

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
      import timm


if is_torch_available():
      from torch import Tensor

class   _a					(			snake_case_     ,				snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Optional[int]      		=       'pixel_values'
      _lowerCamelCase       :			Optional[Any]      		=       False
      _lowerCamelCase       :			Union[str, Any]      		=       TimmBackboneConfig





      def __init__(   self							:				Optional[int]						,			UpperCAmelCase							:				Optional[Any]						,			**UpperCAmelCase							:				Optional[Any]	):
            requires_backends(self						,			"timm"	)
            super().__init__(UpperCAmelCase	)
            A_						  =				config

            if config.backbone is None:
                  raise ValueError("backbone is not set in the config. Please set it to a timm model name."	)

            if config.backbone not in timm.list_models():
                  raise ValueError(f'''backbone {config.backbone} is not supported by timm.'''	)

            if hasattr(UpperCAmelCase						,			"out_features"	) and config.out_features is not None:
                  raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."	)

            A_						  =				getattr(UpperCAmelCase						,			"use_pretrained_backbone"						,			UpperCAmelCase	)
            if pretrained is None:
                  raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."	)

            # We just take the final layer by default. This matches the default for the transformers models.
            A_						  =				config.out_indices if getattr(UpperCAmelCase						,			"out_indices"						,			UpperCAmelCase	) is not None else (-1,)

            A_						  =				timm.create_model(
                config.backbone						,			pretrained=UpperCAmelCase						,			features_only=config.features_only						,			in_chans=config.num_channels						,			out_indices=UpperCAmelCase						,			**UpperCAmelCase						,			)
            # These are used to control the output of the model when called. If output_hidden_states is True, then
            # return_layers is modified to include all layers.
            A_						  =				self._backbone.return_layers
            A_						  =				{layer["module"]: str(UpperCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
            super()._init_backbone(UpperCAmelCase	)





      @classmethod
      def 		__A	(   cls							:				Optional[int]						,			UpperCAmelCase							:				List[Any]						,			*UpperCAmelCase							:				Dict						,			**UpperCAmelCase							:				List[Any]	):
            requires_backends(cls						,			["vision", "timm"]	)
            from ...models.timm_backbone import TimmBackboneConfig

            A_						  =				kwargs.pop("config"						,			TimmBackboneConfig()	)

            A_						  =				kwargs.pop("use_timm_backbone"						,			UpperCAmelCase	)
            if not use_timm:
                  raise ValueError("use_timm_backbone must be True for timm backbones"	)

            A_						  =				kwargs.pop("num_channels"						,			config.num_channels	)
            A_						  =				kwargs.pop("features_only"						,			config.features_only	)
            A_						  =				kwargs.pop("use_pretrained_backbone"						,			config.use_pretrained_backbone	)
            A_						  =				kwargs.pop("out_indices"						,			config.out_indices	)
            A_						  =				TimmBackboneConfig(
                backbone=UpperCAmelCase						,			num_channels=UpperCAmelCase						,			features_only=UpperCAmelCase						,			use_pretrained_backbone=UpperCAmelCase						,			out_indices=UpperCAmelCase						,			)
            return super()._from_config(UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				Optional[int]	):
            pass







      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Union[str, Any]=None						,			UpperCAmelCase							:				Optional[int]=None						,			UpperCAmelCase							:				Dict=None						,			**UpperCAmelCase							:				int	):
            A_						  =				return_dict if return_dict is not None else self.config.use_return_dict
            A_						  =				(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            A_						  =				output_attentions if output_attentions is not None else self.config.output_attentions

            if output_attentions:
                  raise ValueError("Cannot output attentions for timm backbones at the moment"	)

            if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  A_						  =				self._all_layers
                  A_						  =				self._backbone(UpperCAmelCase						,			**UpperCAmelCase	)
                  A_						  =				self._return_layers
                  A_						  =				tuple(hidden_states[i] for i in self.out_indices	)
            else:
                  A_						  =				self._backbone(UpperCAmelCase						,			**UpperCAmelCase	)
                  A_						  =				None

            A_						  =				tuple(UpperCAmelCase	)
            A_						  =				tuple(UpperCAmelCase	) if hidden_states is not None else None

            if not return_dict:
                  A_						  =				(feature_maps,)
                  if output_hidden_states:
                        A_						  =				output + (hidden_states,)
                  return output

            return BackboneOutput(feature_maps=UpperCAmelCase						,			hidden_states=UpperCAmelCase						,			attentions=UpperCAmelCase	)



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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbart import MBartTokenizer
else:
				lowerCAmelCase       		=    None


lowerCAmelCase       		=    logging.get_logger(__name__)


lowerCAmelCase       		=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase       		=    {
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase       		=    {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCAmelCase       		=    ["""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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[str]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		str										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[int]										=						['''input_ids''', '''attention_mask''']
	_lowerCAmelCase     :		Optional[int]										=						MBartTokenizer

	_lowerCAmelCase     :		List[int]										=						[]
	_lowerCAmelCase     :		List[int]										=						[]


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

							super().__init__(
							    vocab_file=lowercase__				,     tokenizer_file=lowercase__				,     bos_token=lowercase__				,     eos_token=lowercase__				,     sep_token=lowercase__				,     cls_token=lowercase__				,     unk_token=lowercase__				,     pad_token=lowercase__				,     mask_token=lowercase__				,     src_lang=lowercase__				,     tgt_lang=lowercase__				,     additional_special_tokens=lowercase__				,     **lowercase__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	vocab_file
							__UpperCAmelCase			:							int	    =	False if not self.vocab_file else True

							__UpperCAmelCase			:							str	    =	FAIRSEQ_LANGUAGE_CODES.copy()

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

							self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens})
							__UpperCAmelCase			:							Union[str, Any]	    =	{
							    lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							__UpperCAmelCase			:							Any	    =	src_lang if src_lang is not None else '''en_XX'''
							__UpperCAmelCase			:							Optional[int]	    =	self.convert_tokens_to_ids(self._src_lang)
							__UpperCAmelCase			:							List[Any]	    =	tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)


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


	@src_lang.setter
	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def  A(    self				,     lowercase__				,     lowercase__ = 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  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Optional[int]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							int	    =	[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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__):
							if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
							__UpperCAmelCase			:							Optional[Any]	    =	src_lang
							__UpperCAmelCase			:							Union[str, Any]	    =	self(lowercase__				,     add_special_tokens=lowercase__				,     return_tensors=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							int	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	tgt_lang_id
							return inputs


	def  A(    self				,     lowercase__				,     lowercase__ = "en_XX"				,     lowercase__ = None				,     lowercase__ = "ro_RO"				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Any	    =	src_lang
							__UpperCAmelCase			:							Tuple	    =	tgt_lang
							return super().prepare_seqaseq_batch(lowercase__				,     lowercase__				,     **lowercase__)


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


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


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[]
							__UpperCAmelCase			:							Dict	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							int	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Dict	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							str	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	[]
							__UpperCAmelCase			:							List[Any]	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							Optional[int]	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
													    '''tokenizer.''')

							if not os.path.isdir(lowercase__):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
													return
							__UpperCAmelCase			:							List[Any]	    =	os.path.join(
							    lowercase__				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

							if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase__):
													copyfile(self.vocab_file				,     lowercase__)

							return (out_vocab_file,)

