


'''simple docstring'''





import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
    import tensorflow as tf

    tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset


__lowerCamelCase       				=	5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def 					a__    (				UpperCamelCase_	:			Namespace	):
   return PTtoTFCommand(
       args.model_name,      args.local_dir,      args.max_error,      args.new_weights,      args.no_pr,      args.push,      args.extra_commit_description,      args.override_model_class,      )




class 							UpperCAmelCase ( _snake_case	):


 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  ArgumentParser   ):
    UpperCAmelCase__  :Any            =			parser.add_parser(
        '''pt-to-tf''' ,					help=(
            '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
            ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
        ) ,					)
    train_parser.add_argument(
        '''--model-name''' ,					type=__lowerCamelCase ,					required=__lowerCamelCase ,					help='''The model name, including owner/organization, as seen on the hub.''' ,					)
    train_parser.add_argument(
        '''--local-dir''' ,					type=__lowerCamelCase ,					default='''''' ,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}''' ,					)
    train_parser.add_argument(
        '''--max-error''' ,					type=__lowerCamelCase ,					default=__lowerCamelCase ,					help=(
            f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
        ) ,					)
    train_parser.add_argument(
        '''--new-weights''' ,					action='''store_true''' ,					help='''Optional flag to create new TensorFlow weights, even if they already exist.''' ,					)
    train_parser.add_argument(
        '''--no-pr''' ,					action='''store_true''' ,					help='''Optional flag to NOT open a PR with converted weights.'''   )
    train_parser.add_argument(
        '''--push''' ,					action='''store_true''' ,					help='''Optional flag to push the weights directly to `main` (requires permissions)''' ,					)
    train_parser.add_argument(
        '''--extra-commit-description''' ,					type=__lowerCamelCase ,					default='''''' ,					help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).''' ,					)
    train_parser.add_argument(
        '''--override-model-class''' ,					type=__lowerCamelCase ,					default=__lowerCamelCase ,					help='''If you think you know better than the auto-detector, you can specify the model class here. '''
        '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.''' ,					)
    train_parser.set_defaults(func=__lowerCamelCase   )


 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Union[str, Any]   ):
    UpperCAmelCase__  :Dict            =			set(pt_outputs.keys()   )
    UpperCAmelCase__  :Optional[int]            =			set(tf_outputs.keys()   )
    if pt_out_attrs != tf_out_attrs:
       raise ValueError(
           f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
           f''' {tf_out_attrs})'''   )

    # 2. For each output attribute, computes the difference
    def _find_pt_tf_differences(__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  List[str] ,					__lowerCamelCase       :  Union[str, Any]=""   ):
       # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
       # recursivelly, keeping the name of the attribute.
       if isinstance(__lowerCamelCase ,					torch.Tensor   ):
          UpperCAmelCase__  :Dict            =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()   )   )
          UpperCAmelCase__  :Optional[Any]            =			tensor_difference
       else:
          UpperCAmelCase__  :Tuple            =			attr_name
          for i, pt_item in enumerate(__lowerCamelCase   ):
             # If it is a named attribute, we keep the name. Otherwise, just its index.
             if isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
                UpperCAmelCase__  :str            =			root_name + pt_item
                UpperCAmelCase__  :List[str]            =			tf_out[pt_item]
                UpperCAmelCase__  :Dict            =			pt_out[pt_item]
             else:
                UpperCAmelCase__  :Any            =			root_name + f'''[{i}]'''
                UpperCAmelCase__  :str            =			tf_out[i]
             UpperCAmelCase__  :str            =			_find_pt_tf_differences(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )

       return differences

    return _find_pt_tf_differences(__lowerCamelCase ,					__lowerCamelCase ,					{}   )


 def __init__(			self       :  Optional[Any] ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  float ,					__lowerCamelCase       :  bool ,					__lowerCamelCase       :  bool ,					__lowerCamelCase       :  bool ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  str ,					*__lowerCamelCase       :  List[str] ,					):
    UpperCAmelCase__  :List[Any]            =			logging.get_logger('''transformers-cli/pt_to_tf'''   )
    UpperCAmelCase__  :int            =			model_name
    UpperCAmelCase__  :Union[str, Any]            =			local_dir if local_dir else os.path.join('''/tmp''' ,					__lowerCamelCase   )
    UpperCAmelCase__  :int            =			max_error
    UpperCAmelCase__  :str            =			new_weights
    UpperCAmelCase__  :List[Any]            =			no_pr
    UpperCAmelCase__  :Tuple            =			push
    UpperCAmelCase__  :Any            =			extra_commit_description
    UpperCAmelCase__  :Optional[Any]            =			override_model_class


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any] ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  int   ):

    def _get_audio_input():
       UpperCAmelCase__  :Optional[int]            =			load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,					'''clean''' ,					split='''validation'''   )
       UpperCAmelCase__  :Dict            =			ds.sort('''id'''   ).select(range(2   )   )[:2]['''audio''']
       UpperCAmelCase__  :Dict            =			[x['''array'''] for x in speech_samples]
       return raw_samples

    UpperCAmelCase__  :Optional[Any]            =			type(pt_model.config   )
    if model_config_class in PROCESSOR_MAPPING:
       UpperCAmelCase__  :Dict            =			AutoProcessor.from_pretrained(self._local_dir   )
       if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
          UpperCAmelCase__  :Optional[int]            =			processor.tokenizer.eos_token
    elif model_config_class in IMAGE_PROCESSOR_MAPPING:
       UpperCAmelCase__  :Any            =			AutoImageProcessor.from_pretrained(self._local_dir   )
    elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
       UpperCAmelCase__  :Optional[Any]            =			AutoFeatureExtractor.from_pretrained(self._local_dir   )
    elif model_config_class in TOKENIZER_MAPPING:
       UpperCAmelCase__  :int            =			AutoTokenizer.from_pretrained(self._local_dir   )
       if processor.pad_token is None:
          UpperCAmelCase__  :Optional[int]            =			processor.eos_token
    else:
       raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''   )

    UpperCAmelCase__  :Union[str, Any]            =			set(inspect.signature(pt_model.forward   ).parameters.keys()   )
    UpperCAmelCase__  :Optional[Any]            =			{}
    if "input_ids" in model_forward_signature:
       processor_inputs.update(
           {
               '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
               '''padding''': True,
               '''truncation''': True,
           }   )
    if "pixel_values" in model_forward_signature:
       UpperCAmelCase__  :Any            =			load_dataset('''cifar10''' ,					'''plain_text''' ,					split='''test'''   )[:2]['''img''']
       processor_inputs.update({'''images''': sample_images}   )
    if "input_features" in model_forward_signature:
       UpperCAmelCase__  :List[Any]            =			inspect.signature(processor.feature_extractor   ).parameters
       # Pad to the largest input length by default but take feature extractor default
       # padding value if it exists e.g. "max_length" and is not False or None
       if "padding" in feature_extractor_signature:
          UpperCAmelCase__  :List[str]            =			feature_extractor_signature['''padding'''].default
          if default_strategy is not False and default_strategy is not None:
             UpperCAmelCase__  :Dict            =			default_strategy
          else:
             UpperCAmelCase__  :Union[str, Any]            =			True
       else:
          UpperCAmelCase__  :Optional[int]            =			True
       processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}   )
    if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
       processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}   )
    UpperCAmelCase__  :Optional[Any]            =			processor(**__lowerCamelCase ,					return_tensors='''pt'''   )
    UpperCAmelCase__  :List[Any]            =			processor(**__lowerCamelCase ,					return_tensors='''tf'''   )

    # Extra input requirements, in addition to the input modality
    if (
        config.is_encoder_decoder
        or (hasattr(__lowerCamelCase ,					'''encoder'''   ) and hasattr(__lowerCamelCase ,					'''decoder'''   ))
        or "decoder_input_ids" in tf_dummy_inputs
    ):
       UpperCAmelCase__  :Dict            =			np.asarray([[1], [1]] ,					dtype=__lowerCamelCase   ) * (pt_model.config.decoder_start_token_id or 0)
       pt_input.update({'''decoder_input_ids''': torch.tensor(__lowerCamelCase   )}   )
       tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(__lowerCamelCase   )}   )

    return pt_input, tf_input





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
    if version.parse(huggingface_hub.__version__   ) < version.parse('''0.9.0'''   ):
       raise ImportError(
           '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
           ''' installation.'''   )
    else:
       from huggingface_hub import Repository, create_commit
       from huggingface_hub._commit_api import CommitOperationAdd

    # Fetch remote data
    UpperCAmelCase__  :Union[str, Any]            =			Repository(local_dir=self._local_dir ,					clone_from=self._model_name   )

    # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
    UpperCAmelCase__  :Dict            =			AutoConfig.from_pretrained(self._local_dir   )
    UpperCAmelCase__  :List[str]            =			config.architectures
    if self._override_model_class is not None:
       if self._override_model_class.startswith('''TF'''   ):
          UpperCAmelCase__  :str            =			[self._override_model_class[2:]]
       else:
          UpperCAmelCase__  :Any            =			[self._override_model_class]
       try:
          UpperCAmelCase__  :int            =			getattr(import_module('''transformers'''   ) ,					architectures[0]   )
       except AttributeError:
          raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''   )
       try:
          UpperCAmelCase__  :str            =			getattr(import_module('''transformers'''   ) ,					'''TF''' + architectures[0]   )
       except AttributeError:
          raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''   )
    elif architectures is None:  # No architecture defined -- use auto classes
       UpperCAmelCase__  :Union[str, Any]            =			getattr(import_module('''transformers'''   ) ,					'''AutoModel'''   )
       UpperCAmelCase__  :Union[str, Any]            =			getattr(import_module('''transformers'''   ) ,					'''TFAutoModel'''   )
       self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''   )
    else:  # Architecture defined -- use it
       if len(__lowerCamelCase   ) > 1:
          raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''   )
       self._logger.warning(f'''Detected architecture: {architectures[0]}'''   )
       UpperCAmelCase__  :Any            =			getattr(import_module('''transformers'''   ) ,					architectures[0]   )
       try:
          UpperCAmelCase__  :str            =			getattr(import_module('''transformers'''   ) ,					'''TF''' + architectures[0]   )
       except AttributeError:
          raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''   )

        # Check the TF dummy inputs to see what keys we need in the forward pass
    UpperCAmelCase__  :Dict            =			tf_class.from_config(__lowerCamelCase   )
    UpperCAmelCase__  :int            =			tf_from_pt_model.dummy_inputs

    del tf_from_pt_model  # Try to keep only one model in memory at a time

    # Load the model and get some basic inputs
    UpperCAmelCase__  :str            =			pt_class.from_pretrained(self._local_dir   )
    pt_model.eval()

    UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			self.get_inputs(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )

    with torch.no_grad():
       UpperCAmelCase__  :Union[str, Any]            =			pt_model(**__lowerCamelCase ,					output_hidden_states=__lowerCamelCase   )
    del pt_model  # will no longer be used, and may have a large memory footprint

    UpperCAmelCase__  :str            =			tf_class.from_pretrained(self._local_dir ,					from_pt=__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			tf_from_pt_model(**__lowerCamelCase ,					output_hidden_states=__lowerCamelCase ,					training=__lowerCamelCase   )

    # Confirms that cross loading PT weights into TF worked.
    UpperCAmelCase__  :int            =			self.find_pt_tf_differences(__lowerCamelCase ,					__lowerCamelCase   )
    UpperCAmelCase__  :str            =			{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
    UpperCAmelCase__  :Optional[int]            =			{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
    if len(__lowerCamelCase   ) == 0 and architectures is not None:
       raise ValueError(
           f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
           ''' output was found. All outputs start with \'hidden\''''   )
    UpperCAmelCase__  :List[Any]            =			max(output_differences.values()   ) if output_differences else 0.0
    UpperCAmelCase__  :List[str]            =			max(hidden_differences.values()   )
    if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
       raise ValueError(
           '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
           + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
           + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]   )
           + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
           + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]   )   )

    # Save the weights in a TF format (if needed) and confirms that the results are still good
    UpperCAmelCase__  :Optional[int]            =			os.path.join(self._local_dir ,					__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			os.path.join(self._local_dir ,					__lowerCamelCase   )
    if (not os.path.exists(__lowerCamelCase   ) and not os.path.exists(__lowerCamelCase   )) or self._new_weights:
       tf_from_pt_model.save_pretrained(self._local_dir   )
    del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

    UpperCAmelCase__  :List[str]            =			tf_class.from_pretrained(self._local_dir   )
    UpperCAmelCase__  :Tuple            =			tf_model(**__lowerCamelCase ,					output_hidden_states=__lowerCamelCase   )

    UpperCAmelCase__  :List[Any]            =			self.find_pt_tf_differences(__lowerCamelCase ,					__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
    UpperCAmelCase__  :List[str]            =			{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
    if len(__lowerCamelCase   ) == 0 and architectures is not None:
       raise ValueError(
           f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
           ''' output was found. All outputs start with \'hidden\''''   )
    UpperCAmelCase__  :Union[str, Any]            =			max(output_differences.values()   ) if output_differences else 0.0
    UpperCAmelCase__  :Union[str, Any]            =			max(hidden_differences.values()   )
    if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
       raise ValueError(
           '''The converted TensorFlow model has different outputs, something went wrong!\n'''
           + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
           + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]   )
           + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
           + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]   )   )

    UpperCAmelCase__  :Any            =			'''Update TF weights''' if self._new_weights else '''Add TF weights'''
    if self._push:
       repo.git_add(auto_lfs_track=__lowerCamelCase   )
       repo.git_commit(__lowerCamelCase   )
       repo.git_push(blocking=__lowerCamelCase   )  # this prints a progress bar with the upload
       self._logger.warning(f'''TF weights pushed into {self._model_name}'''   )
    elif not self._no_pr:
       self._logger.warning('''Uploading the weights into a new PR...'''   )
       UpperCAmelCase__  :Optional[int]            =			(
           '''Model converted by the [`transformers`\' `pt_to_tf`'''
           ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
           '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
           f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
           f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
           f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
           f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
       )
       if self._max_error > MAX_ERROR:
          commit_descrition += (
              f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
          )
       if self._extra_commit_description:
          commit_descrition += "\n\n" + self._extra_commit_description

       # sharded model -> adds all related files (index and .h5 shards)
       if os.path.exists(__lowerCamelCase   ):
          UpperCAmelCase__  :Union[str, Any]            =			[
              CommitOperationAdd(path_in_repo=__lowerCamelCase ,					path_or_fileobj=__lowerCamelCase   )
          ]
          for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''   ):
             operations += [
                 CommitOperationAdd(path_in_repo=os.path.basename(__lowerCamelCase   ) ,					path_or_fileobj=__lowerCamelCase   )
             ]
       else:
          UpperCAmelCase__  :Tuple            =			[CommitOperationAdd(path_in_repo=__lowerCamelCase ,					path_or_fileobj=__lowerCamelCase   )]

       UpperCAmelCase__  :Dict            =			create_commit(
           repo_id=self._model_name ,					operations=__lowerCamelCase ,					commit_message=__lowerCamelCase ,					commit_description=__lowerCamelCase ,					repo_type='''model''' ,					create_pr=__lowerCamelCase ,					).pr_url
       self._logger.warning(f'''PR open in {hub_pr_url}'''   )







'''simple docstring'''





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCamelCase       				=	'''stas/tiny-wmt19-en-ru'''




class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				FSMTTokenizer
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase__  :Any            =			[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    UpperCAmelCase__  :Dict            =			dict(zip(__lowerCamelCase ,					range(len(__lowerCamelCase   )   )   )   )
    UpperCAmelCase__  :Dict            =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    UpperCAmelCase__  :Dict            =			['''en''', '''ru''']
    UpperCAmelCase__  :Tuple            =			{
        '''langs''': self.langs,
        '''src_vocab_size''': 1_0,
        '''tgt_vocab_size''': 2_0,
    }

    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''src_vocab_file''']   )
    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
    UpperCAmelCase__  :Dict            =			os.path.join(self.tmpdirname ,					'''tokenizer_config.json'''   )
    UpperCAmelCase__  :Union[str, Any]            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']   )
    with open(self.src_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.tgt_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.merges_file ,					'''w'''   ) as fp:
       fp.write('''\n'''.join(__lowerCamelCase   )   )
    with open(__lowerCamelCase ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			FSMTTokenizer.from_pretrained(__lowerCamelCase   )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,					['''en''', '''ru''']   )
    self.assertEqual(tokenizer.src_vocab_size ,					2_1   )
    self.assertEqual(tokenizer.tgt_vocab_size ,					2_1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file   )

    UpperCAmelCase__  :Any            =			'''lower'''
    UpperCAmelCase__  :List[Any]            =			['''low''', '''er</w>''']
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.tokenize(__lowerCamelCase   )
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

    UpperCAmelCase__  :Dict            =			tokens + ['''<unk>''']
    UpperCAmelCase__  :Union[str, Any]            =			[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   ) ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :str            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.encode('''sequence builders''' ,					add_special_tokens=__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__lowerCamelCase   )

    UpperCAmelCase__  :Any            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,					__lowerCamelCase   )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :List[str]            =			self.tokenizer_en_ru
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Any            =			[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
       UpperCAmelCase__  :List[Any]            =			tokenizer_enc.encode(__lowerCamelCase ,					return_tensors=__lowerCamelCase   )
       self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

       # and decode backward, using the reversed languages model
       UpperCAmelCase__  :str            =			tokenizer_dec.decode(__lowerCamelCase ,					skip_special_tokens=__lowerCamelCase   )
       self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' ,					do_lower_case=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			tokenizer.tokenize('''USA is United States of America'''   )
    UpperCAmelCase__  :str            =			['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    pass





 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    pass



