





'''simple docstring'''







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowercase						(       __a				,		unittest.TestCase					):
					_UpperCAmelCase										=					None
					_UpperCAmelCase										=					BloomTokenizerFast
					_UpperCAmelCase										=					BloomTokenizerFast
					_UpperCAmelCase										=					True
					_UpperCAmelCase										=					False
					_UpperCAmelCase										=					'''tokenizer_file'''
					_UpperCAmelCase										=					{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}







					def     UpperCamelCase			( self    ) ->	Dict:
						super().setUp()
						snake_case				=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''    )
						tokenizer.save_pretrained(self.tmpdirname    )







					def     UpperCamelCase			( self    ,   **A__    ) ->	Optional[Any]:
						kwargs.update(self.special_tokens_map    )
						return BloomTokenizerFast.from_pretrained(self.tmpdirname    ,   **A__    )







					def     UpperCamelCase			( self    ) ->	List[Any]:
						snake_case				=			self.get_rust_tokenizer()

						snake_case				=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
						snake_case				=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

						snake_case				=			tokenizer.batch_encode_plus(A__    )['''input_ids''']
						self.assertListEqual(A__    ,   A__    )

						snake_case				=			tokenizer.batch_decode(A__    )
						self.assertListEqual(A__    ,   A__    )







					def     UpperCamelCase			( self    ,   A__=6    ) ->	Any:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
								snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )
								# tokenizer_r.pad_token = None # Hotfixing padding = None
								# Simple input
								snake_case				=			'''This is a simple input'''
								snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']
								snake_case				=			('''This is a simple input''', '''This is a pair''')
								snake_case				=			[
								    ('''This is a simple input 1''', '''This is a simple input 2'''),
								    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
								]

								# Simple input tests
								try:
									tokenizer_r.encode(A__    ,   max_length=A__    )
									tokenizer_r.encode_plus(A__    ,   max_length=A__    )

									tokenizer_r.batch_encode_plus(A__    ,   max_length=A__    )
									tokenizer_r.encode(A__    ,   max_length=A__    )
									tokenizer_r.batch_encode_plus(A__    ,   max_length=A__    )
								except ValueError:
									self.fail('''Bloom Tokenizer should be able to deal with padding'''    )

								snake_case				=			None  # Hotfixing padding = None
								self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

								# Simple input
								self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

								# Simple input
								self.assertRaises(
								    A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )

								# Pair input
								self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

								# Pair input
								self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

								# Pair input
								self.assertRaises(
								    A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )







					def     UpperCamelCase			( self    ) ->	Any:
						snake_case				=			self.get_rust_tokenizer()
						snake_case				=			load_dataset('''xnli'''    ,   '''all_languages'''    ,   split='''test'''    ,   streaming=A__    )

						snake_case				=			next(iter(A__    )    )['''premise''']  # pick up one data
						snake_case				=			list(sample_data.values()    )

						snake_case				=			list(map(tokenizer.encode    ,   A__    )    )
						snake_case				=			[tokenizer.decode(A__    ,   clean_up_tokenization_spaces=A__    ) for x in output_tokens]
						self.assertListEqual(A__    ,   A__    )







					def     UpperCamelCase			( self    ) ->	Optional[Any]:
						# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
						# any sequence length constraints. This test of the parent class will fail since it relies on the
						# maximum sequence length of the positoonal embeddings.
						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map    )    ,   1    )
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()    )[0]    )    ,   1    )








'''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


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # 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(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           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()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       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

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

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

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

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

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

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

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

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

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

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      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.'''    )

      snake_case				=			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):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			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

