"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__A			   = logging.get_logger(__name__)


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			**_UpperCAmelCase							):
       lowercase__:   Any					=      feature_size
       lowercase__:   Optional[int]					=      sampling_rate
       lowercase__:   List[str]					=      padding_value

       lowercase__:   Dict					=      kwargs.pop('''padding_side'''    ,			'''right'''							)
       lowercase__:   Optional[int]					=      kwargs.pop('''return_attention_mask'''    ,			_UpperCAmelCase							)

       super().__init__(**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       # If we have a list of dicts, let's convert it in a dict of lists
       # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
       if isinstance(_UpperCAmelCase    ,			(list, tuple)							) and isinstance(processed_features[0]    ,			(dict, BatchFeature)							):
          lowercase__:   Dict					=      {
              key: [example[key] for example in processed_features] for key in processed_features[0].keys()
          }

       # The model's main input name, usually `input_values`, has be passed for padding
       if self.model_input_names[0] not in processed_features:
          raise ValueError(
              '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
              F""" to this method that includes {self.model_input_names[0]}, but you provided"""
              F""" {list(processed_features.keys()							)}"""							)

       lowercase__:   List[Any]					=      processed_features[self.model_input_names[0]]
       lowercase__:   Optional[Any]					=      (
           return_attention_mask if return_attention_mask is not None else self.return_attention_mask
       )

       if len(_UpperCAmelCase							) == 0:
          if return_attention_mask:
             lowercase__:   str					=      []
          return processed_features

       # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
       # and rebuild them afterwards if no return_tensors is specified
       # Note that we lose the specific device the tensor may be on for PyTorch

       lowercase__:   Optional[Any]					=      required_input[0]
       if isinstance(_UpperCAmelCase    ,			(list, tuple)							):
          # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
          lowercase__:   Any					=      0
          while len(required_input[index]							) == 0:
             index += 1
          if index < len(_UpperCAmelCase							):
             lowercase__:   Dict					=      required_input[index][0]

       if return_tensors is None:
          if is_tf_tensor(_UpperCAmelCase							):
             lowercase__:   str					=      '''tf'''
          elif is_torch_tensor(_UpperCAmelCase							):
             lowercase__:   Optional[Any]					=      '''pt'''
          elif isinstance(_UpperCAmelCase    ,			(int, float, list, tuple, np.ndarray)							):
             lowercase__:   Optional[Any]					=      '''np'''
          else:
             raise ValueError(
                 F"""type of {first_element} unknown: {type(_UpperCAmelCase							)}. """
                 '''Should be one of a python, numpy, pytorch or tensorflow object.'''							)

       for key, value in processed_features.items():
          if isinstance(value[0]    ,			(int, float)							):
             lowercase__:   Any					=      to_numpy(_UpperCAmelCase							)
          else:
             lowercase__:   int					=      [to_numpy(_UpperCAmelCase							) for v in value]

                # Convert padding_strategy in PaddingStrategy
       lowercase__:   Any					=      self._get_padding_strategies(padding=_UpperCAmelCase    ,			max_length=_UpperCAmelCase							)

       lowercase__:   Tuple					=      processed_features[self.model_input_names[0]]

       lowercase__:   List[Any]					=      len(_UpperCAmelCase							)
       if not all(len(_UpperCAmelCase							) == batch_size for v in processed_features.values()							):
          raise ValueError('''Some items in the output dictionary have a different batch size than others.'''							)

       lowercase__:   str					=      []
       for i in range(_UpperCAmelCase							):
          lowercase__:   List[str]					=      {k: v[i] for k, v in processed_features.items()}
          # truncation
          lowercase__:   Dict					=      self._truncate(
              _UpperCAmelCase    ,			max_length=_UpperCAmelCase    ,			pad_to_multiple_of=_UpperCAmelCase    ,			truncation=_UpperCAmelCase    ,			)
          truncated_inputs.append(_UpperCAmelCase							)

       if padding_strategy == PaddingStrategy.LONGEST:
          # make sure that `max_length` cannot be longer than the longest truncated length
          lowercase__:   Dict					=      max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
          lowercase__:   Dict					=      PaddingStrategy.MAX_LENGTH

       lowercase__:   List[Any]					=      {}
       for i in range(_UpperCAmelCase							):
          # padding
          lowercase__:   Dict					=      self._pad(
              truncated_inputs[i]    ,			max_length=_UpperCAmelCase    ,			padding_strategy=_UpperCAmelCase    ,			pad_to_multiple_of=_UpperCAmelCase    ,			return_attention_mask=_UpperCAmelCase    ,			)

          for key, value in outputs.items():
             if key not in batch_outputs:
                lowercase__:   str					=      []
             if value.dtype is np.dtype(np.floataa							):
                lowercase__:   Tuple					=      value.astype(np.floataa							)
             batch_outputs[key].append(_UpperCAmelCase							)

       return BatchFeature(_UpperCAmelCase    ,			tensor_type=_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = PaddingStrategy.DO_NOT_PAD    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       lowercase__:   Any					=      processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
          lowercase__:   Optional[int]					=      len(_UpperCAmelCase							)

       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          lowercase__:   int					=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       lowercase__:   Union[str, Any]					=      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCAmelCase							) < max_length

       if return_attention_mask and "attention_mask" not in processed_features:
          lowercase__:   Tuple					=      np.ones(len(_UpperCAmelCase							)    ,			dtype=np.intaa							)

       if needs_to_be_padded:
          lowercase__:   Dict					=      max_length - len(_UpperCAmelCase							)
          if self.padding_side == "right":
             if return_attention_mask:
                lowercase__:   List[Any]					=      np.pad(
                    processed_features['''attention_mask''']    ,			(0, difference)							)
             lowercase__:   Tuple					=      ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             lowercase__:   int					=      np.pad(
                 _UpperCAmelCase    ,			_UpperCAmelCase    ,			'''constant'''    ,			constant_values=self.padding_value							)
          elif self.padding_side == "left":
             if return_attention_mask:
                lowercase__:   int					=      np.pad(
                    processed_features['''attention_mask''']    ,			(difference, 0)							)
             lowercase__:   Any					=      ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             lowercase__:   Optional[Any]					=      np.pad(
                 _UpperCAmelCase    ,			_UpperCAmelCase    ,			'''constant'''    ,			constant_values=self.padding_value							)
          else:
             raise ValueError('''Invalid padding strategy:''' + str(self.padding_side							)							)

       return processed_features






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       if not truncation:
          return processed_features
       elif truncation and max_length is None:
          raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''							)

       lowercase__:   List[Any]					=      processed_features[self.model_input_names[0]]

       # find `max_length` that fits `pad_to_multiple_of`
       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          lowercase__:   Tuple					=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       lowercase__:   Optional[Any]					=      len(_UpperCAmelCase							) > max_length

       if needs_to_be_truncated:
          lowercase__:   Union[str, Any]					=      processed_features[self.model_input_names[0]][:max_length]
          if "attention_mask" in processed_features:
             lowercase__:   List[Any]					=      processed_features['''attention_mask'''][:max_length]

       return processed_features






    def _snake_case			(   self    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=None							):

       # Get padding strategy
       if padding is not False:
          if padding is True:
             lowercase__:   Dict					=      PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
          elif not isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      PaddingStrategy(_UpperCAmelCase							)
          elif isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Any					=      padding
       else:
          lowercase__:   int					=      PaddingStrategy.DO_NOT_PAD

       # Set max length if needed
       if max_length is None:
          if padding_strategy == PaddingStrategy.MAX_LENGTH:
             raise ValueError(
                 F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""							)

                # Test if we have a padding value
       if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
          raise ValueError(
              '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
              ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''							)

       return padding_strategy




'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  BlenderbotSmallTokenizer
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_      =  ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
   lowerCamelCase_      =  dict(zip(UpperCamelCase			,  range(len(UpperCamelCase     )     )     )     )

   lowerCamelCase_      =  ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
   lowerCamelCase_      =  {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]     )
   with open(self.vocab_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write(json.dumps(UpperCamelCase     ) + "\n"     )
   with open(self.merges_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write("\n".join(UpperCamelCase     )     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map     )
   return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  "adapt act apte"
   return input_text, output_text






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map     )
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  ["adapt", "act", "ap@@", "te"]
   lowerCamelCase_      =  tokenizer.tokenize(UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

   lowerCamelCase_      =  [0, 1, 2, 3, 4, 5]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase     )			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   assert tok("sam"     ).input_ids == [1384]
   lowerCamelCase_      =  "I am a small frog."
   lowerCamelCase_      =  tok([src_text]			,  padding=UpperCamelCase			,  truncation=UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase			,  clean_up_tokenization_spaces=UpperCamelCase     )[0]
   assert src_text != decoded  # I wish it did!
   assert decoded == "i am a small frog ."







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   lowerCamelCase_      =  "I am a small frog ."
   lowerCamelCase_      =  "."
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]

   assert encoded[-1] == encoded_dot[0]



