




from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


__A						:							Dict							=  logging.get_logger(__name__)


# General docstring
__A						:							Union[str, Any]							=  """MobileNetV1Config"""

# Base docstring
__A						:							Tuple							=  """google/mobilenet_v1_1.0_224"""
__A						:							List[Any]							=  [1, 1_0_2_4, 7, 7]

# Image classification docstring
__A						:							Union[str, Any]							=  """google/mobilenet_v1_1.0_224"""
__A						:							Optional[Any]							=  """tabby, tabby cat"""


__A						:							Optional[int]							=  [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{}

			if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					model.mobilenet_va
			else:
						SCREAMING_SNAKE_CASE           =					model

			SCREAMING_SNAKE_CASE           =					"""MobilenetV1/Conv2d_0/"""
			SCREAMING_SNAKE_CASE           =					backbone.conv_stem.convolution.weight
			SCREAMING_SNAKE_CASE           =					backbone.conv_stem.normalization.bias
			SCREAMING_SNAKE_CASE           =					backbone.conv_stem.normalization.weight
			SCREAMING_SNAKE_CASE           =					backbone.conv_stem.normalization.running_mean
			SCREAMING_SNAKE_CASE           =					backbone.conv_stem.normalization.running_var

			for i in range(13   ):
						SCREAMING_SNAKE_CASE           =					i + 1
						SCREAMING_SNAKE_CASE           =					i * 2

						SCREAMING_SNAKE_CASE           =					backbone.layer[pt_index]
						SCREAMING_SNAKE_CASE           =					f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
						SCREAMING_SNAKE_CASE           =					pointer.convolution.weight
						SCREAMING_SNAKE_CASE           =					pointer.normalization.bias
						SCREAMING_SNAKE_CASE           =					pointer.normalization.weight
						SCREAMING_SNAKE_CASE           =					pointer.normalization.running_mean
						SCREAMING_SNAKE_CASE           =					pointer.normalization.running_var

						SCREAMING_SNAKE_CASE           =					backbone.layer[pt_index + 1]
						SCREAMING_SNAKE_CASE           =					f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
						SCREAMING_SNAKE_CASE           =					pointer.convolution.weight
						SCREAMING_SNAKE_CASE           =					pointer.normalization.bias
						SCREAMING_SNAKE_CASE           =					pointer.normalization.weight
						SCREAMING_SNAKE_CASE           =					pointer.normalization.running_mean
						SCREAMING_SNAKE_CASE           =					pointer.normalization.running_var

			if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
						SCREAMING_SNAKE_CASE           =					model.classifier.weight
						SCREAMING_SNAKE_CASE           =					model.classifier.bias

			return tf_to_pt_map
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			try:
						import numpy as np
						import tensorflow as tf
			except ImportError:
						logger.error(
						    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
						    """https://www.tensorflow.org/install/ for installation instructions."""   )
						raise

			# Load weights from TF model
			SCREAMING_SNAKE_CASE           =					tf.train.list_variables(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					{}
			for name, shape in init_vars:
						logger.info(f"""Loading TF weight {name} with shape {shape}"""   )
						SCREAMING_SNAKE_CASE           =					tf.train.load_variable(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					array

			# Build TF to PyTorch weights loading map
			SCREAMING_SNAKE_CASE           =					_build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			for name, pointer in tf_to_pt_map.items():
						logger.info(f"""Importing {name}"""   )
						if name not in tf_weights:
									logger.info(f"""{name} not in tf pre-trained weights, skipping"""   )
									continue

						SCREAMING_SNAKE_CASE           =					tf_weights[name]

						if "depthwise_weights" in name:
									logger.info("""Transposing depthwise"""   )
									SCREAMING_SNAKE_CASE           =					np.transpose(SCREAMING_SNAKE_CASE					,	(2, 3, 0, 1)   )
						elif "weights" in name:
									logger.info("""Transposing"""   )
									if len(pointer.shape   ) == 2:  # copying into linear layer
												SCREAMING_SNAKE_CASE           =					array.squeeze().transpose()
									else:
												SCREAMING_SNAKE_CASE           =					np.transpose(SCREAMING_SNAKE_CASE					,	(3, 2, 0, 1)   )

						if pointer.shape != array.shape:
									raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""   )

						logger.info(f"""Initialize PyTorch weight {name} {array.shape}"""   )
						SCREAMING_SNAKE_CASE           =					torch.from_numpy(SCREAMING_SNAKE_CASE   )

						tf_weights.pop(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						tf_weights.pop(name + """/RMSProp"""					,	SCREAMING_SNAKE_CASE   )
						tf_weights.pop(name + """/RMSProp_1"""					,	SCREAMING_SNAKE_CASE   )
						tf_weights.pop(name + """/ExponentialMovingAverage"""					,	SCREAMING_SNAKE_CASE   )

			logger.info(f"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys()   )}"""   )
			return model
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					features.shape[-2:]
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					conv_layer.stride
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					conv_layer.kernel_size

			if in_height % stride_height == 0:
						SCREAMING_SNAKE_CASE           =					max(kernel_height - stride_height					,	0   )
			else:
						SCREAMING_SNAKE_CASE           =					max(kernel_height - (in_height % stride_height)					,	0   )

			if in_width % stride_width == 0:
						SCREAMING_SNAKE_CASE           =					max(kernel_width - stride_width					,	0   )
			else:
						SCREAMING_SNAKE_CASE           =					max(kernel_width - (in_width % stride_width)					,	0   )

			SCREAMING_SNAKE_CASE           =					pad_along_width // 2
			SCREAMING_SNAKE_CASE           =					pad_along_width - pad_left
			SCREAMING_SNAKE_CASE           =					pad_along_height // 2
			SCREAMING_SNAKE_CASE           =					pad_along_height - pad_top

			SCREAMING_SNAKE_CASE           =					(pad_left, pad_right, pad_top, pad_bottom)
			return nn.functional.pad(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	"""constant"""					,	0.0   )




class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Dict       ,     a       :	MobileNetVaConfig       ,     a       :	int       ,     a       :	int       ,     a       :	int       ,     a       :	Optional[int] = 1       ,     a       :	Optional[int] = 1       ,     a       :	bool = False       ,     a       :	Optional[bool] = True       ,     a       :	Optional[bool or str] = True       ,     ):
										super().__init__()
										SCREAMING_SNAKE_CASE           =					config

										if in_channels % groups != 0:
													raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups."""			)
										if out_channels % groups != 0:
													raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups."""			)

										SCREAMING_SNAKE_CASE           =					0 if config.tf_padding else int((kernel_size - 1) / 2			)

										SCREAMING_SNAKE_CASE           =					nn.Convad(
										    in_channels=a       ,     out_channels=a       ,     kernel_size=a       ,     stride=a       ,     padding=a       ,     groups=a       ,     bias=a       ,     padding_mode="""zeros"""       ,     )

										if use_normalization:
													SCREAMING_SNAKE_CASE           =					nn.BatchNormad(
													    num_features=a       ,     eps=config.layer_norm_eps       ,     momentum=0.9997       ,     affine=a       ,     track_running_stats=a       ,     )
										else:
													SCREAMING_SNAKE_CASE           =					None

										if use_activation:
													if isinstance(a       ,     a			):
																SCREAMING_SNAKE_CASE           =					ACTaFN[use_activation]
													elif isinstance(config.hidden_act       ,     a			):
																SCREAMING_SNAKE_CASE           =					ACTaFN[config.hidden_act]
													else:
																SCREAMING_SNAKE_CASE           =					config.hidden_act
										else:
													SCREAMING_SNAKE_CASE           =					None







							def    _UpperCAmelCase     (					self       :	str       ,     a       :	torch.Tensor			):
										if self.config.tf_padding:
													SCREAMING_SNAKE_CASE           =					apply_tf_padding(a       ,     self.convolution			)
										SCREAMING_SNAKE_CASE           =					self.convolution(a			)
										if self.normalization is not None:
													SCREAMING_SNAKE_CASE           =					self.normalization(a			)
										if self.activation is not None:
													SCREAMING_SNAKE_CASE           =					self.activation(a			)
										return features





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     MobileNetVaConfig
							a__						  =     load_tf_weights_in_mobilenet_va
							a__						  =     '''mobilenet_v1'''
							a__						  =     '''pixel_values'''
							a__						  =     False





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Union[nn.Linear, nn.Convad]			):
										if isinstance(a       ,     (nn.Linear, nn.Convad)			):
													module.weight.data.normal_(mean=0.0       ,     std=self.config.initializer_range			)
													if module.bias is not None:
																module.bias.data.zero_()
										elif isinstance(a       ,     nn.BatchNormad			):
													module.bias.data.zero_()
													module.weight.data.fill_(1.0			)





__A						:							Any							=  r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__A						:							str							=  r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""




@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''		,      A		,      )
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Tuple       ,     a       :	MobileNetVaConfig       ,     a       :	bool = True			):
										super().__init__(a			)
										SCREAMING_SNAKE_CASE           =					config

										SCREAMING_SNAKE_CASE           =					32
										SCREAMING_SNAKE_CASE           =					max(int(depth * config.depth_multiplier			)       ,     config.min_depth			)

										SCREAMING_SNAKE_CASE           =					MobileNetVaConvLayer(
										    a       ,     in_channels=config.num_channels       ,     out_channels=a       ,     kernel_size=3       ,     stride=2       ,     )

										SCREAMING_SNAKE_CASE           =					[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

										SCREAMING_SNAKE_CASE           =					nn.ModuleList()
										for i in range(13			):
													SCREAMING_SNAKE_CASE           =					out_channels

													if strides[i] == 2 or i == 0:
																depth *= 2
																SCREAMING_SNAKE_CASE           =					max(int(depth * config.depth_multiplier			)       ,     config.min_depth			)

													self.layer.append(
													    MobileNetVaConvLayer(
													        a       ,     in_channels=a       ,     out_channels=a       ,     kernel_size=3       ,     stride=strides[i]       ,     groups=a       ,     )			)

													self.layer.append(
													    MobileNetVaConvLayer(
													        a       ,     in_channels=a       ,     out_channels=a       ,     kernel_size=1       ,     )			)

										SCREAMING_SNAKE_CASE           =					nn.AdaptiveAvgPoolad((1, 1)			) if add_pooling_layer else None

										# Initialize weights and apply final processing
										self.post_init()





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Tuple			):
										raise NotImplementedError







							@add_start_docstrings_to_model_forward(a			)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC       ,     output_type=a       ,     config_class=_CONFIG_FOR_DOC       ,     modality="""vision"""       ,     expected_output=_EXPECTED_OUTPUT_SHAPE       ,     )
							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[torch.Tensor] = None       ,     a       :	Optional[bool] = None       ,     a       :	Optional[bool] = None       ,     ):
										SCREAMING_SNAKE_CASE           =					(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE           =					return_dict if return_dict is not None else self.config.use_return_dict

										if pixel_values is None:
													raise ValueError("""You have to specify pixel_values"""			)

										SCREAMING_SNAKE_CASE           =					self.conv_stem(a			)

										SCREAMING_SNAKE_CASE           =					() if output_hidden_states else None

										for i, layer_module in enumerate(self.layer			):
													SCREAMING_SNAKE_CASE           =					layer_module(a			)

													if output_hidden_states:
																SCREAMING_SNAKE_CASE           =					all_hidden_states + (hidden_states,)

										SCREAMING_SNAKE_CASE           =					hidden_states

										if self.pooler is not None:
													SCREAMING_SNAKE_CASE           =					torch.flatten(self.pooler(a			)       ,     start_dim=1			)
										else:
													SCREAMING_SNAKE_CASE           =					None

										if not return_dict:
													return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None			)

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=a       ,     pooler_output=a       ,     hidden_states=a       ,     )





@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,      A		,      )
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	str       ,     a       :	MobileNetVaConfig			):
										super().__init__(a			)

										SCREAMING_SNAKE_CASE           =					config.num_labels
										SCREAMING_SNAKE_CASE           =					MobileNetVaModel(a			)

										SCREAMING_SNAKE_CASE           =					self.mobilenet_va.layer[-1].convolution.out_channels

										# Classifier head
										SCREAMING_SNAKE_CASE           =					nn.Dropout(config.classifier_dropout_prob       ,     inplace=a			)
										SCREAMING_SNAKE_CASE           =					nn.Linear(a       ,     config.num_labels			) if config.num_labels > 0 else nn.Identity()

										# Initialize weights and apply final processing
										self.post_init()







							@add_start_docstrings_to_model_forward(a			)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,     output_type=a       ,     config_class=_CONFIG_FOR_DOC       ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,     )
							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Optional[torch.Tensor] = None       ,     a       :	Optional[bool] = None       ,     a       :	Optional[torch.Tensor] = None       ,     a       :	Optional[bool] = None       ,     ):
										SCREAMING_SNAKE_CASE           =					return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE           =					self.mobilenet_va(a       ,     output_hidden_states=a       ,     return_dict=a			)

										SCREAMING_SNAKE_CASE           =					outputs.pooler_output if return_dict else outputs[1]

										SCREAMING_SNAKE_CASE           =					self.classifier(self.dropout(a			)			)

										SCREAMING_SNAKE_CASE           =					None
										if labels is not None:
													if self.config.problem_type is None:
																if self.num_labels == 1:
																			SCREAMING_SNAKE_CASE           =					"""regression"""
																elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																			SCREAMING_SNAKE_CASE           =					"""single_label_classification"""
																else:
																			SCREAMING_SNAKE_CASE           =					"""multi_label_classification"""

													if self.config.problem_type == "regression":
																SCREAMING_SNAKE_CASE           =					MSELoss()
																if self.num_labels == 1:
																			SCREAMING_SNAKE_CASE           =					loss_fct(logits.squeeze()       ,     labels.squeeze()			)
																else:
																			SCREAMING_SNAKE_CASE           =					loss_fct(a       ,     a			)
													elif self.config.problem_type == "single_label_classification":
																SCREAMING_SNAKE_CASE           =					CrossEntropyLoss()
																SCREAMING_SNAKE_CASE           =					loss_fct(logits.view(-1       ,     self.num_labels			)       ,     labels.view(-1			)			)
													elif self.config.problem_type == "multi_label_classification":
																SCREAMING_SNAKE_CASE           =					BCEWithLogitsLoss()
																SCREAMING_SNAKE_CASE           =					loss_fct(a       ,     a			)

										if not return_dict:
													SCREAMING_SNAKE_CASE           =					(logits,) + outputs[2:]
													return ((loss,) + output) if loss is not None else output

										return ImageClassifierOutputWithNoAttention(
										    loss=a       ,     logits=a       ,     hidden_states=outputs.hidden_states       ,     )












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:
		__A						:							Tuple							=  None


__A						:							Any							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """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""",
    },
}

__A						:							int							=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""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       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     MBartTokenizer

							a__						  =     []
							a__						  =     []





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

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

										SCREAMING_SNAKE_CASE           =					vocab_file
										SCREAMING_SNAKE_CASE           =					False if not self.vocab_file else True

										SCREAMING_SNAKE_CASE           =					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}			)
										SCREAMING_SNAKE_CASE           =					{
										    lang_code: self.convert_tokens_to_ids(a			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										SCREAMING_SNAKE_CASE           =					src_lang if src_lang is not None else """en_XX"""
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(self._src_lang			)
										SCREAMING_SNAKE_CASE           =					tgt_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										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    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Optional[Any]			)			->					Optional[Any]:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	List[str]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										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(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					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			):
													copyfile(self.vocab_file       ,     a			)

										return (out_vocab_file,)






