


"""simple docstring"""



# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase      		=			2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
UpperCAmelCase      		=			{
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.15},
    '''wmt19-en-de''': {'''length_penalty''': 1.0},
    '''wmt19-de-en''': {'''length_penalty''': 1.1},
    # allenai:
    '''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
    '''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
    '''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}

# this remaps the different models to their organization names
UpperCAmelCase      		=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 UpperCAmelCase      		=			'''facebook'''
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
 UpperCAmelCase      		=			'''allenai'''






def       lowerCamelCase					(a_						:Dict)     ->							str:
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       lowercase		:str       							=				dict((re.sub(R'''@@$'''				,				''''''				,				a_), v) if k.endswith('''@@''') else (re.sub(R'''$'''				,				'''</w>'''				,				a_), v) for k, v in d.items())
       lowercase		:Dict       							=				'''<s> <pad> </s> <unk>'''.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"""{k}</w>"""]
              lowercase		:str       							=				d[k]  # restore
       return da






def       lowerCamelCase					(a_						:Any				,				a_						:Tuple)     ->							List[str]:
       # prep
       assert os.path.exists(a_)
       os.makedirs(a_				,				exist_ok=a_)
       print(F"""Writing results to {pytorch_dump_folder_path}""")

       # handle various types of models

       lowercase		:List[str]       							=				basename(a_)
       lowercase		:Dict       							=				dirname(a_)

       lowercase		:List[str]       							=				fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
       lowercase		:List[Any]       							=				cls.hub_models()
       lowercase		:Any       							=				{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
       lowercase		:Dict       							=				'''.'''
       # note: since the model dump is old, fairseq has upgraded its model some
       # time later, and it does a whole lot of rewrites and splits on the saved
       # weights, therefore we can't use torch.load() directly on the model file.
       # see: upgrade_state_dict(state_dict) in fairseq_model.py
       print(F"""using checkpoint {checkpoint_file}""")
       lowercase		:List[Any]       							=				hub_utils.from_pretrained(
           a_				,				a_				,				a_				,				archive_map=a_				,				**a_)

       lowercase		:int       							=				vars(chkpt['''args''']['''model'''])

       lowercase		:List[str]       							=				args['''source_lang''']
       lowercase		:Optional[int]       							=				args['''target_lang''']

       lowercase		:Optional[int]       							=				dirname(a_)
       lowercase		:str       							=				basename(a_)

       # dicts
       lowercase		:Dict       							=				os.path.join(a_				,				F"""dict.{src_lang}.txt""")
       lowercase		:List[str]       							=				os.path.join(a_				,				F"""dict.{tgt_lang}.txt""")

       lowercase		:Dict       							=				Dictionary.load(a_)
       lowercase		:Dict       							=				rewrite_dict_keys(src_dict.indices)
       lowercase		:Union[str, Any]       							=				len(a_)
       lowercase		:Any       							=				os.path.join(a_				,				'''vocab-src.json''')
       print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(a_				,				ensure_ascii=a_				,				indent=a_))

       # detect whether this is a do_lower_case situation, which can be derived by checking whether we
       # have at least one uppercase letter in the source vocab
       lowercase		:Tuple       							=				True
       for k in src_vocab.keys():
              if not k.islower():
                     lowercase		:Dict       							=				False
                     break

       lowercase		:str       							=				Dictionary.load(a_)
       lowercase		:Union[str, Any]       							=				rewrite_dict_keys(tgt_dict.indices)
       lowercase		:Union[str, Any]       							=				len(a_)
       lowercase		:Any       							=				os.path.join(a_				,				'''vocab-tgt.json''')
       print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(a_				,				ensure_ascii=a_				,				indent=a_))

       # merges_file (bpecodes)
       lowercase		:Dict       							=				os.path.join(a_				,				VOCAB_FILES_NAMES['''merges_file'''])
       for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
              lowercase		:Optional[Any]       							=				os.path.join(a_				,				a_)
              if os.path.exists(a_):
                     break
       with open(a_				,				encoding='''utf-8''') as fin:
              lowercase		:Optional[Any]       							=				fin.read()
       lowercase		:List[Any]       							=				re.sub(R''' \d+$'''				,				''''''				,				a_				,				0				,				re.M)  # remove frequency number
       print(F"""Generating {merges_file}""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as fout:
              fout.write(a_)

       # model config
       lowercase		:List[Any]       							=				os.path.join(a_				,				'''config.json''')

       # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
       # may have to modify the tokenizer if a different type is used by a future model
       assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
       assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

       lowercase		:List[Any]       							=				{
           '''architectures''': ['''FSMTForConditionalGeneration'''],
           '''model_type''': '''fsmt''',
           '''activation_dropout''': args['''activation_dropout'''],
           '''activation_function''': '''relu''',
           '''attention_dropout''': args['''attention_dropout'''],
           '''d_model''': args['''decoder_embed_dim'''],
           '''dropout''': args['''dropout'''],
           '''init_std''': 0.02,
           '''max_position_embeddings''': args['''max_source_positions'''],
           '''num_hidden_layers''': args['''encoder_layers'''],
           '''src_vocab_size''': src_vocab_size,
           '''tgt_vocab_size''': tgt_vocab_size,
           '''langs''': [src_lang, tgt_lang],
           '''encoder_attention_heads''': args['''encoder_attention_heads'''],
           '''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
           '''encoder_layerdrop''': args['''encoder_layerdrop'''],
           '''encoder_layers''': args['''encoder_layers'''],
           '''decoder_attention_heads''': args['''decoder_attention_heads'''],
           '''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
           '''decoder_layerdrop''': args['''decoder_layerdrop'''],
           '''decoder_layers''': args['''decoder_layers'''],
           '''bos_token_id''': 0,
           '''pad_token_id''': 1,
           '''eos_token_id''': 2,
           '''is_encoder_decoder''': True,
           '''scale_embedding''': not args['''no_scale_embedding'''],
           '''tie_word_embeddings''': args['''share_all_embeddings'''],
       }

       # good hparam defaults to start with
       lowercase		:Union[str, Any]       							=				5
       lowercase		:Tuple       							=				False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowercase		:Union[str, Any]       							=				best_score_hparams[model_dir]['''length_penalty''']
       else:
              lowercase		:Tuple       							=				1.0

       print(F"""Generating {fsmt_model_config_file}""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(a_				,				ensure_ascii=a_				,				indent=a_))

       # tokenizer config
       lowercase		:Tuple       							=				os.path.join(a_				,				a_)

       lowercase		:str       							=				{
           '''langs''': [src_lang, tgt_lang],
           '''model_max_length''': 1024,
           '''do_lower_case''': do_lower_case,
       }

       print(F"""Generating {fsmt_tokenizer_config_file}""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(a_				,				ensure_ascii=a_				,				indent=a_))

       # model
       lowercase		:Optional[int]       							=				chkpt['''models'''][0]
       lowercase		:int       							=				model.state_dict()

       # rename keys to start with 'model.'
       lowercase		:int       							=				OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items())

       # remove unneeded keys
       lowercase		:Optional[int]       							=				[
           '''model.model''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''model.encoder_embed_tokens.weight''',
           '''model.decoder_embed_tokens.weight''',
           '''model.encoder.embed_positions._float_tensor''',
           '''model.decoder.embed_positions._float_tensor''',
       ]
       for k in ignore_keys:
              model_state_dict.pop(a_				,				a_)

       lowercase		:Any       							=				FSMTConfig.from_pretrained(a_)
       lowercase		:Dict       							=				FSMTForConditionalGeneration(a_)

       # check that it loads ok
       model_new.load_state_dict(a_				,				strict=a_)

       # save
       lowercase		:str       							=				os.path.join(a_				,				a_)
       print(F"""Generating {pytorch_weights_dump_path}""")
       torch.save(a_				,				a_)

       print('''Conversion is done!''')
       print('''\nLast step is to upload the files to s3''')
       print(F"""cd {data_root}""")
       print(F"""transformers-cli upload {model_dir}""")


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--fsmt_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
         ''' bpecodes, etc.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 UpperCAmelCase      		=			parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase      		=			{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class 					__magic_name__		(			unittest.TestCase     ):
       __A			:      int        =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       __A			:      List[str]        =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              __A			:      int        =   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              __A			:      Optional[int]        =   {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    Any  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				ZeroShotClassificationPipeline(
                  model=snake_case__  ,     tokenizer=snake_case__  ,     candidate_labels=['''polics''', '''health''']				)
              return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              lowercase		:int       							=				classifier('''Who are you voting for in 2020?'''  ,     candidate_labels='''politics'''				)
              self.assertEqual(snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				)], '''scores''': [ANY(snake_case__				)]}				)

              # No kwarg
              lowercase		:Tuple       							=				classifier('''Who are you voting for in 2020?'''  ,     ['''politics''']				)
              self.assertEqual(snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				)], '''scores''': [ANY(snake_case__				)]}				)

              lowercase		:Tuple       							=				classifier('''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''']				)
              self.assertEqual(snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				)], '''scores''': [ANY(snake_case__				)]}				)

              lowercase		:Union[str, Any]       							=				classifier('''Who are you voting for in 2020?'''  ,     candidate_labels='''politics, public health'''				)
              self.assertEqual(
                  snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				), ANY(snake_case__				)], '''scores''': [ANY(snake_case__				), ANY(snake_case__				)]}				)
              self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']				)				)  ,     1.0				)

              lowercase		:Optional[Any]       							=				classifier('''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''', '''public health''']				)
              self.assertEqual(
                  snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				), ANY(snake_case__				)], '''scores''': [ANY(snake_case__				), ANY(snake_case__				)]}				)
              self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']				)				)  ,     1.0				)

              lowercase		:Optional[Any]       							=				classifier(
                  '''Who are you voting for in 2020?'''  ,     candidate_labels='''politics'''  ,     hypothesis_template='''This text is about {}'''				)
              self.assertEqual(snake_case__  ,     {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				)], '''scores''': [ANY(snake_case__				)]}				)

              # https://github.com/huggingface/transformers/issues/13846
              lowercase		:Optional[Any]       							=				classifier(['''I am happy''']  ,     ['''positive''', '''negative''']				)
              self.assertEqual(
                  snake_case__  ,     [
                      {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				), ANY(snake_case__				)], '''scores''': [ANY(snake_case__				), ANY(snake_case__				)]}
                      for i in range(1				)
                  ]  ,     )
              lowercase		:Tuple       							=				classifier(['''I am happy''', '''I am sad''']  ,     ['''positive''', '''negative''']				)
              self.assertEqual(
                  snake_case__  ,     [
                      {'''sequence''': ANY(snake_case__				), '''labels''': [ANY(snake_case__				), ANY(snake_case__				)], '''scores''': [ANY(snake_case__				), ANY(snake_case__				)]}
                      for i in range(2				)
                  ]  ,     )

              with self.assertRaises(snake_case__				):
                     classifier(''''''  ,     candidate_labels='''politics'''				)

              with self.assertRaises(snake_case__				):
                     classifier(snake_case__  ,     candidate_labels='''politics'''				)

              with self.assertRaises(snake_case__				):
                     classifier('''Who are you voting for in 2020?'''  ,     candidate_labels=''''''				)

              with self.assertRaises(snake_case__				):
                     classifier('''Who are you voting for in 2020?'''  ,     candidate_labels=snake_case__				)

              with self.assertRaises(snake_case__				):
                     classifier(
                         '''Who are you voting for in 2020?'''  ,     candidate_labels='''politics'''  ,     hypothesis_template='''Not formatting template'''  ,     )

              with self.assertRaises(snake_case__				):
                     classifier(
                         '''Who are you voting for in 2020?'''  ,     candidate_labels='''politics'''  ,     hypothesis_template=snake_case__  ,     )

              self.run_entailment_id(snake_case__				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Pipeline				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				zero_shot_classifier.model.config
              lowercase		:int       							=				config.labelaid
              lowercase		:str       							=				zero_shot_classifier.entailment_id

              lowercase		:Dict       							=				{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
              self.assertEqual(zero_shot_classifier.entailment_id  ,     -1				)

              lowercase		:Optional[Any]       							=				{'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
              self.assertEqual(zero_shot_classifier.entailment_id  ,     0				)

              lowercase		:Tuple       							=				{'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
              self.assertEqual(zero_shot_classifier.entailment_id  ,     0				)

              lowercase		:str       							=				{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
              self.assertEqual(zero_shot_classifier.entailment_id  ,     2				)

              lowercase		:Optional[Any]       							=				original_labelaid
              self.assertEqual(snake_case__  ,     zero_shot_classifier.entailment_id				)






       @require_torch
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				pipeline(
                  '''zero-shot-classification'''  ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''  ,     framework='''pt'''  ,     )
              # There was a regression in 4.10 for this
              # Adding a test so we don't make the mistake again.
              # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
              zero_shot_classifier(
                  '''Who are you voting for in 2020?''' * 1_0_0  ,     candidate_labels=['''politics''', '''public health''', '''science''']				)






       @require_torch
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				pipeline(
                  '''zero-shot-classification'''  ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''  ,     framework='''pt'''  ,     )
              lowercase		:Any       							=				zero_shot_classifier(
                  '''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''', '''public health''', '''science''']				)

              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': '''Who are you voting for in 2020?''',
                      '''labels''': ['''science''', '''public health''', '''politics'''],
                      '''scores''': [0.3_33, 0.3_33, 0.3_33],
                  }  ,     )






       @require_tf
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				pipeline(
                  '''zero-shot-classification'''  ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''  ,     framework='''tf'''  ,     )
              lowercase		:List[Any]       							=				zero_shot_classifier(
                  '''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''', '''public health''', '''science''']				)

              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': '''Who are you voting for in 2020?''',
                      '''labels''': ['''science''', '''public health''', '''politics'''],
                      '''scores''': [0.3_33, 0.3_33, 0.3_33],
                  }  ,     )






       @slow
       @require_torch
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				pipeline('''zero-shot-classification'''  ,     model='''roberta-large-mnli'''  ,     framework='''pt'''				)
              lowercase		:Union[str, Any]       							=				zero_shot_classifier(
                  '''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''', '''public health''', '''science''']				)

              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': '''Who are you voting for in 2020?''',
                      '''labels''': ['''politics''', '''public health''', '''science'''],
                      '''scores''': [0.9_76, 0.0_15, 0.0_09],
                  }  ,     )
              lowercase		:Optional[Any]       							=				zero_shot_classifier(
                  '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
                  ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
                  ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
                  ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
                  ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
                  ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
                  ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
                  ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
                  ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
                  ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
                  ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
                  ''' large and limited training data.'''  ,     candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']  ,     multi_label=snake_case__  ,     )
              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': (
                          '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                          ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                          ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                          ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                          ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                          ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                          ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                          ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                          ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                          ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                          ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                          ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                          ''' limited training data.'''
                      ),
                      '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
                      '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                  }  ,     )







       @slow
       @require_tf
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:str       							=				pipeline('''zero-shot-classification'''  ,     model='''roberta-large-mnli'''  ,     framework='''tf'''				)
              lowercase		:Optional[int]       							=				zero_shot_classifier(
                  '''Who are you voting for in 2020?'''  ,     candidate_labels=['''politics''', '''public health''', '''science''']				)

              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': '''Who are you voting for in 2020?''',
                      '''labels''': ['''politics''', '''public health''', '''science'''],
                      '''scores''': [0.9_76, 0.0_15, 0.0_09],
                  }  ,     )
              lowercase		:str       							=				zero_shot_classifier(
                  '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
                  ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
                  ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
                  ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
                  ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
                  ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
                  ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
                  ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
                  ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
                  ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
                  ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
                  ''' large and limited training data.'''  ,     candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']  ,     multi_label=snake_case__  ,     )
              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      '''sequence''': (
                          '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                          ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                          ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                          ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                          ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                          ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                          ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                          ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                          ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                          ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                          ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                          ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                          ''' limited training data.'''
                      ),
                      '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
                      '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                  }  ,     )

