



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Any											=						LEDTokenizer
       UpperCamelCase__  :    str											=						LEDTokenizerFast
       UpperCamelCase__  :    List[Any]											=						True



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           super().setUp()
           __a						       =				[
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''<unk>''',
           ]
           __a						       =				dict(zip(__SCREAMING_SNAKE_CASE     ,		range(len(__SCREAMING_SNAKE_CASE))))
           __a						       =				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           __a						       =				{'''unk_token''': '''<unk>'''}

           __a						       =				os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''vocab_file'''])
           __a						       =				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(__SCREAMING_SNAKE_CASE) + '''\n''')
           with open(self.merges_file     ,		'''w'''     ,		encoding='''utf-8''') as fp:
               fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           return "lower newer", "lower newer"



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return LEDTokenizer.from_pretrained('''allenai/led-base-16384''')



       @cached_property
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''')



       @require_torch
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           __a						       =				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		max_length=len(__SCREAMING_SNAKE_CASE)     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               self.assertEqual((2, 9)     ,		batch.input_ids.shape)
               self.assertEqual((2, 9)     ,		batch.attention_mask.shape)
               __a						       =				batch.input_ids.tolist()[0]
               self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIn('''input_ids'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertIn('''attention_mask'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''labels'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''decoder_attention_mask'''     ,		__SCREAMING_SNAKE_CASE)



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		max_length=32     ,		padding='''max_length'''     ,		return_tensors='''pt''')
               self.assertEqual(32     ,		targets['''input_ids'''].shape[1])



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


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(
                   ['''I am a small frog''' * 1_024, '''I am a small frog''']     ,		padding=__SCREAMING_SNAKE_CASE     ,		truncation=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(batch.input_ids.shape     ,		(2, 5_122))



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''']
           __a						       =				[
               '''Summary of the text.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				inputs['''input_ids''']
               __a						       =				targets['''input_ids''']
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



       @require_torch
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				['''Summary of the text.''', '''Another summary.''']
               __a						       =				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE)
               __a						       =				[[0] * len(__SCREAMING_SNAKE_CASE) for x in encoded_output['''input_ids''']]
               __a						       =				tokenizer.pad(__SCREAMING_SNAKE_CASE)
               self.assertSequenceEqual(outputs['''global_attention_mask''']     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           pass





       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                   __a						       =				self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				'''A, <mask> AllenNLP sentence.'''
                   __a						       =				tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   __a						       =				tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   self.assertEqual(sum(tokens_r['''token_type_ids'''])     ,		sum(tokens_p['''token_type_ids''']))
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask'''])     ,		sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask'''])     ,		)

                   __a						       =				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                   __a						       =				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])
                   self.assertSequenceEqual(tokens_p['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                   self.assertSequenceEqual(tokens_r['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

                   self.assertSequenceEqual(
                       __SCREAMING_SNAKE_CASE     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])
                   self.assertSequenceEqual(
                       __SCREAMING_SNAKE_CASE     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])









"""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__				:							Union[str, Any]						=     logging.get_logger(__name__)




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				float			,			**UpperCAmelCase__			:				List[str]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				feature_size
						__SCREAMING_SNAKE_CASE						   =				sampling_rate
						__SCREAMING_SNAKE_CASE						   =				padding_value

						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("padding_side"			,			"right"   )
						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("return_attention_mask"			,			UpperCAmelCase__   )

						super().__init__(**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[
	        BatchFeature,
	        List[BatchFeature],
	        Dict[str, BatchFeature],
	        Dict[str, List[BatchFeature]],
	        List[Dict[str, BatchFeature]],
	    ]			,			UpperCAmelCase__			:				Union[bool, str, PaddingStrategy] = True			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				bool = False			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			UpperCAmelCase__			:				Optional[Union[str, TensorType]] = 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(UpperCAmelCase__			,			(list, tuple)   ) and isinstance(processed_features[0]			,			(dict, BatchFeature)   ):
											__SCREAMING_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()   )}"""   )

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

						if len(UpperCAmelCase__   ) == 0:
											if return_attention_mask:
																__SCREAMING_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

						__SCREAMING_SNAKE_CASE						   =				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.
											__SCREAMING_SNAKE_CASE						   =				0
											while len(required_input[index]   ) == 0:
																index += 1
											if index < len(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				required_input[index][0]

						if return_tensors is None:
											if is_tf_tensor(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				"tf"
											elif is_torch_tensor(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				"pt"
											elif isinstance(UpperCAmelCase__			,			(int, float, list, tuple, np.ndarray)   ):
																__SCREAMING_SNAKE_CASE						   =				"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)   ):
																__SCREAMING_SNAKE_CASE						   =				to_numpy(UpperCAmelCase__   )
											else:
																__SCREAMING_SNAKE_CASE						   =				[to_numpy(UpperCAmelCase__   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
						__SCREAMING_SNAKE_CASE						   =				self._get_padding_strategies(padding=UpperCAmelCase__			,			max_length=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]

						__SCREAMING_SNAKE_CASE						   =				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."   )

						__SCREAMING_SNAKE_CASE						   =				[]
						for i in range(UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				{k: v[i] for k, v in processed_features.items()}
											# truncation
											__SCREAMING_SNAKE_CASE						   =				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
											__SCREAMING_SNAKE_CASE						   =				max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
											__SCREAMING_SNAKE_CASE						   =				PaddingStrategy.MAX_LENGTH

						__SCREAMING_SNAKE_CASE						   =				{}
						for i in range(UpperCAmelCase__   ):
											# padding
											__SCREAMING_SNAKE_CASE						   =				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:
																					__SCREAMING_SNAKE_CASE						   =				[]
																if value.dtype is np.dtype(np.floataa   ):
																					__SCREAMING_SNAKE_CASE						   =				value.astype(np.floataa   )
																batch_outputs[key].append(UpperCAmelCase__   )

						return BatchFeature(UpperCAmelCase__			,			tensor_type=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[Dict[str, np.ndarray], BatchFeature]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			)       ->	dict:
						__SCREAMING_SNAKE_CASE						   =				processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
											__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   )

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

						__SCREAMING_SNAKE_CASE						   =				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase__   ) < max_length

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

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

						return processed_features




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Union[Dict[str, np.ndarray], BatchFeature]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[bool] = None			,			)       ->	str:
						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."   )

						__SCREAMING_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):
											__SCREAMING_SNAKE_CASE						   =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   ) > max_length

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

						return processed_features




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Optional[Any]=False			,			UpperCAmelCase__			:				Optional[int]=None   )       ->	str:

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