



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


SCREAMING_SNAKE_CASE_:Optional[int]             =						logging.get_logger(__name__)


# General docstring
SCREAMING_SNAKE_CASE_:List[str]             =						"""MobileNetV1Config"""

# Base docstring
SCREAMING_SNAKE_CASE_:Tuple             =						"""google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE_:Tuple             =						[1, 1_024, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_:List[str]             =						"""google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE_:Dict             =						"""tabby, tabby cat"""


SCREAMING_SNAKE_CASE_:Tuple             =						[
    """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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=None		)       ->  Tuple:



    """simple docstring"""






    A :       int          =   {}

    if isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		):
        A :       Tuple          =   model.mobilenet_va
    else:
        A :       List[Any]          =   model

    A :       Dict          =   """MobilenetV1/Conv2d_0/"""
    A :       List[str]          =   backbone.conv_stem.convolution.weight
    A :       List[str]          =   backbone.conv_stem.normalization.bias
    A :       int          =   backbone.conv_stem.normalization.weight
    A :       Optional[Any]          =   backbone.conv_stem.normalization.running_mean
    A :       Any          =   backbone.conv_stem.normalization.running_var

    for i in range(13		):
        A :       Any          =   i + 1
        A :       Optional[Any]          =   i * 2

        A :       int          =   backbone.layer[pt_index]
        A :       List[Any]          =   f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
        A :       Optional[int]          =   pointer.convolution.weight
        A :       Optional[int]          =   pointer.normalization.bias
        A :       str          =   pointer.normalization.weight
        A :       Optional[Any]          =   pointer.normalization.running_mean
        A :       str          =   pointer.normalization.running_var

        A :       Optional[int]          =   backbone.layer[pt_index + 1]
        A :       Any          =   f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
        A :       str          =   pointer.convolution.weight
        A :       List[Any]          =   pointer.normalization.bias
        A :       Optional[int]          =   pointer.normalization.weight
        A :       Tuple          =   pointer.normalization.running_mean
        A :       int          =   pointer.normalization.running_var

    if isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		):
        A :       int          =   """MobilenetV1/Logits/Conv2d_1c_1x1/"""
        A :       Optional[int]          =   model.classifier.weight
        A :       Tuple          =   model.classifier.bias

    return tf_to_pt_map




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Dict:



    """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
    A :       List[Any]          =   tf.train.list_variables(_lowerCAmelCase		)
    A :       int          =   {}
    for name, shape in init_vars:
        logger.info(f'''Loading TF weight {name} with shape {shape}'''		)
        A :       Optional[Any]          =   tf.train.load_variable(_lowerCAmelCase      ,     _lowerCAmelCase		)
        A :       Union[str, Any]          =   array

    # Build TF to PyTorch weights loading map
    A :       Union[str, Any]          =   _build_tf_to_pytorch_map(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)

    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

        A :       Optional[int]          =   tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("""Transposing depthwise"""		)
            A :       Union[str, Any]          =   np.transpose(_lowerCAmelCase      ,     (2, 3, 0, 1)		)
        elif "weights" in name:
            logger.info("""Transposing"""		)
            if len(pointer.shape		) == 2:  # copying into linear layer
                A :       Any          =   array.squeeze().transpose()
            else:
                A :       List[str]          =   np.transpose(_lowerCAmelCase      ,     (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}'''		)
        A :       Any          =   torch.from_numpy(_lowerCAmelCase		)

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

    logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys()		)}'''		)
    return model




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  torch.Tensor:



    """simple docstring"""






    A     ,							A :       Any          =   features.shape[-2:]
    A     ,							A :       List[Any]          =   conv_layer.stride
    A     ,							A :       Optional[Any]          =   conv_layer.kernel_size

    if in_height % stride_height == 0:
        A :       Optional[int]          =   max(kernel_height - stride_height      ,     0		)
    else:
        A :       List[Any]          =   max(kernel_height - (in_height % stride_height)      ,     0		)

    if in_width % stride_width == 0:
        A :       Dict          =   max(kernel_width - stride_width      ,     0		)
    else:
        A :       Any          =   max(kernel_width - (in_width % stride_width)      ,     0		)

    A :       Optional[Any]          =   pad_along_width // 2
    A :       Optional[int]          =   pad_along_width - pad_left
    A :       Tuple          =   pad_along_height // 2
    A :       List[str]          =   pad_along_height - pad_top

    A :       Tuple          =   (pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(_lowerCAmelCase      ,     _lowerCAmelCase      ,     """constant"""      ,     0.0		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1,		lowerCamelCase__ = 1,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = True,		):
        super().__init__()
        A :       Optional[int]          =   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.'''		)

        A :       Tuple          =   0 if config.tf_padding else int((kernel_size - 1) / 2		)

        A :       str          =   nn.Convad(
            in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		kernel_size=lowerCamelCase__,		stride=lowerCamelCase__,		padding=lowerCamelCase__,		groups=lowerCamelCase__,		bias=lowerCamelCase__,		padding_mode="""zeros""",		)

        if use_normalization:
            A :       List[str]          =   nn.BatchNormad(
                num_features=lowerCamelCase__,		eps=config.layer_norm_eps,		momentum=0.9997,		affine=lowerCamelCase__,		track_running_stats=lowerCamelCase__,		)
        else:
            A :       str          =   None

        if use_activation:
            if isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A :       Dict          =   ACTaFN[use_activation]
            elif isinstance(config.hidden_act,		lowerCamelCase__		):
                A :       Any          =   ACTaFN[config.hidden_act]
            else:
                A :       Dict          =   config.hidden_act
        else:
            A :       Dict          =   None






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if self.config.tf_padding:
            A :       Dict          =   apply_tf_padding(lowerCamelCase__,		self.convolution		)
        A :       Optional[int]          =   self.convolution(lowerCamelCase__		)
        if self.normalization is not None:
            A :       Tuple          =   self.normalization(lowerCamelCase__		)
        if self.activation is not None:
            A :       List[Any]          =   self.activation(lowerCamelCase__		)
        return features






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: int					=      MobileNetVaConfig
    __lowerCamelCase			: List[Any]					=      load_tf_weights_in_mobilenet_va
    __lowerCamelCase			: Optional[int]					=      "mobilenet_v1"
    __lowerCamelCase			: Tuple					=      "pixel_values"
    __lowerCamelCase			: Dict					=      False






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if isinstance(lowerCamelCase__,		(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(lowerCamelCase__,		nn.BatchNormad		):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0		)


SCREAMING_SNAKE_CASE_:Tuple             =						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.
"""

SCREAMING_SNAKE_CASE_:Tuple             =						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." ,						SCREAMING_SNAKE_CASE__ ,						)
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        super().__init__(lowerCamelCase__		)
        A :       str          =   config

        A :       Optional[Any]          =   32
        A :       str          =   max(int(depth * config.depth_multiplier		),		config.min_depth		)

        A :       Any          =   MobileNetVaConvLayer(
            lowerCamelCase__,		in_channels=config.num_channels,		out_channels=lowerCamelCase__,		kernel_size=3,		stride=2,		)

        A :       List[str]          =   [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

        A :       Union[str, Any]          =   nn.ModuleList()
        for i in range(13		):
            A :       str          =   out_channels

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

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

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

        A :       Optional[int]          =   nn.AdaptiveAvgPoolad((1, 1)		) if add_pooling_layer else None

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        raise NotImplementedError






    @add_start_docstrings_to_model_forward(lowerCamelCase__		)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,		output_type=lowerCamelCase__,		config_class=_CONFIG_FOR_DOC,		modality="""vision""",		expected_output=_EXPECTED_OUTPUT_SHAPE,		)
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        A :       Tuple          =   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        A :       int          =   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"""		)

        A :       List[str]          =   self.conv_stem(lowerCamelCase__		)

        A :       Dict          =   () if output_hidden_states else None

        for i, layer_module in enumerate(self.layer		):
            A :       Optional[Any]          =   layer_module(lowerCamelCase__		)

            if output_hidden_states:
                A :       str          =   all_hidden_states + (hidden_states,)

        A :       Optional[Any]          =   hidden_states

        if self.pooler is not None:
            A :       Any          =   torch.flatten(self.pooler(lowerCamelCase__		),		start_dim=1		)
        else:
            A :       List[Any]          =   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=lowerCamelCase__,		pooler_output=lowerCamelCase__,		hidden_states=lowerCamelCase__,		)






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




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__		):
        super().__init__(lowerCamelCase__		)

        A :       str          =   config.num_labels
        A :       Dict          =   MobileNetVaModel(lowerCamelCase__		)

        A :       str          =   self.mobilenet_va.layer[-1].convolution.out_channels

        # Classifier head
        A :       List[str]          =   nn.Dropout(config.classifier_dropout_prob,		inplace=lowerCamelCase__		)
        A :       Dict          =   nn.Linear(lowerCamelCase__,		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(lowerCamelCase__		)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,		output_type=lowerCamelCase__,		config_class=_CONFIG_FOR_DOC,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,		)
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        A :       Union[str, Any]          =   return_dict if return_dict is not None else self.config.use_return_dict

        A :       Optional[Any]          =   self.mobilenet_va(lowerCamelCase__,		output_hidden_states=lowerCamelCase__,		return_dict=lowerCamelCase__		)

        A :       Optional[int]          =   outputs.pooler_output if return_dict else outputs[1]

        A :       Optional[int]          =   self.classifier(self.dropout(lowerCamelCase__		)		)

        A :       Any          =   None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    A :       Union[str, Any]          =   """regression"""
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    A :       Union[str, Any]          =   """single_label_classification"""
                else:
                    A :       Tuple          =   """multi_label_classification"""

            if self.config.problem_type == "regression":
                A :       List[str]          =   MSELoss()
                if self.num_labels == 1:
                    A :       List[Any]          =   loss_fct(logits.squeeze(),		labels.squeeze()		)
                else:
                    A :       List[Any]          =   loss_fct(lowerCamelCase__,		lowerCamelCase__		)
            elif self.config.problem_type == "single_label_classification":
                A :       Any          =   CrossEntropyLoss()
                A :       Tuple          =   loss_fct(logits.view(-1,		self.num_labels		),		labels.view(-1		)		)
            elif self.config.problem_type == "multi_label_classification":
                A :       Union[str, Any]          =   BCEWithLogitsLoss()
                A :       Union[str, Any]          =   loss_fct(lowerCamelCase__,		lowerCamelCase__		)

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

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







from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


