


'''simple docstring'''







from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)





@flax.struct.dataclass
class 	SCREAMING_SNAKE_CASE					(					__lowerCamelCase       ):
			snake_case__  =				42
			snake_case__  =				42





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):
			snake_case__  =				42
			snake_case__  =				(16, 32, 96, 256)
			snake_case__  =				jnp.floataa






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[Any]:
							a_		:  Union[str, Any]											=      nn.Conv(
							    self.block_out_channels[0]  ,						kernel_size=(3, 3)  ,						padding=((1, 1), (1, 1))  ,						dtype=self.dtype  ,						)

							a_		:  Optional[int]											=      []
							for i in range(len(self.block_out_channels					) - 1					):
											a_		:  str											=      self.block_out_channels[i]
											a_		:  List[Any]											=      self.block_out_channels[i + 1]
											a_		:  Tuple											=      nn.Conv(
											    a_  ,						kernel_size=(3, 3)  ,						padding=((1, 1), (1, 1))  ,						dtype=self.dtype  ,						)
											blocks.append(a_					)
											a_		:  Optional[Any]											=      nn.Conv(
											    a_  ,						kernel_size=(3, 3)  ,						strides=(2, 2)  ,						padding=((1, 1), (1, 1))  ,						dtype=self.dtype  ,						)
											blocks.append(a_					)
							a_		:  Union[str, Any]											=      blocks

							a_		:  int											=      nn.Conv(
							    self.conditioning_embedding_channels  ,						kernel_size=(3, 3)  ,						padding=((1, 1), (1, 1))  ,						kernel_init=nn.initializers.zeros_init()  ,						bias_init=nn.initializers.zeros_init()  ,						dtype=self.dtype  ,						)







			def __call__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Any					)			->			int:
							a_		:  Optional[Any]											=      self.conv_in(a_					)
							a_		:  Optional[Any]											=      nn.silu(a_					)

							for block in self.blocks:
											a_		:  str											=      block(a_					)
											a_		:  str											=      nn.silu(a_					)

							a_		:  Any											=      self.conv_out(a_					)

							return embedding





@flax_register_to_config
class 	SCREAMING_SNAKE_CASE					(					nn.Module	,   __lowerCamelCase	,   __lowerCamelCase       ):
			snake_case__  =				32
			snake_case__  =				4
			snake_case__  =				(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			snake_case__  =				False
			snake_case__  =				(320, 640, 1280, 1280)
			snake_case__  =				2
			snake_case__  =				8
			snake_case__  =				None
			snake_case__  =				1280
			snake_case__  =				0.0
			snake_case__  =				False
			snake_case__  =				jnp.floataa
			snake_case__  =				True
			snake_case__  =				0
			snake_case__  =				"rgb"
			snake_case__  =				(16, 32, 96, 256)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[Any]:
							# init input tensors
							a_		:  Tuple											=      (1, self.in_channels, self.sample_size, self.sample_size)
							a_		:  Union[str, Any]											=      jnp.zeros(a_  ,						dtype=jnp.floataa					)
							a_		:  int											=      jnp.ones((1,)  ,						dtype=jnp.intaa					)
							a_		:  int											=      jnp.zeros((1, 1, self.cross_attention_dim)  ,						dtype=jnp.floataa					)
							a_		:  Union[str, Any]											=      (1, 3, self.sample_size * 8, self.sample_size * 8)
							a_		:  List[str]											=      jnp.zeros(a_  ,						dtype=jnp.floataa					)

							a_		:  List[str]											=      jax.random.split(a_					)
							a_		:  Dict											=      {"params": params_rng, "dropout": dropout_rng}

							return self.init(a_  ,						a_  ,						a_  ,						a_  ,						a_					)["params"]






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			List[str]:
							a_		:  Tuple											=      self.block_out_channels
							a_		:  Optional[Any]											=      block_out_channels[0] * 4

							# If `num_attention_heads` is not defined (which is the case for most models)
							# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
							# The reason for this behavior is to correct for incorrectly named variables that were introduced
							# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
							# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
							# which is why we correct for the naming here.
							a_		:  List[str]											=      self.num_attention_heads or self.attention_head_dim

							# input
							a_		:  Dict											=      nn.Conv(
							    block_out_channels[0]  ,						kernel_size=(3, 3)  ,						strides=(1, 1)  ,						padding=((1, 1), (1, 1))  ,						dtype=self.dtype  ,						)

							# time
							a_		:  Union[str, Any]											=      FlaxTimesteps(
							    block_out_channels[0]  ,						flip_sin_to_cos=self.flip_sin_to_cos  ,						freq_shift=self.config.freq_shift					)
							a_		:  int											=      FlaxTimestepEmbedding(a_  ,						dtype=self.dtype					)

							a_		:  List[str]											=      FlaxControlNetConditioningEmbedding(
							    conditioning_embedding_channels=block_out_channels[0]  ,						block_out_channels=self.conditioning_embedding_out_channels  ,						)

							a_		:  Dict											=      self.only_cross_attention
							if isinstance(a_  ,						a_					):
											a_		:  List[Any]											=      (only_cross_attention,) * len(self.down_block_types					)

							if isinstance(a_  ,						a_					):
											a_		:  Optional[Any]											=      (num_attention_heads,) * len(self.down_block_types					)

							# down
							a_		:  Tuple											=      []
							a_		:  List[str]											=      []

							a_		:  Dict											=      block_out_channels[0]

							a_		:  Any											=      nn.Conv(
							    a_  ,						kernel_size=(1, 1)  ,						padding='''VALID'''  ,						kernel_init=nn.initializers.zeros_init()  ,						bias_init=nn.initializers.zeros_init()  ,						dtype=self.dtype  ,						)
							controlnet_down_blocks.append(a_					)

							for i, down_block_type in enumerate(self.down_block_types					):
											a_		:  List[str]											=      output_channel
											a_		:  Any											=      block_out_channels[i]
											a_		:  Optional[Any]											=      i == len(a_					) - 1

											if down_block_type == "CrossAttnDownBlock2D":
															a_		:  Dict											=      FlaxCrossAttnDownBlockaD(
															    in_channels=a_  ,						out_channels=a_  ,						dropout=self.dropout  ,						num_layers=self.layers_per_block  ,						num_attention_heads=num_attention_heads[i]  ,						add_downsample=not is_final_block  ,						use_linear_projection=self.use_linear_projection  ,						only_cross_attention=only_cross_attention[i]  ,						dtype=self.dtype  ,						)
											else:
															a_		:  Dict											=      FlaxDownBlockaD(
															    in_channels=a_  ,						out_channels=a_  ,						dropout=self.dropout  ,						num_layers=self.layers_per_block  ,						add_downsample=not is_final_block  ,						dtype=self.dtype  ,						)

											down_blocks.append(a_					)

											for _ in range(self.layers_per_block					):
															a_		:  Dict											=      nn.Conv(
															    a_  ,						kernel_size=(1, 1)  ,						padding='''VALID'''  ,						kernel_init=nn.initializers.zeros_init()  ,						bias_init=nn.initializers.zeros_init()  ,						dtype=self.dtype  ,						)
															controlnet_down_blocks.append(a_					)

											if not is_final_block:
															a_		:  Union[str, Any]											=      nn.Conv(
															    a_  ,						kernel_size=(1, 1)  ,						padding='''VALID'''  ,						kernel_init=nn.initializers.zeros_init()  ,						bias_init=nn.initializers.zeros_init()  ,						dtype=self.dtype  ,						)
															controlnet_down_blocks.append(a_					)

							a_		:  List[str]											=      down_blocks
							a_		:  Union[str, Any]											=      controlnet_down_blocks

							# mid
							a_		:  Optional[int]											=      block_out_channels[-1]
							a_		:  List[str]											=      FlaxUNetMidBlockaDCrossAttn(
							    in_channels=a_  ,						dropout=self.dropout  ,						num_attention_heads=num_attention_heads[-1]  ,						use_linear_projection=self.use_linear_projection  ,						dtype=self.dtype  ,						)

							a_		:  str											=      nn.Conv(
							    a_  ,						kernel_size=(1, 1)  ,						padding='''VALID'''  ,						kernel_init=nn.initializers.zeros_init()  ,						bias_init=nn.initializers.zeros_init()  ,						dtype=self.dtype  ,						)







			def __call__( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Any = 1.0  ,						__SCREAMING_SNAKE_CASE      :			List[Any] = True  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = False  ,						)			->			List[Any]:
							a_		:  int											=      self.controlnet_conditioning_channel_order
							if channel_order == "bgr":
											a_		:  int											=      jnp.flip(a_  ,						axis=1					)

							# 1. time
							if not isinstance(a_  ,						jnp.ndarray					):
											a_		:  List[str]											=      jnp.array([timesteps]  ,						dtype=jnp.intaa					)
							elif isinstance(a_  ,						jnp.ndarray					) and len(timesteps.shape					) == 0:
											a_		:  str											=      timesteps.astype(dtype=jnp.floataa					)
											a_		:  Any											=      jnp.expand_dims(a_  ,						0					)

							a_		:  Optional[int]											=      self.time_proj(a_					)
							a_		:  Any											=      self.time_embedding(a_					)

							# 2. pre-process
							a_		:  Union[str, Any]											=      jnp.transpose(a_  ,						(0, 2, 3, 1)					)
							a_		:  Tuple											=      self.conv_in(a_					)

							a_		:  Tuple											=      jnp.transpose(a_  ,						(0, 2, 3, 1)					)
							a_		:  int											=      self.controlnet_cond_embedding(a_					)
							sample += controlnet_cond

							# 3. down
							a_		:  str											=      (sample,)
							for down_block in self.down_blocks:
											if isinstance(a_  ,						a_					):
															a_		:  Dict											=      down_block(a_  ,						a_  ,						a_  ,						deterministic=not train					)
											else:
															a_		:  Tuple											=      down_block(a_  ,						a_  ,						deterministic=not train					)
											down_block_res_samples += res_samples

							# 4. mid
							a_		:  List[str]											=      self.mid_block(a_  ,						a_  ,						a_  ,						deterministic=not train					)

							# 5. contronet blocks
							a_		:  Union[str, Any]											=      ()
							for down_block_res_sample, controlnet_block in zip(a_  ,						self.controlnet_down_blocks					):
											a_		:  Optional[int]											=      controlnet_block(a_					)
											controlnet_down_block_res_samples += (down_block_res_sample,)

							a_		:  List[Any]											=      controlnet_down_block_res_samples

							a_		:  Any											=      self.controlnet_mid_block(a_					)

							# 6. scaling
							a_		:  Optional[int]											=      [sample * conditioning_scale for sample in down_block_res_samples]
							mid_block_res_sample *= conditioning_scale

							if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

							return FlaxControlNetOutput(
							    down_block_res_samples=a_  ,						mid_block_res_sample=a_					)












'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict[str, Any]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

							if token_ids_a is None:
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															if not prev_is_special:
																			out_string += " "
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file  ,						__SCREAMING_SNAKE_CASE					)
							elif not os.path.isfile(self.vocab_file					):
											with open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					) as fi:
															a_		:  Any											=      self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE					)

							return (out_vocab_file,)




