


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    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							List[Any]       =LEDTokenizer
							UpperCamelCase_   :							List[Any]       =LEDTokenizerFast
							UpperCamelCase_   :							Tuple       =True


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

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


							def 			_A       (self      ,				**lowerCAmelCase					):
									kwargs.update(self.special_tokens_map					)
									return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				**lowerCAmelCase					):
									kwargs.update(self.special_tokens_map					)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase					):
									return "lower newer", "lower newer"


							@cached_property
							def 			_A       (self					):
									return LEDTokenizer.from_pretrained('allenai/led-base-16384'					)


							@cached_property
							def 			_A       (self					):
									return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'					)


							@require_torch
							def 			_A       (self					):
									__lowercase=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
									__lowercase=			[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]:
											__lowercase=			tokenizer(lowerCAmelCase      ,				max_length=len(lowerCAmelCase					)      ,				padding=lowerCAmelCase      ,				return_tensors='pt'					)
											self.assertIsInstance(lowerCAmelCase      ,				lowerCAmelCase					)

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


							@require_torch
							def 			_A       (self					):
									__lowercase=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase=			tokenizer(lowerCAmelCase      ,				padding=lowerCAmelCase      ,				return_tensors='pt'					)
											self.assertIn('input_ids'      ,				lowerCAmelCase					)
											self.assertIn('attention_mask'      ,				lowerCAmelCase					)
											self.assertNotIn('labels'      ,				lowerCAmelCase					)
											self.assertNotIn('decoder_attention_mask'      ,				lowerCAmelCase					)


							@require_torch
							def 			_A       (self					):
									__lowercase=			[
									    'Summary of the text.',
									    'Another summary.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase=			tokenizer(text_target=lowerCAmelCase      ,				max_length=3_2      ,				padding='max_length'      ,				return_tensors='pt'					)
											self.assertEqual(3_2      ,				targets['input_ids'].shape[1]					)


							@require_torch
							def 			_A       (self					):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase=			tokenizer(
											    ['I am a small frog' * 1_0_2_4, 'I am a small frog']      ,				padding=lowerCAmelCase      ,				truncation=lowerCAmelCase      ,				return_tensors='pt'					)
											self.assertIsInstance(lowerCAmelCase      ,				lowerCAmelCase					)
											self.assertEqual(batch.input_ids.shape      ,				(2, 5_1_2_2)					)


							@require_torch
							def 			_A       (self					):
									__lowercase=			['A long paragraph for summarization.']
									__lowercase=			[
									    'Summary of the text.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase=			tokenizer(lowerCAmelCase      ,				return_tensors='pt'					)
											__lowercase=			tokenizer(text_target=lowerCAmelCase      ,				return_tensors='pt'					)
											__lowercase=			inputs['input_ids']
											__lowercase=			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					):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase=			['Summary of the text.', 'Another summary.']
											__lowercase=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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


							def 			_A       (self					):
									pass







							def 			_A       (self					):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
													__lowercase=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase      ,				**lowerCAmelCase					)
													__lowercase=			self.tokenizer_class.from_pretrained(lowerCAmelCase      ,				**lowerCAmelCase					)
													__lowercase=			'A, <mask> AllenNLP sentence.'
													__lowercase=			tokenizer_r.encode_plus(lowerCAmelCase      ,				add_special_tokens=lowerCAmelCase      ,				return_token_type_ids=lowerCAmelCase					)
													__lowercase=			tokenizer_p.encode_plus(lowerCAmelCase      ,				add_special_tokens=lowerCAmelCase      ,				return_token_type_ids=lowerCAmelCase					)
													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']					)      ,				)

													__lowercase=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
													__lowercase=			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(
													    lowerCAmelCase      ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
													self.assertSequenceEqual(
													    lowerCAmelCase      ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)










from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






