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       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	LEDTokenizer
						UpperCamelCase_  =	LEDTokenizerFast
						UpperCamelCase_  =	True


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												SCREAMING_SNAKE_CASE							:					str           =							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  __A ( self			:       Dict					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       List[Any]					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





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


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												return "lower newer", "lower newer"


						@cached_property
						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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


						@cached_property
						def  __A ( self			:       Dict		):





												'''simple docstring'''





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


						@require_torch
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												SCREAMING_SNAKE_CASE							:					int           =							[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

																		self.assertEqual((2, 9)					,	batch.input_ids.shape		)
																		self.assertEqual((2, 9)					,	batch.attention_mask.shape		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							batch.input_ids.tolist()[0]
																		self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)


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





												'''simple docstring'''





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


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





												'''simple docstring'''





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


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





												'''simple docstring'''





												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer(
																		    ['''I am a small frog''' * 1024, '''I am a small frog''']					,	padding=UpperCamelCase__					,	truncation=UpperCamelCase__					,	return_tensors='''pt'''		)
																		self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)
																		self.assertEqual(batch.input_ids.shape					,	(2, 5122)		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							['''A long paragraph for summarization.''']
												SCREAMING_SNAKE_CASE							:					Any           =							[
												    '''Summary of the text.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer(UpperCamelCase__					,	return_tensors='''pt'''		)
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer(text_target=UpperCamelCase__					,	return_tensors='''pt'''		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							inputs['''input_ids''']
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							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  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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

																		SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[[0] * len(UpperCamelCase__		) for x in encoded_output['''input_ids''']]
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.pad(UpperCamelCase__		)
																		self.assertSequenceEqual(outputs['''global_attention_mask''']					,	UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												pass



						def  __A ( self			:       Any		):





												'''simple docstring'''





												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							self.rust_tokenizer_class.from_pretrained(UpperCamelCase__					,	**UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							self.tokenizer_class.from_pretrained(UpperCamelCase__					,	**UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''A, <mask> AllenNLP sentence.'''
																								SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer_r.encode_plus(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__					,	return_token_type_ids=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Any           =							tokenizer_p.encode_plus(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__					,	return_token_type_ids=UpperCamelCase__		)
																								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''']		)					,	)

																								SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																								SCREAMING_SNAKE_CASE							:					int           =							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)
																								self.assertSequenceEqual(tokens_p['''input_ids''']					,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
																								self.assertSequenceEqual(tokens_r['''input_ids''']					,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

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






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							process
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							params


						def __len__( self			:       Tuple		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset[i]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.process(UpperCamelCase__					,	**self.params		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]=None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							loader
												SCREAMING_SNAKE_CASE							:					List[Any]           =							infer
												SCREAMING_SNAKE_CASE							:					int           =							params
												if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							loader_batch_size

												# Internal bookkeeping
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							None


						def __len__( self			:       int		):





												'''simple docstring'''





												return len(self.loader		)


						def __iter__( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							iter(self.loader		)
												return self


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if isinstance(self._loader_batch_data					,	torch.Tensor		):
																		# Batch data is simple tensor, just fetch the slice
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._loader_batch_data[self._loader_batch_index]
												else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
																		for k, element in self._loader_batch_data.items():
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Convert ModelOutput to tuple first
																														SCREAMING_SNAKE_CASE							:					Dict           =							element.to_tuple()
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Dict           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Those are stored as lists of tensors so need specific unbatching.
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					List[str]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if element is None:
																														# This can happen for optional data that get passed around
																														SCREAMING_SNAKE_CASE							:					int           =							None
																								elif isinstance(element[self._loader_batch_index]					,	torch.Tensor		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers

																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							element[self._loader_batch_index].unsqueeze(0		)
																								elif isinstance(element[self._loader_batch_index]					,	np.ndarray		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers
																														SCREAMING_SNAKE_CASE							:					Tuple           =							np.expand_dims(element[self._loader_batch_index]					,	0		)
																								else:
																														# This is typically a list, so no need to `unsqueeze`.
																														SCREAMING_SNAKE_CASE							:					Tuple           =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		SCREAMING_SNAKE_CASE							:					Any           =							self._loader_batch_data.__class__(UpperCamelCase__		)
												self._loader_batch_index += 1
												return result



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		# We are currently unrolling a batch so we just need to return
																		# the current item within a batch
																		return self.loader_batch_item()

												# We're out of items within a batch
												SCREAMING_SNAKE_CASE							:					Tuple           =							next(self.iterator		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.infer(UpperCamelCase__					,	**self.params		)
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																		# Try to infer the size of the batch
																		if isinstance(UpperCamelCase__					,	torch.Tensor		):
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							processed
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							list(processed.keys()		)[0]
																								SCREAMING_SNAKE_CASE							:					int           =							processed[key]
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							first_tensor.shape[0]
																		if 0 < observed_batch_size < self.loader_batch_size:
																								# could be last batch so we can't unroll as many
																								# elements.
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							observed_batch_size
																		# Setting internal index to unwrap the batch
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																		SCREAMING_SNAKE_CASE							:					int           =							0
																		return self.loader_batch_item()
												else:
																		# We're not unrolling batches
																		return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]=None		):





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def __iter__( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							iter(self.loader		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if self.subiterator is None:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.infer(next(self.iterator		)					,	**self.params		)
												try:
																		# Try to return next item
																		SCREAMING_SNAKE_CASE							:					Any           =							next(self.subiterator		)
												except StopIteration:
																		# When a preprocess iterator ends, we can start lookig at the next item
																		# ChunkIterator will keep feeding until ALL elements of iterator
																		# all have created their subiterator and have been iterating against.
																		#
																		# Another way to look at it, is we're basically flattening lists of lists
																		# into a single list, but with generators
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.infer(next(self.iterator		)					,	**self.params		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							next(self.subiterator		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							iter(self.loader		)
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		while self._loader_batch_index < self.loader_batch_size:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.loader_batch_item()
																								SCREAMING_SNAKE_CASE							:					Any           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
																								if is_last:
																														return accumulator

												while not is_last:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.infer(next(self.iterator		)					,	**self.params		)
																		if self.loader_batch_size is not None:
																								if isinstance(UpperCamelCase__					,	torch.Tensor		):
																														SCREAMING_SNAKE_CASE							:					Tuple           =							processed
																								else:
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							list(processed.keys()		)[0]
																														SCREAMING_SNAKE_CASE							:					List[str]           =							processed[key]
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																								else:
																														SCREAMING_SNAKE_CASE							:					int           =							first_tensor.shape[0]
																								if 0 < observed_batch_size < self.loader_batch_size:
																														# could be last batch so we can't unroll as many
																														# elements.
																														SCREAMING_SNAKE_CASE							:					List[str]           =							observed_batch_size
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																								SCREAMING_SNAKE_CASE							:					str           =							0
																								while self._loader_batch_index < self.loader_batch_size:
																														SCREAMING_SNAKE_CASE							:					Any           =							self.loader_batch_item()
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							item.pop('''is_last'''		)
																														accumulator.append(UpperCamelCase__		)
																														if is_last:
																																				return accumulator
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							processed
																								SCREAMING_SNAKE_CASE							:					List[str]           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
												return accumulator






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Dict           =							key


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.dataset[i][self.key]






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							dataset
												SCREAMING_SNAKE_CASE							:					List[str]           =							keya
												SCREAMING_SNAKE_CASE							:					Tuple           =							keya


						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





