

'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase				:   List[str]      				=  logging.get_logger(__name__)

lowerCamelCase				:   Optional[Any]      				=  {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class 							A__					( A__						):
  A__					=       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  A__					=       'mobilebert'





  def __init__(	self   :			Optional[Any]							,      _a   :			Optional[Any]=3_0522							,      _a   :			Optional[Any]=512							,      _a   :			str=24							,      _a   :			List[str]=4							,      _a   :			Tuple=512							,      _a   :			Any="relu"							,      _a   :			Optional[Any]=0.0							,      _a   :			str=0.1							,      _a   :			List[Any]=512							,      _a   :			Any=2							,      _a   :			Any=0.02							,      _a   :			Tuple=1e-12							,      _a   :			Union[str, Any]=0							,      _a   :			str=128							,      _a   :			List[Any]=True							,      _a   :			List[str]=True							,      _a   :			Tuple=128							,      _a   :			Optional[Any]=False							,      _a   :			Any=True							,      _a   :			Union[str, Any]=4							,      _a   :			Optional[Any]="no_norm"							,      _a   :			Optional[Any]=True							,      _a   :			str=None							,      **_a   :			Any							,      )			->					Tuple:





       '''simple docstring'''






       super().__init__(pad_token_id=_a							,      **_a				)

       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       _SCREAMING_SNAKE_CASE												=embedding_size
       _SCREAMING_SNAKE_CASE												=trigram_input
       _SCREAMING_SNAKE_CASE												=use_bottleneck
       _SCREAMING_SNAKE_CASE												=intra_bottleneck_size
       _SCREAMING_SNAKE_CASE												=use_bottleneck_attention
       _SCREAMING_SNAKE_CASE												=key_query_shared_bottleneck
       _SCREAMING_SNAKE_CASE												=num_feedforward_networks
       _SCREAMING_SNAKE_CASE												=normalization_type
       _SCREAMING_SNAKE_CASE												=classifier_activation

       if self.use_bottleneck:
            _SCREAMING_SNAKE_CASE												=intra_bottleneck_size
       else:
            _SCREAMING_SNAKE_CASE												=hidden_size

       _SCREAMING_SNAKE_CASE												=classifier_dropout







class 							A__					( A__						):





  @property
  def 		A						(	self   :			List[Any]				)			->					Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]				)



'''simple docstring'''
import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  A__					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





  def 		A						(	self   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			Union[str, Any]							,      _a   :			str				)			->					List[Any]:





       '''simple docstring'''






       if isinstance(model.config							,      _a				):
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=list(tokenizer.lang_code_to_id.keys()				)[:2]
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a							,      src_lang=_a							,      tgt_lang=_a				)
       else:
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a				)
       return translator, ["Some string", "Some other text"]





  def 		A						(	self   :			Union[str, Any]							,      _a   :			List[Any]							,      _a   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=translator('Some string'				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string', 'other string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}, {'translation_text': ANY(_a				)}]				)





  @require_torch
  def 		A						(	self   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Tuple				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_torch
  def 		A						(	self   :			Any				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Dict				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )







class 							A__					( unittest.TestCase						):





  @require_torch
  @slow
  def 		A						(	self   :			int				)			->					Optional[Any]:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation_cn_to_ar'				)

       # but we do for this one
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  @slow
  def 		A						(	self   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)
       _SCREAMING_SNAKE_CASE												=MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)

       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(_a				):
            translator('This is a test'				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='hi_IN'				)
       self.assertEqual(_a							,      [{'translation_text': 'यह एक परीक्षण है'}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)





  @require_torch
  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_cn_to_ar'							,      model=_a				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'cn'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'ar'				)





  @require_torch
  def 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       with pytest.warns(_a							,      match=r'.*translation_en_to_de.*'				):
            _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a				)
       self.assertEqual(translator.task							,      'translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation'				)
