



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

import sentencepiece as spm

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		"▁"

_lowercase    	=		{"vocab_file": "sentencepiece.bpe.model"}

_lowercase    	=		{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_lowercase    	=		{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
_lowercase    	=		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					VOCAB_FILES_NAMES
     UpperCamelCase__		     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		     =					PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		     =					['''input_ids''', '''attention_mask''']

     UpperCamelCase__		     =					[]
     UpperCamelCase__		     =					[]




     def __init__(					self			,    a__			,    a__="<s>"			,    a__="</s>"			,    a__="</s>"			,    a__="<s>"			,    a__="<unk>"			,    a__="<pad>"			,    a__="<mask>"			,    a__=None			,    a__=None			,    a__=None			,    a__ = None			,    a__=None			,    **a__			,    ):
         # Mask token behave like a normal word, i.e. include the space before it
         A__					=       AddedToken(a__			,    lstrip=a__			,    rstrip=a__) if isinstance(a__			,    a__) else mask_token

         A__					=       {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=a__			,    eos_token=a__			,    unk_token=a__			,    sep_token=a__			,    cls_token=a__			,    pad_token=a__			,    mask_token=a__			,    tokenizer_file=a__			,    src_lang=a__			,    tgt_lang=a__			,    additional_special_tokens=a__			,    sp_model_kwargs=self.sp_model_kwargs			,    **a__			,    )

         A__					=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(a__))
         A__					=       vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         A__					=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         A__					=       1

         A__					=       len(self.sp_model)
         A__					=       {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a__)
         }
         A__					=       {v: k for k, v in self.lang_code_to_id.items()}
         A__					=       len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
         A__					=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         A__					=       list(self.lang_code_to_id.keys())

         if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             self._additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in self._additional_special_tokens])

         A__					=       src_lang if src_lang is not None else '''en_XX'''
         A__					=       self.lang_code_to_id[self._src_lang]
         A__					=       tgt_lang
         self.set_src_lang_special_tokens(self._src_lang)




     def __getstate__(					self):
         A__					=       self.__dict__.copy()
         A__					=       None
         A__					=       self.sp_model.serialized_model_proto()
         return state




     def __setstate__(					self			,    a__):
         A__					=       d

         # for backward compatibility
         if not hasattr(self			,    '''sp_model_kwargs'''):
             A__					=       {}

         A__					=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto)




     @property
     def 		snake_case_		(					self):
         return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token




     @property
     def 		snake_case_		(					self):
         return self._src_lang




     @src_lang.setter
     def 		snake_case_		(					self			,    a__):
         A__					=       new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = False):

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=a__			,    token_ids_a=a__			,    already_has_special_tokens=a__)

         A__					=       [1] * len(self.prefix_tokens)
         A__					=       [1] * len(self.suffix_tokens)
         if token_ids_a is None:
             return prefix_ones + ([0] * len(a__)) + suffix_ones
         return prefix_ones + ([0] * len(a__)) + ([0] * len(a__)) + suffix_ones




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       [self.sep_token_id]
         A__					=       [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]




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    **a__):
         if src_lang is None or tgt_lang is None:
             raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
         A__					=       src_lang
         A__					=       self(a__			,    add_special_tokens=a__			,    return_tensors=a__			,    **a__)
         A__					=       self.convert_tokens_to_ids(a__)
         A__					=       tgt_lang_id
         return inputs




     def 		snake_case_		(					self):
         A__					=       {self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab




     def 		snake_case_		(					self			,    a__):
         return self.sp_model.encode(a__			,    out_type=a__)




     def 		snake_case_		(					self			,    a__):
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         A__					=       self.sp_model.PieceToId(a__)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




     def 		snake_case_		(					self			,    a__):
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset)




     def 		snake_case_		(					self			,    a__):
         A__					=       ''''''.join(a__).replace(a__			,    ''' ''').strip()
         return out_string




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         if not os.path.isdir(a__):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory")
             return
         A__					=       os.path.join(
             a__			,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

         return (out_vocab_file,)




     def 		snake_case_		(					self			,    a__			,    a__ = "en_XX"			,    a__ = None			,    a__ = "ro_RO"			,    **a__			,    ):
         A__					=       src_lang
         A__					=       tgt_lang
         return super().prepare_seqaseq_batch(a__			,    a__			,    **a__)




     def 		snake_case_		(					self):
         return self.set_src_lang_special_tokens(self.src_lang)




     def 		snake_case_		(					self):
         return self.set_tgt_lang_special_tokens(self.tgt_lang)




     def 		snake_case_		(					self			,    a__):
         A__					=       self.lang_code_to_id[src_lang]
         A__					=       []
         A__					=       [self.eos_token_id, self.cur_lang_code]




     def 		snake_case_		(					self			,    a__):
         A__					=       self.lang_code_to_id[lang]
         A__					=       []
         A__					=       [self.eos_token_id, self.cur_lang_code]





from manim import *



class      _UpperCAmelCase      (							A__  ):




     def 		snake_case_		(					self):
         A__					=       Rectangle(height=0.5			,    width=0.5)
         A__					=       Rectangle(height=0.2_5			,    width=0.2_5)
         A__					=       Rectangle(height=0.4_6			,    width=0.4_6).set_stroke(width=0)

         A__					=       [mem.copy() for i in range(6)]
         A__					=       [mem.copy() for i in range(6)]
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)
         A__					=       VGroup(a__			,    a__).arrange(a__			,    buff=0)
         A__					=       Text('''CPU'''			,    font_size=2_4)
         A__					=       Group(a__			,    a__).arrange(a__			,    buff=0.5			,    aligned_edge=a__)
         cpu.move_to([-2.5, -0.5, 0])
         self.add(a__)

         A__					=       [mem.copy() for i in range(4)]
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)
         A__					=       Text('''GPU'''			,    font_size=2_4)
         A__					=       Group(a__			,    a__).arrange(a__			,    buff=0.5			,    aligned_edge=a__)
         gpu.move_to([-1, -1, 0])
         self.add(a__)

         A__					=       [mem.copy() for i in range(6)]
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)

         A__					=       Text('''Model'''			,    font_size=2_4)
         A__					=       Group(a__			,    a__).arrange(a__			,    buff=0.5			,    aligned_edge=a__)
         model.move_to([3, -1.0, 0])
         self.add(a__)

         A__					=       []
         A__					=       []
         A__					=       []

         for i, rect in enumerate(a__):
             rect.set_stroke(a__)

             A__					=       Rectangle(height=0.4_6 / 4			,    width=0.4_6 / 3).set_stroke(width=0.0).set_fill(a__			,    opacity=0.7)

             if i == 0:
                 cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)			,    buff=0.0_2			,    direction=a__)
                 cpu_target.set_x(cpu_target.get_x() + 0.1)
             elif i == 3:
                 cpu_target.next_to(model_cpu_arr[0]			,    direction=a__			,    buff=0.0)
             else:
                 cpu_target.next_to(model_cpu_arr[i - 1]			,    direction=a__			,    buff=0.0)
             self.add(a__)
             model_cpu_arr.append(a__)

         self.add(*a__			,    *a__			,    *a__)

         A__					=       [mem.copy() for i in range(6)]
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)

         A__					=       Text('''Loaded Checkpoint'''			,    font_size=2_4)
         A__					=       Group(a__			,    a__).arrange(a__			,    buff=0.5			,    aligned_edge=a__)
         checkpoint.move_to([3, 0.5, 0])

         self.add(a__)

         A__					=       []
         A__					=       []

         for i, rect in enumerate(a__):
             A__					=       fill.copy().set_fill(a__			,    opacity=0.7)
             target.move_to(a__)
             ckpt_arr.append(a__)

             A__					=       target.copy()
             if i < 5:
                 cpu_target.move_to(cpu_left_col_base[i + 1])
             else:
                 cpu_target.move_to(cpu_right_col_base[i - 5])
             ckpt_cpu_arr.append(a__)
         self.add(*a__			,    *a__)

         A__					=       Square(side_length=2.2)
         key.move_to([-5, 2, 0])

         A__					=       MarkupText(
             F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"			,    font_size=1_8			,    )

         key_text.move_to([-5, 2.4, 0])

         self.add(a__			,    a__)

         A__					=       MarkupText(
             F"<span fgcolor='{BLUE}'>●</span> Checkpoint"			,    font_size=1_8			,    )

         blue_text.next_to(a__			,    DOWN * 2.4			,    aligned_edge=key_text.get_left())
         self.add(a__)

         A__					=       MarkupText(
             F"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."			,    font_size=2_4			,    )
         step_a.move_to([2, 2, 0])

         A__					=       [meta_mem.copy() for i in range(6)]
         A__					=       [meta_mem.copy() for i in range(6)]
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)
         A__					=       VGroup(*a__).arrange(a__			,    buff=0)
         A__					=       VGroup(a__			,    a__).arrange(a__			,    buff=0)
         A__					=       Text('''Disk'''			,    font_size=2_4)
         A__					=       Group(a__			,    a__).arrange(a__			,    buff=0.5			,    aligned_edge=a__)
         disk.move_to([-4.0, -1.2_5, 0])
         self.play(Write(a__			,    run_time=3)			,    Write(a__			,    run_time=1)			,    Create(a__			,    run_time=1))

         A__					=       []
         for i, rect in enumerate(a__):
             A__					=       rect.copy()
             target.generate_target()
             target.target.move_to(disk_left_col_base[i]).scale(0.5)
             animations.append(MoveToTarget(a__			,    run_time=1.5))
         self.play(*a__)

         self.play(FadeOut(a__))

         A__					=       MarkupText(F"Then, the checkpoint is removed from memory\nthrough garbage collection."			,    font_size=2_4)
         step_a.move_to([2, 2, 0])

         self.play(Write(a__			,    run_time=3))

         self.play(
             FadeOut(a__			,    a__			,    *a__			,    *a__)			,    )

         self.wait()
