





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      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		42





class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = ("DownEncoderBlock2D",)			,  lowerCAmelCase_ = ("UpDecoderBlock2D",)			,  lowerCAmelCase_ = (64,)			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "silu"			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 32			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 32			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = 0.1_82_15			,  lowerCAmelCase_ = "group"			,  )						->							Tuple:
										super().__init__()

										# pass init params to Encoder
										_snake_case													=			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_			,  )

										_snake_case													=			vq_embed_dim if vq_embed_dim is not None else latent_channels

										_snake_case													=			nn.Convad(lowerCAmelCase_			,  lowerCAmelCase_			,  1					)
										_snake_case													=			VectorQuantizer(lowerCAmelCase_			,  lowerCAmelCase_			,  beta=0.25			,  remap=lowerCAmelCase_			,  sane_index_shape=lowerCAmelCase_					)
										_snake_case													=			nn.Convad(lowerCAmelCase_			,  lowerCAmelCase_			,  1					)

										# pass init params to Decoder
										_snake_case													=			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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = True					)						->							VQEncoderOutput:
										_snake_case													=			self.encoder(lowerCAmelCase_					)
										_snake_case													=			self.quant_conv(lowerCAmelCase_					)

										if not return_dict:
															return (h,)

										return VQEncoderOutput(latents=lowerCAmelCase_					)




					@apply_forward_hook
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = True					)						->							Union[DecoderOutput, torch.FloatTensor]:
										# also go through quantization layer
										if not force_not_quantize:
															_snake_case							,       _snake_case							,       _snake_case													=			self.quantize(lowerCAmelCase_					)
										else:
															_snake_case													=			h
										_snake_case													=			self.post_quant_conv(lowerCAmelCase_					)
										_snake_case													=			self.decoder(lowerCAmelCase_			,  quant if self.config.norm_type == 'spatial' else None					)

										if not return_dict:
															return (dec,)

										return DecoderOutput(sample=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = True					)						->							Union[DecoderOutput, torch.FloatTensor]:
										_snake_case													=			sample
										_snake_case													=			self.encode(lowerCAmelCase_					).latents
										_snake_case													=			self.decode(lowerCAmelCase_					).sample

										if not return_dict:
															return (dec,)

										return DecoderOutput(sample=lowerCAmelCase_					)










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   ):
					lowerCAmelCase_							=		LEDTokenizer
					lowerCAmelCase_							=		LEDTokenizerFast
					lowerCAmelCase_							=		True




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

										_snake_case													=			os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case													=			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 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							List[str]:
										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							str:
										kwargs.update(self.special_tokens_map					)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										return "lower newer", "lower newer"




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return LEDTokenizer.from_pretrained('allenai/led-base-16384'					)




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										_snake_case													=			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			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					)
															_snake_case													=			batch.input_ids.tolist()[0]
															self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			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 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case													=			[
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(text_target=lowerCAmelCase_			,  max_length=32			,  padding='max_length'			,  return_tensors='pt'					)
															self.assertEqual(32			,  targets['input_ids'].shape[1]					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(
															    ['I am a small frog' * 1024, 'I am a small frog']			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIsInstance(lowerCAmelCase_			,  lowerCAmelCase_					)
															self.assertEqual(batch.input_ids.shape			,  (2, 5122)					)




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

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




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										pass






					def 		lowerCAmelCase     (						self					)						->							str:
										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(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			self.tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			'A, <mask> AllenNLP sentence.'
																				_snake_case													=			tokenizer_r.encode_plus(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_					)
																				_snake_case													=			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']					)			,  )

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



