


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


A  :     int    					=    logging.get_logger(__name__)


A  :     List[str]    					=    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
A  :     Optional[Any]    					=    {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

A  :     Any    					=    {
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					Union[str, Any]          =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					List[Any]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Tuple          =["""input_ids""", """attention_mask"""]
							__UpperCAmelCase		:					Optional[int]          =BartTokenizer

							def __init__(  self					,     __a=None					,     __a=None					,     __a=None					,     __a="replace"					,     __a="<s>"					,     __a="</s>"					,     __a="</s>"					,     __a="<s>"					,     __a="<unk>"					,     __a="<pad>"					,     __a="<mask>"					,     __a=False					,     __a=True					,     **__a					,     ):
										super().__init__(
										    __a					,     __a					,     tokenizer_file=__a					,     errors=__a					,     bos_token=__a					,     eos_token=__a					,     sep_token=__a					,     cls_token=__a					,     unk_token=__a					,     pad_token=__a					,     mask_token=__a					,     add_prefix_space=__a					,     trim_offsets=__a					,     **__a					,     )

										__lowerCAmelCase       					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
										if pre_tok_state.get("add_prefix_space"					,     __a			) != add_prefix_space:
													__lowerCAmelCase       					=    getattr(__a					,     pre_tok_state.pop("type"			)			)
													__lowerCAmelCase       					=    add_prefix_space
													__lowerCAmelCase       					=    pre_tok_class(**__a			)

										__lowerCAmelCase       					=    add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										__lowerCAmelCase       					=    "post_processor"
										__lowerCAmelCase       					=    getattr(self.backend_tokenizer					,     __a					,     __a			)
										if tokenizer_component_instance:
													__lowerCAmelCase       					=    json.loads(tokenizer_component_instance.__getstate__()			)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																__lowerCAmelCase       					=    tuple(state["sep"]			)
													if "cls" in state:
																__lowerCAmelCase       					=    tuple(state["cls"]			)

													__lowerCAmelCase       					=    False

													if state.get("add_prefix_space"					,     __a			) != add_prefix_space:
																__lowerCAmelCase       					=    add_prefix_space
																__lowerCAmelCase       					=    True

													if state.get("trim_offsets"					,     __a			) != trim_offsets:
																__lowerCAmelCase       					=    trim_offsets
																__lowerCAmelCase       					=    True

													if changes_to_apply:
																__lowerCAmelCase       					=    getattr(__a					,     state.pop("type"			)			)
																__lowerCAmelCase       					=    component_class(**__a			)
																setattr(self.backend_tokenizer					,     __a					,     __a			)

							@property
							def     snake_case       (  self			):
										if self._mask_token is None:
													if self.verbose:
																logger.error("Using mask_token, but it is not set yet."			)
													return None
										return str(self._mask_token			)

							@mask_token.setter
							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    AddedToken(__a					,     lstrip=__a					,     rstrip=__a			) if isinstance(__a					,     __a			) else value
										__lowerCAmelCase       					=    value

							def     snake_case       (  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.get("is_split_into_words"					,     __a			)

										if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."			)

										return super()._batch_encode_plus(*__a					,     **__a			)

							def     snake_case       (  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.get("is_split_into_words"					,     __a			)

										if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."			)

										return super()._encode_plus(*__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    self._tokenizer.model.save(__a					,     name=__a			)
										return tuple(__a			)

							def     snake_case       (  self					,     __a					,     __a=None			):
										__lowerCAmelCase       					=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
													return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    [self.sep_token_id]
										__lowerCAmelCase       					=    [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]








"""simple docstring"""


import flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




