad_token_id=self.pad_token_id,  decoder_start_token_id=self.decoder_start_token_id,  initializer_range=self.initializer_range,  use_cache=__UpperCamelCase,  )
							_snake_case	       =       prepare_mbart_inputs_dict(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
							return config, inputs_dict






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.prepare_config_and_inputs()
							return config, inputs_dict






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(__UpperCamelCase )

							_snake_case	       =       model.encode(inputs_dict["input_ids"] )

							_snake_case       ,    _snake_case	       =       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							_snake_case	       =       model.init_cache(decoder_input_ids.shape[0],  __UpperCamelCase,  __UpperCamelCase )
							_snake_case	       =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length),  dtype="i4" )

							_snake_case	       =       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	       =       model.decode(
							    decoder_input_ids[:, :-1],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype="i4" )
							_snake_case	       =       model.decode(
							    decoder_input_ids[:, -1:],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       model.decode(__UpperCamelCase,  __UpperCamelCase )

							_snake_case	       =       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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(__UpperCamelCase )

							_snake_case	       =       model.encode(inputs_dict["input_ids"] )

							_snake_case       ,    _snake_case	       =       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

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

							_snake_case	       =       model.init_cache(decoder_input_ids.shape[0],  __UpperCamelCase,  __UpperCamelCase )
							_snake_case	       =       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	       =       model.decode(
							    decoder_input_ids[:, :-1],  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )
							_snake_case	       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype="i4" )
							_snake_case	       =       model.decode(
							    decoder_input_ids[:, -1:],  __UpperCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_attention_mask=__UpperCamelCase,  decoder_position_ids=__UpperCamelCase,  )

							_snake_case	       =       model.decode(__UpperCamelCase,  __UpperCamelCase,  decoder_attention_mask=__UpperCamelCase )

							_snake_case	       =       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}''' )





@require_flax
class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = 9_9






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ],  dtype=np.intaa,  )

							_snake_case	       =       input_ids.shape[0]
							_snake_case	       =       MBartConfig(
							    vocab_size=self.vocab_size,  d_model=24,  encoder_layers=2,  decoder_layers=2,  encoder_attention_heads=2,  decoder_attention_heads=2,  encoder_ffn_dim=32,  decoder_ffn_dim=32,  max_position_embeddings=48,  eos_token_id=2,  pad_token_id=1,  bos_token_id=0,  )
							return config, input_ids, batch_size






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForSequenceClassification(__UpperCamelCase )
							_snake_case	       =       model(input_ids=__UpperCamelCase,  decoder_input_ids=__UpperCamelCase )
							_snake_case	       =       (batch_size, config.num_labels)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForQuestionAnswering(__UpperCamelCase )
							_snake_case	       =       model(input_ids=__UpperCamelCase )

							self.assertEqual(outputs["start_logits"].shape,  input_ids.shape )
							self.assertEqual(outputs["end_logits"].shape,  input_ids.shape )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._get_config_and_data()
							_snake_case	       =       FlaxMBartForConditionalGeneration(__UpperCamelCase )
							_snake_case	       =       lm_model(input_ids=__UpperCamelCase )
							_snake_case	       =       (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       MBartConfig(
							    vocab_size=self.vocab_size,  d_model=14,  encoder_layers=2,  decoder_layers=2,  encoder_attention_heads=2,  decoder_attention_heads=2,  encoder_ffn_dim=8,  decoder_ffn_dim=8,  max_position_embeddings=48,  )
							_snake_case	       =       FlaxMBartForConditionalGeneration(__UpperCamelCase )
							_snake_case	       =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],  dtype=np.intaa )
							_snake_case	       =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],  dtype=np.intaa )
							_snake_case	       =       lm_model(input_ids=__UpperCamelCase,  decoder_input_ids=__UpperCamelCase )
							_snake_case	       =       (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa )
							_snake_case	       =       shift_tokens_right(__UpperCamelCase,  1 )
							_snake_case	       =       np.equal(__UpperCamelCase,  1 ).astype(np.floataa ).sum()
							_snake_case	       =       np.equal(__UpperCamelCase,  1 ).astype(np.floataa ).sum()
							self.assertEqual(shifted.shape,  input_ids.shape )
							self.assertEqual(__UpperCamelCase,  n_pad_before - 1 )
							self.assertTrue(np.equal(shifted[:, 0],  2 ).all() )





@require_flax
class      lowerCamelCase_							(       UpperCAmelCase_,       unittest.TestCase,       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = True
					lowercase__    = (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase__    = (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       FlaxMBartModelTester(self )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       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	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase )
											_snake_case	       =       model_class(__UpperCamelCase )

											@jax.jit
											def encode_jitted(__UpperCamelCase,  __UpperCamelCase=None,  **__UpperCamelCase ):
													return model.encode(input_ids=__UpperCamelCase,  attention_mask=__UpperCamelCase )

											with self.subTest("JIT Enabled" ):
													_snake_case	       =       encode_jitted(**__UpperCamelCase ).to_tuple()

											with self.subTest("JIT Disabled" ):
													with jax.disable_jit():
															_snake_case	       =       encode_jitted(**__UpperCamelCase ).to_tuple()

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       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	       =       model_class(__UpperCamelCase )
											_snake_case	       =       model.encode(inputs_dict["input_ids"],  inputs_dict["attention_mask"] )

											_snake_case	       =       {
											    "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(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
													return model.decode(
													    decoder_input_ids=__UpperCamelCase,  decoder_attention_mask=__UpperCamelCase,  encoder_outputs=__UpperCamelCase,  )

											with self.subTest("JIT Enabled" ):
													_snake_case	       =       decode_jitted(**__UpperCamelCase ).to_tuple()

											with self.subTest("JIT Disabled" ):
													with jax.disable_jit():
															_snake_case	       =       decode_jitted(**__UpperCamelCase ).to_tuple()

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






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for model_class_name in self.all_model_classes:
									_snake_case	       =       model_class_name.from_pretrained("facebook/mbart-large-cc25",  from_pt=__UpperCamelCase )
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									_snake_case	       =       np.ones((1, 1) ) * model.config.eos_token_id
									_snake_case	       =       model(__UpperCamelCase )
									self.assertIsNotNone(__UpperCamelCase )





@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = [
					    """ UN Chief Says There Is No Military Solution in Syria""",
					]
					lowercase__    = [
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					]
					lowercase__    = """facebook/mbart-large-en-ro"""






					@cached_property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return AutoTokenizer.from_pretrained(self.model_name )






					@cached_property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,  from_pt=__UpperCamelCase )
							return model






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       self.translate_src_text(**__UpperCamelCase )
							self.assertListEqual(self.expected_text,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       self.tokenizer(self.src_text,  **__UpperCamelCase,  return_tensors="np" )
							_snake_case	       =       self.model.generate(
							    model_inputs.input_ids,  attention_mask=model_inputs.attention_mask,  decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],  early_stopping=__UpperCamelCase,  num_beams=2,  ).sequences
							_snake_case	       =       self.tokenizer.batch_decode(__UpperCamelCase,  skip_special_tokens=__UpperCamelCase )
							return generated_words






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self._assert_generated_batch_equal_expected()







from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.mpnet.modeling_tf_mpnet import (
       TFMPNetForMaskedLM,
       TFMPNetForMultipleChoice,
       TFMPNetForQuestionAnswering,
       TFMPNetForSequenceClassification,
       TFMPNetForTokenClassification,
       TFMPNetModel,
   )







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    int				,    _A      :    Tuple=1_3				,    _A      :    List[str]=7				,    _A      :    Any=True				,    _A      :    Union[str, Any]=True				,    _A      :    Optional[Any]=False				,    _A      :    Tuple=True				,    _A      :    str=9_9				,    _A      :    Optional[Any]=6_4				,    _A      :    Optional[int]=2				,    _A      :    Union[str, Any]=4				,    _A      :    Any=6_4				,    _A      :    Any="gelu"				,    _A      :    str=0.1				,    _A      :    Union[str, Any]=0.1				,    _A      :    Optional[Any]=5_1_2				,    _A      :    int=1_6				,    _A      :    List[str]=2				,    _A      :    Optional[int]=0.02				,    _A      :    Optional[Any]=3				,    _A      :    List[str]=4				,    _A      :    int=None				,    )						->						List[Any]:
         snake_case      =					parent
         snake_case      =					batch_size
         snake_case      =					seq_length
         snake_case      =					is_training
         snake_case      =					use_input_mask
         snake_case      =					use_token_type_ids
         snake_case      =					use_labels
         snake_case      =					vocab_size
         snake_case      =					hidden_size
         snake_case      =					num_hidden_layers
         snake_case      =					num_attention_heads
         snake_case      =					intermediate_size
         snake_case      =					hidden_act
         snake_case      =					hidden_dropout_prob
         snake_case      =					attention_probs_dropout_prob
         snake_case      =					max_position_embeddings
         snake_case      =					type_vocab_size
         snake_case      =					type_sequence_label_size
         snake_case      =					initializer_range
         snake_case      =					num_labels
         snake_case      =					num_choices
         snake_case      =					scope





     def 		UpperCAmelCase(self      :    List[str]   )						->						Dict:
         snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size   )

         snake_case      =					None
         if self.use_input_mask:
             snake_case      =					random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case      =					None
         snake_case      =					None
         snake_case      =					None
         if self.use_labels:
             snake_case      =					ids_tensor([self.batch_size]				,    self.type_sequence_label_size   )
             snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels   )
             snake_case      =					ids_tensor([self.batch_size]				,    self.num_choices   )

         snake_case      =					MPNetConfig(
             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				,    initializer_range=self.initializer_range				,    )
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    str				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    List[str]				,    _A      :    int   )						->						List[Any]:
         snake_case      =					TFMPNetModel(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         snake_case      =					[input_ids, input_mask]
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)   )





     def 		UpperCAmelCase(self      :    int				,    _A      :    str				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    Union[str, Any]				,    _A      :    Optional[Any]				,    _A      :    Any   )						->						int:
         snake_case      =					TFMPNetForMaskedLM(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)   )





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    Optional[int]				,    _A      :    int				,    _A      :    int				,    _A      :    List[str]				,    _A      :    Union[str, Any]				,    _A      :    Union[str, Any]   )						->						int:
         snake_case      =					TFMPNetForQuestionAnswering(config=_A   )
         snake_case      =					{
             "input_ids": input_ids,
             "attention_mask": input_mask,
         }
         snake_case      =					model(_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      :    Optional[int]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    Dict				,    _A      :    Optional[int]				,    _A      :    str				,    _A      :    Optional[Any]   )						->						Dict:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForSequenceClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)   )





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    List[str]				,    _A      :    int				,    _A      :    Optional[int]				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    int   )						->						Optional[Any]:
         snake_case      =					self.num_choices
         snake_case      =					TFMPNetForMultipleChoice(_A   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					{
             "input_ids": multiple_choice_inputs_ids,
             "attention_mask": multiple_choice_input_mask,
         }
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)   )





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    int				,    _A      :    int				,    _A      :    Tuple   )						->						Tuple:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForTokenClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)   )





     def 		UpperCAmelCase(self      :    List[Any]   )						->						List[str]:
         snake_case      =					self.prepare_config_and_inputs()
         ((snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case))      =					config_and_inputs
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase		(  A_							, A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							List[Any]           =		(
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     UpperCAmelCase__				:							Optional[int]           =		(
         {
             "feature-extraction": TFMPNetModel,
             "fill-mask": TFMPNetForMaskedLM,
             "question-answering": TFMPNetForQuestionAnswering,
             "text-classification": TFMPNetForSequenceClassification,
             "token-classification": TFMPNetForTokenClassification,
             "zero-shot": TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__				:							int           =		False
     UpperCAmelCase__				:							str           =		False





     def 		UpperCAmelCase(self      :    List[Any]   )						->						str:
         snake_case      =					TFMPNetModelTester(self   )
         snake_case      =					ConfigTester(self				,    config_class=_A				,    hidden_size=3_7   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						int:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*_A   )





     def 		UpperCAmelCase(self      :    List[str]   )						->						List[str]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_masked_lm(*_A   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*_A   )





     def 		UpperCAmelCase(self      :    Dict   )						->						Tuple:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A   )





     def 		UpperCAmelCase(self      :    int   )						->						List[Any]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						int:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*_A   )





     @slow
     def 		UpperCAmelCase(self      :    Optional[int]   )						->						Union[str, Any]:
         for model_name in ["microsoft/mpnet-base"]:
             snake_case      =					TFMPNetModel.from_pretrained(_A   )
             self.assertIsNotNone(_A   )







@require_tf
class      lowerCamelCase		(  unittest.TestCase  ):





     @slow
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						List[Any]:
         snake_case      =					TFMPNetModel.from_pretrained("microsoft/mpnet-base"   )
         snake_case      =					tf.constant([[0, 1, 2, 3, 4, 5]]   )
         snake_case      =					model(_A   )[0]

         snake_case      =					[1, 6, 7_6_8]
         self.assertEqual(output.shape				,    _A   )

         snake_case      =					tf.constant(
             [
                 [
                     [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
                     [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
                     [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
                 ]
             ]   )
         tf.debugging.assert_near(output[:, :3, :3]				,    _A				,    atol=1E-4   )

