






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 			_UpperCamelCase(       __lowerCamelCase		,				unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							LEDTokenizer
		__SCREAMING_SNAKE_CASE  :       List[str]							=							LEDTokenizerFast
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							True

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



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

					__a						:  List[str]									=     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['vocab_file']    )
					__a						:  Optional[int]									=     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		:    Union[str, Any]    ,       **SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    str    ,       **SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					return "lower newer", "lower newer"

		@cached_property
		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



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

		@cached_property
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



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

		@require_torch
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  str									=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__a						:  int									=     [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Optional[int]									=     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						:  int									=     batch.input_ids.tolist()[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

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



					'''simple docstring'''



					__a						:  Any									=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Dict									=     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		:    Dict    ):



					'''simple docstring'''



					__a						:  int									=     [
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Optional[int]									=     tokenizer(text_target=SCREAMING_SNAKE_CASE__    ,       max_length=3_2    ,       padding='max_length'    ,       return_tensors='pt'    )
								self.assertEqual(3_2    ,       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						:  List[Any]									=     tokenizer(
								    ['I am a small frog' * 1_0_2_4, '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_1_2_2)    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



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



					'''simple docstring'''



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

								__a						:  Union[str, Any]									=     tokenizer(SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    )
								__a						:  Dict									=     [[0] * len(SCREAMING_SNAKE_CASE__    ) for x in encoded_output['input_ids']]
								__a						:  Optional[int]									=     tokenizer.pad(SCREAMING_SNAKE_CASE__    )
								self.assertSequenceEqual(outputs['global_attention_mask']    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					pass



		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
											__a						:  Tuple									=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
											__a						:  str									=     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
											__a						:  Optional[Any]									=     'A, <mask> AllenNLP sentence.'
											__a						:  Dict									=     tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
											__a						:  Dict									=     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						:  Dict									=     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
											__a						:  List[str]									=     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )
											self.assertSequenceEqual(tokens_p['input_ids']    ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
											self.assertSequenceEqual(tokens_r['input_ids']    ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A__						:   Tuple				=    logging.get_logger(__name__)

A__						:   Dict				=    {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     'rwkv'
    lowerCamelCase    :   Any					=     {'max_position_embeddings': 'context_length'}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_77     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=40_96     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						vocab_size
         __lowerCamelCase  :      Tuple														=						context_length
         __lowerCamelCase  :      str														=						hidden_size
         __lowerCamelCase  :      List[str]														=						num_hidden_layers
         __lowerCamelCase  :      Any														=						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __lowerCamelCase  :      Optional[int]														=						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __lowerCamelCase  :      Optional[Any]														=						layer_norm_epsilon
         __lowerCamelCase  :      int														=						rescale_every
         __lowerCamelCase  :      Tuple														=						use_cache

         __lowerCamelCase  :      int														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						eos_token_id

         super().__init__(
             tie_word_embeddings=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






