



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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class     __a			(  a_					):
	SCREAMING_SNAKE_CASE  	=							42






class     __a			(  a_						,     a_					):



	@register_to_config
	def __init__(    self				:     Union[str, Any]					,						snake_case_				:     int = 6_55_36					,						snake_case_				:     Optional[int] = None					,						snake_case_				:     int = 2					,						snake_case_				:     int = 2					,						snake_case_				:     int = 0					,						snake_case_				:     str = "fourier"					,						snake_case_				:     bool = True					,						snake_case_				:     bool = False					,						snake_case_				:     float = 0.0					,						snake_case_				:     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,						snake_case_				:     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,						snake_case_				:     Tuple[str] = "UNetMidBlock1D"					,						snake_case_				:     str = None					,						snake_case_				:     Tuple[int] = (32, 32, 64)					,						snake_case_				:     str = None					,						snake_case_				:     int = 8					,						snake_case_				:     int = 1					,						snake_case_				:     bool = False					,						)->				List[str]:
						super().__init__()
						__lowerCAmelCase						 =sample_size

						# time
						if time_embedding_type == "fourier":
											__lowerCAmelCase						 =GaussianFourierProjection(
											    embedding_size=8					,						set_W_to_weight=__SCREAMING_SNAKE_CASE					,						log=__SCREAMING_SNAKE_CASE					,						flip_sin_to_cos=__SCREAMING_SNAKE_CASE)
											__lowerCAmelCase						 =2 * block_out_channels[0]
						elif time_embedding_type == "positional":
											__lowerCAmelCase						 =Timesteps(
											    block_out_channels[0]					,						flip_sin_to_cos=__SCREAMING_SNAKE_CASE					,						downscale_freq_shift=__SCREAMING_SNAKE_CASE)
											__lowerCAmelCase						 =block_out_channels[0]

						if use_timestep_embedding:
											__lowerCAmelCase						 =block_out_channels[0] * 4
											__lowerCAmelCase						 =TimestepEmbedding(
											    in_channels=__SCREAMING_SNAKE_CASE					,						time_embed_dim=__SCREAMING_SNAKE_CASE					,						act_fn=__SCREAMING_SNAKE_CASE					,						out_dim=block_out_channels[0]					,						)

						__lowerCAmelCase						 =nn.ModuleList([])
						__lowerCAmelCase						 =None
						__lowerCAmelCase						 =nn.ModuleList([])
						__lowerCAmelCase						 =None

						# down
						__lowerCAmelCase						 =in_channels
						for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE):
											__lowerCAmelCase						 =output_channel
											__lowerCAmelCase						 =block_out_channels[i]

											if i == 0:
																input_channel += extra_in_channels

											__lowerCAmelCase						 =i == len(__SCREAMING_SNAKE_CASE) - 1

											__lowerCAmelCase						 =get_down_block(
											    __SCREAMING_SNAKE_CASE					,						num_layers=__SCREAMING_SNAKE_CASE					,						in_channels=__SCREAMING_SNAKE_CASE					,						out_channels=__SCREAMING_SNAKE_CASE					,						temb_channels=block_out_channels[0]					,						add_downsample=not is_final_block or downsample_each_block					,						)
											self.down_blocks.append(__SCREAMING_SNAKE_CASE)

						# mid
						__lowerCAmelCase						 =get_mid_block(
						    __SCREAMING_SNAKE_CASE					,						in_channels=block_out_channels[-1]					,						mid_channels=block_out_channels[-1]					,						out_channels=block_out_channels[-1]					,						embed_dim=block_out_channels[0]					,						num_layers=__SCREAMING_SNAKE_CASE					,						add_downsample=__SCREAMING_SNAKE_CASE					,						)

						# up
						__lowerCAmelCase						 =list(reversed(__SCREAMING_SNAKE_CASE))
						__lowerCAmelCase						 =reversed_block_out_channels[0]
						if out_block_type is None:
											__lowerCAmelCase						 =out_channels
						else:
											__lowerCAmelCase						 =block_out_channels[0]

						for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE):
											__lowerCAmelCase						 =output_channel
											__lowerCAmelCase						 =(
											    reversed_block_out_channels[i + 1] if i < len(__SCREAMING_SNAKE_CASE) - 1 else final_upsample_channels
											)

											__lowerCAmelCase						 =i == len(__SCREAMING_SNAKE_CASE) - 1

											__lowerCAmelCase						 =get_up_block(
											    __SCREAMING_SNAKE_CASE					,						num_layers=__SCREAMING_SNAKE_CASE					,						in_channels=__SCREAMING_SNAKE_CASE					,						out_channels=__SCREAMING_SNAKE_CASE					,						temb_channels=block_out_channels[0]					,						add_upsample=not is_final_block					,						)
											self.up_blocks.append(__SCREAMING_SNAKE_CASE)
											__lowerCAmelCase						 =output_channel

						# out
						__lowerCAmelCase						 =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,						32)
						__lowerCAmelCase						 =get_out_block(
						    out_block_type=__SCREAMING_SNAKE_CASE					,						num_groups_out=__SCREAMING_SNAKE_CASE					,						embed_dim=block_out_channels[0]					,						out_channels=__SCREAMING_SNAKE_CASE					,						act_fn=__SCREAMING_SNAKE_CASE					,						fc_dim=block_out_channels[-1] // 4					,						)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     torch.FloatTensor					,						snake_case_				:     Union[torch.Tensor, float, int]					,						snake_case_				:     bool = True					,						)->				Optional[Any]:
						__lowerCAmelCase						 =timestep
						if not torch.is_tensor(__SCREAMING_SNAKE_CASE):
											__lowerCAmelCase						 =torch.tensor([timesteps]					,						dtype=torch.long					,						device=sample.device)
						elif torch.is_tensor(__SCREAMING_SNAKE_CASE) and len(timesteps.shape) == 0:
											__lowerCAmelCase						 =timesteps[None].to(sample.device)

						__lowerCAmelCase						 =self.time_proj(__SCREAMING_SNAKE_CASE)
						if self.config.use_timestep_embedding:
											__lowerCAmelCase						 =self.time_mlp(__SCREAMING_SNAKE_CASE)
						else:
											__lowerCAmelCase						 =timestep_embed[..., None]
											__lowerCAmelCase						 =timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
											__lowerCAmelCase						 =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

						# 2. down
						__lowerCAmelCase						 =()
						for downsample_block in self.down_blocks:
											__lowerCAmelCase ,						__lowerCAmelCase						 =downsample_block(hidden_states=__SCREAMING_SNAKE_CASE					,						temb=__SCREAMING_SNAKE_CASE)
											down_block_res_samples += res_samples

						# 3. mid
						if self.mid_block:
											__lowerCAmelCase						 =self.mid_block(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE)

						# 4. up
						for i, upsample_block in enumerate(self.up_blocks):
											__lowerCAmelCase						 =down_block_res_samples[-1:]
											__lowerCAmelCase						 =down_block_res_samples[:-1]
											__lowerCAmelCase						 =upsample_block(__SCREAMING_SNAKE_CASE					,						res_hidden_states_tuple=__SCREAMING_SNAKE_CASE					,						temb=__SCREAMING_SNAKE_CASE)

						# 5. post-process
						if self.out_block:
											__lowerCAmelCase						 =self.out_block(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE)

						if not return_dict:
											return (sample,)

						return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE)





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,				"<s>NOTUSED"							)
   self.assertEqual(vocab_keys[1]  ,				"<pad>"							)
   self.assertEqual(vocab_keys[-1]  ,				"<mask>"							)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)
   __a							=	CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   __a							=	tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   if not self.test_rust_tokenizer:
    return

   __a							=	self.get_tokenizer()
   __a							=	self.get_rust_tokenizer()

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_rust_tokenizer()
   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   __a							=	[
       "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
       "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
       "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
       "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
       "telles que la traduction et la synthèse de texte.",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)


