

"""simple docstring"""





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


_lowercase	      =    logging.get_logger(__name__)

# General docstring
_lowercase	      =    """ResNetConfig"""

# Base docstring
_lowercase	      =    """microsoft/resnet-50"""
_lowercase	      =    [1, 20_48, 7, 7]

# Image classification docstring
_lowercase	      =    """microsoft/resnet-50"""
_lowercase	      =    """tiger cat"""

_lowercase	      =    [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     str = 3      ,SCREAMING_SNAKE_CASE_   :     List[str] = 1      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = "relu"      ,**SCREAMING_SNAKE_CASE_   :     int      ):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					Tuple										=					kernel_size // 2
										snake_case__       :					List[str]										=					tf.keras.layers.ConvaD(
										    __lowerCamelCase      ,kernel_size=__lowerCamelCase      ,strides=__lowerCamelCase      ,padding='''valid'''      ,use_bias=__lowerCamelCase      ,name='''convolution'''      )
										# Use same default momentum and epsilon as PyTorch equivalent
										snake_case__       :					Dict										=					tf.keras.layers.BatchNormalization(epsilon=1E-5      ,momentum=0.9      ,name='''normalization'''      )
										snake_case__       :					List[Any]										=					ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''      )

				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										# Pad to match that done in the PyTorch Conv2D model
										snake_case__       :					Dict										=					(self.pad_value, self.pad_value)
										snake_case__       :					List[Any]										=					tf.pad(__lowerCamelCase      ,[(0, 0), height_pad, width_pad, (0, 0)]      )
										snake_case__       :					int										=					self.conv(__lowerCamelCase      )
										return hidden_state





				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = False      ):
										snake_case__       :					Optional[int]										=					self.convolution(__lowerCamelCase      )
										snake_case__       :					List[Any]										=					self.normalization(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					Optional[int]										=					self.activation(__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,**SCREAMING_SNAKE_CASE_   :     List[str]      ):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					Optional[int]										=					TFResNetConvLayer(
										    config.embedding_size      ,kernel_size=7      ,stride=2      ,activation=config.hidden_act      ,name='''embedder'''      ,)
										snake_case__       :					Optional[Any]										=					tf.keras.layers.MaxPoolaD(pool_size=3      ,strides=2      ,padding='''valid'''      ,name='''pooler'''      )
										snake_case__       :					int										=					config.num_channels





				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Optional[Any] = False      ):
										snake_case__       :					Union[str, Any]										=					shape_list(__lowerCamelCase      )
										if tf.executing_eagerly() and num_channels != self.num_channels:
																raise ValueError(
																    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''      )
										snake_case__       :					Optional[int]										=					pixel_values
										snake_case__       :					str										=					self.embedder(__lowerCamelCase      )
										snake_case__       :					int										=					tf.pad(__lowerCamelCase      ,[[0, 0], [1, 1], [1, 1], [0, 0]]      )
										snake_case__       :					List[str]										=					self.pooler(__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     Dict = 2      ,**SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					Tuple										=					tf.keras.layers.ConvaD(
										    __lowerCamelCase      ,kernel_size=1      ,strides=__lowerCamelCase      ,use_bias=__lowerCamelCase      ,name='''convolution'''      )
										# Use same default momentum and epsilon as PyTorch equivalent
										snake_case__       :					int										=					tf.keras.layers.BatchNormalization(epsilon=1E-5      ,momentum=0.9      ,name='''normalization'''      )





				def       _lowerCamelCase					(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = False      ):
										snake_case__       :					Any										=					x
										snake_case__       :					List[Any]										=					self.convolution(__lowerCamelCase      )
										snake_case__       :					List[str]										=					self.normalization(__lowerCamelCase      ,training=__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Tuple = 1      ,SCREAMING_SNAKE_CASE_   :     Tuple = "relu"      ,**SCREAMING_SNAKE_CASE_   :     int      ):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					int										=					in_channels != out_channels or stride != 1
										snake_case__       :					List[Any]										=					TFResNetConvLayer(__lowerCamelCase      ,stride=__lowerCamelCase      ,name='''layer.0'''      )
										snake_case__       :					str										=					TFResNetConvLayer(__lowerCamelCase      ,activation=__lowerCamelCase      ,name='''layer.1'''      )
										snake_case__       :					Optional[Any]										=					(
										    TFResNetShortCut(__lowerCamelCase      ,stride=__lowerCamelCase      ,name='''shortcut'''      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation('''linear'''      ,name='''shortcut'''      )
										)
										snake_case__       :					int										=					ACTaFN[activation]





				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = False      ):
										snake_case__       :					str										=					hidden_state
										snake_case__       :					Union[str, Any]										=					self.conva(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					int										=					self.conva(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					Any										=					self.shortcut(__lowerCamelCase      ,training=__lowerCamelCase      )
										hidden_state += residual
										snake_case__       :					Union[str, Any]										=					self.activation(__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = 1      ,SCREAMING_SNAKE_CASE_   :     Dict = "relu"      ,SCREAMING_SNAKE_CASE_   :     Any = 4      ,**SCREAMING_SNAKE_CASE_   :     List[Any]      ,):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					Any										=					in_channels != out_channels or stride != 1
										snake_case__       :					Any										=					out_channels // reduction
										snake_case__       :					int										=					TFResNetConvLayer(__lowerCamelCase      ,kernel_size=1      ,name='''layer.0'''      )
										snake_case__       :					str										=					TFResNetConvLayer(__lowerCamelCase      ,stride=__lowerCamelCase      ,name='''layer.1'''      )
										snake_case__       :					Union[str, Any]										=					TFResNetConvLayer(__lowerCamelCase      ,kernel_size=1      ,activation=__lowerCamelCase      ,name='''layer.2'''      )
										snake_case__       :					Optional[Any]										=					(
										    TFResNetShortCut(__lowerCamelCase      ,stride=__lowerCamelCase      ,name='''shortcut'''      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation('''linear'''      ,name='''shortcut'''      )
										)
										snake_case__       :					Union[str, Any]										=					ACTaFN[activation]





				def       _lowerCamelCase					(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     List[str] = False      ):
										snake_case__       :					int										=					hidden_state
										snake_case__       :					Tuple										=					self.conva(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					Dict										=					self.conva(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					List[Any]										=					self.conva(__lowerCamelCase      ,training=__lowerCamelCase      )
										snake_case__       :					Union[str, Any]										=					self.shortcut(__lowerCamelCase      ,training=__lowerCamelCase      )
										hidden_state += residual
										snake_case__       :					Tuple										=					self.activation(__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple = 2      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = 2      ,**SCREAMING_SNAKE_CASE_   :     Tuple      ):
										super().__init__(**__lowerCamelCase      )

										snake_case__       :					Union[str, Any]										=					TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

										snake_case__       :					Optional[int]										=					[layer(__lowerCamelCase      ,__lowerCamelCase      ,stride=__lowerCamelCase      ,activation=config.hidden_act      ,name='''layers.0'''      )]
										layers += [
										    layer(__lowerCamelCase      ,__lowerCamelCase      ,activation=config.hidden_act      ,name=F'''layers.{i + 1}'''      )
										    for i in range(depth - 1      )
										]
										snake_case__       :					Optional[Any]										=					layers





				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Any = False      ):
										for layer in self.stage_layers:
																snake_case__       :					Dict										=					layer(__lowerCamelCase      ,training=__lowerCamelCase      )
										return hidden_state







class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				def __init__(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,**SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										super().__init__(**__lowerCamelCase      )
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										snake_case__       :					Dict										=					[
										    TFResNetStage(
										        __lowerCamelCase      ,config.embedding_size      ,config.hidden_sizes[0]      ,stride=2 if config.downsample_in_first_stage else 1      ,depth=config.depths[0]      ,name='''stages.0'''      ,)
										]
										for i, (in_channels, out_channels, depth) in enumerate(
										    zip(config.hidden_sizes      ,config.hidden_sizes[1:]      ,config.depths[1:]      )      ):
																self.stages.append(TFResNetStage(__lowerCamelCase      ,__lowerCamelCase      ,__lowerCamelCase      ,depth=__lowerCamelCase      ,name=F'''stages.{i + 1}'''      )      )





				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Tuple = False      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = True      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = False      ,):
										snake_case__       :					List[str]										=					() if output_hidden_states else None

										for stage_module in self.stages:
																if output_hidden_states:
																						snake_case__       :					str										=					hidden_states + (hidden_state,)

																snake_case__       :					int										=					stage_module(__lowerCamelCase      ,training=__lowerCamelCase      )

										if output_hidden_states:
																snake_case__       :					List[str]										=					hidden_states + (hidden_state,)

										if not return_dict:
																return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

										return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCamelCase      ,hidden_states=__lowerCamelCase      )







class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				_lowerCAmelCase				=ResNetConfig
				_lowerCAmelCase				="resnet"
				_lowerCAmelCase				="pixel_values"

				@property
				def       _lowerCamelCase					(   self   :     List[str]      ):
										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)      ,dtype=tf.floataa      )}







_lowercase	      =    r"""\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"""


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

@keras_serializable
class       __lowercase       (			tf.keras.layers.Layer	):


				'''simple docstring'''


				_lowerCAmelCase				=ResNetConfig

				def __init__(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Any      ,**SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										super().__init__(**__lowerCamelCase      )
										snake_case__       :					Any										=					config
										snake_case__       :					Any										=					TFResNetEmbeddings(__lowerCamelCase      ,name='''embedder'''      )
										snake_case__       :					Any										=					TFResNetEncoder(__lowerCamelCase      ,name='''encoder'''      )
										snake_case__       :					str										=					tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCamelCase      )





				@unpack_inputs
				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = None      ,SCREAMING_SNAKE_CASE_   :     List[Any] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[int] = False      ,):
										snake_case__       :					int										=					(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										snake_case__       :					Union[str, Any]										=					return_dict if return_dict is not None else self.config.use_return_dict

										# TF 2.0 image layers can't use NCHW format when running on CPU.
										# We transpose to NHWC format and then transpose back after the full forward pass.
										# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
										snake_case__       :					List[Any]										=					tf.transpose(__lowerCamelCase      ,perm=[0, 2, 3, 1]      )
										snake_case__       :					List[Any]										=					self.embedder(__lowerCamelCase      ,training=__lowerCamelCase      )

										snake_case__       :					str										=					self.encoder(
										    __lowerCamelCase      ,output_hidden_states=__lowerCamelCase      ,return_dict=__lowerCamelCase      ,training=__lowerCamelCase      )

										snake_case__       :					str										=					encoder_outputs[0]

										snake_case__       :					List[Any]										=					self.pooler(__lowerCamelCase      )

										# Transpose all the outputs to the NCHW format
										# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
										snake_case__       :					Union[str, Any]										=					tf.transpose(__lowerCamelCase      ,(0, 3, 1, 2)      )
										snake_case__       :					Union[str, Any]										=					tf.transpose(__lowerCamelCase      ,(0, 3, 1, 2)      )
										snake_case__       :					int										=					()
										for hidden_state in encoder_outputs[1:]:
																snake_case__       :					Optional[int]										=					hidden_states + tuple(tf.transpose(__lowerCamelCase      ,(0, 3, 1, 2)      ) for h in hidden_state      )

										if not return_dict:
																return (last_hidden_state, pooled_output) + hidden_states

										snake_case__       :					Dict										=					hidden_states if output_hidden_states else None

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=__lowerCamelCase      ,pooler_output=__lowerCamelCase      ,hidden_states=__lowerCamelCase      ,)







@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."						,   snake_case_						,   )
class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				def __init__(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     Any      ,**SCREAMING_SNAKE_CASE_   :     int      ):
										super().__init__(__lowerCamelCase      ,**__lowerCamelCase      )
										snake_case__       :					List[str]										=					TFResNetMainLayer(config=__lowerCamelCase      ,name='''resnet'''      )





				@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      ,)
				@unpack_inputs
				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = None      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = None      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = False      ,):
										snake_case__       :					List[Any]										=					(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										snake_case__       :					List[str]										=					return_dict if return_dict is not None else self.config.use_return_dict

										snake_case__       :					Dict										=					self.resnet(
										    pixel_values=__lowerCamelCase      ,output_hidden_states=__lowerCamelCase      ,return_dict=__lowerCamelCase      ,training=__lowerCamelCase      ,)
										return resnet_outputs







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


				'''simple docstring'''


				def __init__(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,**SCREAMING_SNAKE_CASE_   :     int      ):
										super().__init__(__lowerCamelCase      ,**__lowerCamelCase      )
										snake_case__       :					Optional[int]										=					config.num_labels
										snake_case__       :					Any										=					TFResNetMainLayer(__lowerCamelCase      ,name='''resnet'''      )
										# classification head
										snake_case__       :					str										=					(
										    tf.keras.layers.Dense(config.num_labels      ,name='''classifier.1'''      )
										    if config.num_labels > 0
										    else tf.keras.layers.Activation('''linear'''      ,name='''classifier.1'''      )
										)

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										snake_case__       :					Any										=					tf.keras.layers.Flatten()(__lowerCamelCase      )
										snake_case__       :					int										=					self.classifier_layer(__lowerCamelCase      )
										return logits





				@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      ,)
				@unpack_inputs
				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Dict = None      ,SCREAMING_SNAKE_CASE_   :     Any = None      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any] = None      ,SCREAMING_SNAKE_CASE_   :     int = None      ,SCREAMING_SNAKE_CASE_   :     Dict = False      ,):
										snake_case__       :					str										=					return_dict if return_dict is not None else self.config.use_return_dict

										snake_case__       :					Union[str, Any]										=					self.resnet(
										    __lowerCamelCase      ,output_hidden_states=__lowerCamelCase      ,return_dict=__lowerCamelCase      ,training=__lowerCamelCase      )

										snake_case__       :					str										=					outputs.pooler_output if return_dict else outputs[1]

										snake_case__       :					Tuple										=					self.classifier(__lowerCamelCase      )

										snake_case__       :					str										=					None if labels is None else self.hf_compute_loss(__lowerCamelCase      ,__lowerCamelCase      )

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

										return TFImageClassifierOutputWithNoAttention(loss=__lowerCamelCase      ,logits=__lowerCamelCase      ,hidden_states=outputs.hidden_states      )








"""simple docstring"""





import argparse
import os
import sys
import urllib.request
import zipfile


_lowercase	      =    ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
_lowercase	      =    {
    """CoLA""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4""",
    """SST""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8""",
    """MRPC""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc""",
    """QQP""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5""",
    """STS""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5""",
    """MNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce""",
    """SNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df""",
    """QNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601""",
    """RTE""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb""",
    """WNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf""",
    """diagnostic""": """https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D""",
}

_lowercase	      =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
_lowercase	      =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						print(f'''Downloading and extracting {task}...'''		)
						snake_case__       :					Optional[Any]										=					f'''{task}.zip'''
						urllib.request.urlretrieve(TASKaPATH[task]      ,			lowerCamelCase		)
						with zipfile.ZipFile(lowerCamelCase		) as zip_ref:
												zip_ref.extractall(lowerCamelCase		)
						os.remove(lowerCamelCase		)
						print('''\tCompleted!'''		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						print('''Processing MRPC...'''		)
						snake_case__       :					Union[str, Any]										=					os.path.join(lowerCamelCase      ,			'''MRPC'''		)
						if not os.path.isdir(lowerCamelCase		):
												os.mkdir(lowerCamelCase		)
						if path_to_data:
												snake_case__       :					List[Any]										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Any										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_test.txt'''		)
						else:
												print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN		)
												snake_case__       :					Tuple										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Optional[int]										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_test.txt'''		)
												urllib.request.urlretrieve(lowerCamelCase      ,			lowerCamelCase		)
												urllib.request.urlretrieve(lowerCamelCase      ,			lowerCamelCase		)
						if not os.path.isfile(lowerCamelCase		):
												raise ValueError(f'''Train data not found at {mrpc_train_file}'''		)
						if not os.path.isfile(lowerCamelCase		):
												raise ValueError(f'''Test data not found at {mrpc_test_file}'''		)
						urllib.request.urlretrieve(TASKaPATH['''MRPC''']      ,			os.path.join(lowerCamelCase      ,			'''dev_ids.tsv'''		)		)

						snake_case__       :					Optional[Any]										=					[]
						with open(os.path.join(lowerCamelCase      ,			'''dev_ids.tsv'''		)      ,			encoding='''utf8'''		) as ids_fh:
												for row in ids_fh:
																		dev_ids.append(row.strip().split('''\t'''		)		)

						with open(lowerCamelCase      ,			encoding='''utf8'''		) as data_fh, open(
						    os.path.join(lowerCamelCase      ,			'''train.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as train_fh, open(os.path.join(lowerCamelCase      ,			'''dev.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as dev_fh:
												snake_case__       :					Any										=					data_fh.readline()
												train_fh.write(lowerCamelCase		)
												dev_fh.write(lowerCamelCase		)
												for row in data_fh:
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					Dict										=					row.strip().split('''\t'''		)
																		if [ida, ida] in dev_ids:
																								dev_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)		)
																		else:
																								train_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)		)

						with open(lowerCamelCase      ,			encoding='''utf8'''		) as data_fh, open(
						    os.path.join(lowerCamelCase      ,			'''test.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as test_fh:
												snake_case__       :					Tuple										=					data_fh.readline()
												test_fh.write('''index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'''		)
												for idx, row in enumerate(lowerCamelCase		):
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					Union[str, Any]										=					row.strip().split('''\t'''		)
																		test_fh.write('''%d\t%s\t%s\t%s\t%s\n''' % (idx, ida, ida, sa, sa)		)
						print('''\tCompleted!'''		)

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						print('''Downloading and extracting diagnostic...'''		)
						if not os.path.isdir(os.path.join(lowerCamelCase      ,			'''diagnostic'''		)		):
												os.mkdir(os.path.join(lowerCamelCase      ,			'''diagnostic'''		)		)
						snake_case__       :					Any										=					os.path.join(lowerCamelCase      ,			'''diagnostic'''      ,			'''diagnostic.tsv'''		)
						urllib.request.urlretrieve(TASKaPATH['''diagnostic''']      ,			lowerCamelCase		)
						print('''\tCompleted!'''		)
						return

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					task_names.split(''','''		)
						if "all" in task_names:
												snake_case__       :					str										=					TASKS
						else:
												snake_case__       :					Union[str, Any]										=					[]
												for task_name in task_names:
																		if task_name not in TASKS:
																								raise ValueError(f'''Task {task_name} not found!'''		)
																		tasks.append(lowerCamelCase		)
						return tasks

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					int										=					argparse.ArgumentParser()
						parser.add_argument('''--data_dir'''      ,			help='''directory to save data to'''      ,			type=lowerCamelCase      ,			default='''glue_data'''		)
						parser.add_argument(
						    '''--tasks'''      ,			help='''tasks to download data for as a comma separated string'''      ,			type=lowerCamelCase      ,			default='''all'''		)
						parser.add_argument(
						    '''--path_to_mrpc'''      ,			help='''path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'''      ,			type=lowerCamelCase      ,			default=''''''      ,			)
						snake_case__       :					Dict										=					parser.parse_args(lowerCamelCase		)

						if not os.path.isdir(args.data_dir		):
												os.mkdir(args.data_dir		)
						snake_case__       :					Union[str, Any]										=					get_tasks(args.tasks		)

						for task in tasks:
												if task == "MRPC":
																		format_mrpc(args.data_dir      ,			args.path_to_mrpc		)
												elif task == "diagnostic":
																		download_diagnostic(args.data_dir		)
												else:
																		download_and_extract(lowerCamelCase      ,			args.data_dir		)


if __name__ == "__main__":
							sys.exit(main(sys.argv[1:]))


