





"""simple docstring"""





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


__A											=		logging.get_logger(__name__)

# General docstring
__A											=		"RegNetConfig"

# Base docstring
__A											=		"facebook/regnet-y-040"
__A											=		[1, 1088, 7, 7]

# Image classification docstring
__A											=		"facebook/regnet-y-040"
__A											=		"tabby, tabby cat"

__A											=		[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase = 3    , __UpperCAmelCase = 1    , __UpperCAmelCase = 1    , __UpperCAmelCase = "relu"    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         # The padding and conv has been verified in
         # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
         lowerCAmelCase__			:Dict											=							tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2      )
         lowerCAmelCase__			:Union[str, Any]											=							tf.keras.layers.ConvaD(
             filters=__lowercase    , kernel_size=__lowercase    , strides=__lowercase    , padding='VALID'    , groups=__lowercase    , use_bias=__lowercase    , name='convolution'    , )
         lowerCAmelCase__			:Any											=							tf.keras.layers.BatchNormalization(epsilon=1E-5    , momentum=0.9    , name='normalization'      )
         lowerCAmelCase__			:int											=							ACTaFN[activation] if activation is not None else tf.identity






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							self.convolution(self.padding(__lowercase      )      )
         lowerCAmelCase__			:Optional[int]											=							self.normalization(__lowercase      )
         lowerCAmelCase__			:Tuple											=							self.activation(__lowercase      )
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:List[str]											=							config.num_channels
         lowerCAmelCase__			:Tuple											=							TFRegNetConvLayer(
             out_channels=config.embedding_size    , kernel_size=3    , stride=2    , activation=config.hidden_act    , name='embedder'    , )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							shape_list(__lowercase      )[1]
         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.'      )

         # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
         # So change the input format from `NCHW` to `NHWC`.
         # shape = (batch_size, in_height, in_width, in_channels=num_channels)
         lowerCAmelCase__			:Optional[Any]											=							tf.transpose(__lowercase    , perm=(0, 2, 3, 1)      )
         lowerCAmelCase__			:Any											=							self.embedder(__lowercase      )
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase = 2    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:List[Any]											=							tf.keras.layers.ConvaD(
             filters=__lowercase    , kernel_size=1    , strides=__lowercase    , use_bias=__lowercase    , name='convolution'      )
         lowerCAmelCase__			:Dict											=							tf.keras.layers.BatchNormalization(epsilon=1E-5    , momentum=0.9    , name='normalization'      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = False      ):



         '''simple docstring'''

         return self.normalization(self.convolution(__lowercase      )    , training=__lowercase      )






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:List[Any]											=							tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase    , name='pooler'      )
         lowerCAmelCase__			:Dict											=							[
             tf.keras.layers.ConvaD(filters=__lowercase    , kernel_size=1    , activation='relu'    , name='attention.0'      ),
             tf.keras.layers.ConvaD(filters=__lowercase    , kernel_size=1    , activation='sigmoid'    , name='attention.2'      ),
         ]






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							self.pooler(__lowercase      )
         for layer_module in self.attention:
           lowerCAmelCase__			:Tuple											=							layer_module(__lowercase      )
         lowerCAmelCase__			:Optional[int]											=							hidden_state * pooled
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase = 1    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:Any											=							in_channels != out_channels or stride != 1
         lowerCAmelCase__			:List[str]											=							max(1    , out_channels // config.groups_width      )
         lowerCAmelCase__			:int											=							(
             TFRegNetShortCut(__lowercase    , stride=__lowercase    , name='shortcut'      )
             if should_apply_shortcut
             else tf.keras.layers.Activation('linear'    , name='shortcut'      )
         )
         # `self.layers` instead of `self.layer` because that is a reserved argument.
         lowerCAmelCase__			:Any											=							[
             TFRegNetConvLayer(__lowercase    , kernel_size=1    , activation=config.hidden_act    , name='layer.0'      ),
             TFRegNetConvLayer(
                 __lowercase    , stride=__lowercase    , groups=__lowercase    , activation=config.hidden_act    , name='layer.1'      ),
             TFRegNetConvLayer(__lowercase    , kernel_size=1    , activation=__lowercase    , name='layer.2'      ),
         ]
         lowerCAmelCase__			:List[str]											=							ACTaFN[config.hidden_act]






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							hidden_state
         for layer_module in self.layers:
           lowerCAmelCase__			:Optional[Any]											=							layer_module(__lowercase      )
         lowerCAmelCase__			:Dict											=							self.shortcut(__lowercase      )
         hidden_state += residual
         lowerCAmelCase__			:int											=							self.activation(__lowercase      )
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase = 1    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:Dict											=							in_channels != out_channels or stride != 1
         lowerCAmelCase__			:str											=							max(1    , out_channels // config.groups_width      )
         lowerCAmelCase__			:Optional[Any]											=							(
             TFRegNetShortCut(__lowercase    , stride=__lowercase    , name='shortcut'      )
             if should_apply_shortcut
             else tf.keras.layers.Activation('linear'    , name='shortcut'      )
         )
         lowerCAmelCase__			:Optional[int]											=							[
             TFRegNetConvLayer(__lowercase    , kernel_size=1    , activation=config.hidden_act    , name='layer.0'      ),
             TFRegNetConvLayer(
                 __lowercase    , stride=__lowercase    , groups=__lowercase    , activation=config.hidden_act    , name='layer.1'      ),
             TFRegNetSELayer(__lowercase    , reduced_channels=int(round(in_channels / 4      )      )    , name='layer.2'      ),
             TFRegNetConvLayer(__lowercase    , kernel_size=1    , activation=__lowercase    , name='layer.3'      ),
         ]
         lowerCAmelCase__			:Optional[int]											=							ACTaFN[config.hidden_act]






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							hidden_state
         for layer_module in self.layers:
           lowerCAmelCase__			:List[Any]											=							layer_module(__lowercase      )
         lowerCAmelCase__			:List[str]											=							self.shortcut(__lowercase      )
         hidden_state += residual
         lowerCAmelCase__			:List[str]											=							self.activation(__lowercase      )
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase = 2    , __UpperCAmelCase = 2    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )

         lowerCAmelCase__			:Any											=							TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
         lowerCAmelCase__			:Optional[Any]											=							[
             # downsampling is done in the first layer with stride of 2
             layer(__lowercase    , __lowercase    , __lowercase    , stride=__lowercase    , name='layers.0'      ),
             *[layer(__lowercase    , __lowercase    , __lowercase    , name=F"layers.{i+1}"      ) for i in range(depth - 1      )],
         ]






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for layer_module in self.layers:
           lowerCAmelCase__			:Optional[int]											=							layer_module(__lowercase      )
         return hidden_state






class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = False    , __UpperCAmelCase = True      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							() if output_hidden_states else None

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

           lowerCAmelCase__			:Any											=							stage_module(__lowercase      )

         if output_hidden_states:
           lowerCAmelCase__			:Dict											=							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=__lowercase    , hidden_states=__lowercase      )






@keras_serializable
class 					_lowerCAmelCase   (     tf.keras.layers.Layer       ):
       """simple docstring"""




       __magic_name__     :int		  =					RegNetConfig






       def __init__(    self    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__lowercase      )
         lowerCAmelCase__			:Dict											=							config
         lowerCAmelCase__			:List[str]											=							TFRegNetEmbeddings(__lowercase    , name='embedder'      )
         lowerCAmelCase__			:Optional[int]											=							TFRegNetEncoder(__lowercase    , name='encoder'      )
         lowerCAmelCase__			:str											=							tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase    , name='pooler'      )






       @unpack_inputs
       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = False    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__			:Dict											=							return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__			:Union[str, Any]											=							self.embedder(__lowercase    , training=__lowercase      )

         lowerCAmelCase__			:Union[str, Any]											=							self.encoder(
             __lowercase    , output_hidden_states=__lowercase    , return_dict=__lowercase    , training=__lowercase      )

         lowerCAmelCase__			:Optional[Any]											=							encoder_outputs[0]
         lowerCAmelCase__			:int											=							self.pooler(__lowercase      )

         # Change to NCHW output format have uniformity in the modules
         lowerCAmelCase__			:Union[str, Any]											=							tf.transpose(__lowercase    , perm=(0, 3, 1, 2)      )
         lowerCAmelCase__			:str											=							tf.transpose(__lowercase    , perm=(0, 3, 1, 2)      )

         # Change the other hidden state outputs to NCHW as well
         if output_hidden_states:
           lowerCAmelCase__			:Union[str, Any]											=							tuple([tf.transpose(__lowercase    , perm=(0, 3, 1, 2)      ) for h in encoder_outputs[1]]      )

         if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

         return TFBaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__lowercase    , pooler_output=__lowercase    , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states    , )






class 					_lowerCAmelCase   (     UpperCAmelCase__       ):
       """simple docstring"""




       __magic_name__     :Tuple		  =					RegNetConfig
       __magic_name__     :Tuple		  =					"""regnet"""
       __magic_name__     :Dict		  =					"""pixel_values"""






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)    , dtype=tf.floataa      )}






__A											=		R"\n    Parameters:\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        config ([`RegNetConfig`]): 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"

__A											=		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            [`ConveNextImageProcessor.__call__`] for details.\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"






@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""  ,   UpperCAmelCase__  ,   )
class 					_lowerCAmelCase   (     UpperCAmelCase__       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(__lowercase    , *__lowercase    , **__lowercase      )
         lowerCAmelCase__			:str											=							TFRegNetMainLayer(__lowercase    , name='regnet'      )






       @unpack_inputs
       @add_start_docstrings_to_model_forward(__lowercase      )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC    , output_type=__lowercase    , config_class=_CONFIG_FOR_DOC    , modality='vision'    , expected_output=_EXPECTED_OUTPUT_SHAPE    , )
       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase=False    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__			:int											=							return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__			:Tuple											=							self.regnet(
             pixel_values=__lowercase    , output_hidden_states=__lowercase    , return_dict=__lowercase    , training=__lowercase    , )
         if not return_dict:
           return (outputs[0],) + outputs[1:]

         return TFBaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=outputs.last_hidden_state    , pooler_output=outputs.pooler_output    , hidden_states=outputs.hidden_states    , )






@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """  ,   UpperCAmelCase__  ,   )
class 					_lowerCAmelCase   (     UpperCAmelCase__  ,   UpperCAmelCase__       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(__lowercase    , *__lowercase    , **__lowercase      )
         lowerCAmelCase__			:List[str]											=							config.num_labels
         lowerCAmelCase__			:Optional[int]											=							TFRegNetMainLayer(__lowercase    , name='regnet'      )
         # classification head
         lowerCAmelCase__			:str											=							[
             tf.keras.layers.Flatten(),
             tf.keras.layers.Dense(config.num_labels    , name='classifier.1'      ) if config.num_labels > 0 else tf.identity,
         ]






       @unpack_inputs
       @add_start_docstrings_to_model_forward(__lowercase      )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT    , output_type=__lowercase    , config_class=_CONFIG_FOR_DOC    , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    , )
       def      snake_case						(    self    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase=False    , ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__			:List[Any]											=							return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__			:Union[str, Any]											=							self.regnet(
             __lowercase    , output_hidden_states=__lowercase    , return_dict=__lowercase    , training=__lowercase      )

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

         lowerCAmelCase__			:Optional[Any]											=							self.classifier[0](__lowercase      )
         lowerCAmelCase__			:Union[str, Any]											=							self.classifier[1](__lowercase      )

         lowerCAmelCase__			:List[Any]											=							None if labels is None else self.hf_compute_loss(labels=__lowercase    , logits=__lowercase      )

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

         return TFSequenceClassifierOutput(loss=__lowercase    , logits=__lowercase    , hidden_states=outputs.hidden_states      )






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A=True     ,	_A="pt"							):







	'''simple docstring'''





	snake_case_		    =     {"add_prefix_space": True} if isinstance(_A     ,	_A							) and not line.startswith(" "							) else {}
	snake_case_		    =     padding_side
	return tokenizer(
	    [line]     ,	max_length=_A     ,	padding="max_length" if pad_to_max_length else None     ,	truncation=_A     ,	return_tensors=_A     ,	add_special_tokens=_A     ,	**_A     ,	)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=None     ,	):







	'''simple docstring'''





	snake_case_		    =     input_ids.ne(_A							).any(dim=0							)
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     int       ,	__lowercase							:     List[Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[Any]       ,	__lowercase							:     str       ,	__lowercase							:     Tuple="train"       ,	__lowercase							:     List[str]=None       ,	__lowercase							:     List[Any]=None       ,	__lowercase							:     Optional[Any]=None       ,	__lowercase							:     Union[str, Any]=""       ,	):






							"""simple docstring"""






							super().__init__()
							snake_case_		    =     Path(__lowercase						).joinpath(type_path + ".source"						)
							snake_case_		    =     Path(__lowercase						).joinpath(type_path + ".target"						)
							snake_case_		    =     self.get_char_lens(self.src_file						)
							snake_case_		    =     max_source_length
							snake_case_		    =     max_target_length
							assert min(self.src_lens						) > 0, f"found empty line in {self.src_file}"
							snake_case_		    =     tokenizer
							snake_case_		    =     prefix
							if n_obs is not None:
								snake_case_		    =     self.src_lens[:n_obs]
							snake_case_		    =     src_lang
							snake_case_		    =     tgt_lang






						def __len__(						self							:     List[Any]						):






							"""simple docstring"""






							return len(self.src_lens						)






						def __getitem__(						self							:     List[Any]       ,	__lowercase							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     index + 1  # linecache starts at 1
							snake_case_		    =     self.prefix + linecache.getline(str(self.src_file						)       ,	__lowercase						).rstrip("\n"						)
							snake_case_		    =     linecache.getline(str(self.tgt_file						)       ,	__lowercase						).rstrip("\n"						)
							assert source_line, f"empty source line for index {index}"
							assert tgt_line, f"empty tgt line for index {index}"

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer       ,	__lowercase						):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							snake_case_		    =     (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,	__lowercase						) else self.tokenizer
							)
							snake_case_		    =     self.tokenizer.generator if isinstance(self.tokenizer       ,	__lowercase						) else self.tokenizer

							snake_case_		    =     encode_line(__lowercase       ,	__lowercase       ,	self.max_source_length       ,	"right"						)
							snake_case_		    =     encode_line(__lowercase       ,	__lowercase       ,	self.max_target_length       ,	"right"						)

							snake_case_		    =     source_inputs["input_ids"].squeeze()
							snake_case_		    =     target_inputs["input_ids"].squeeze()
							snake_case_		    =     source_inputs["attention_mask"].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}






						@staticmethod
						def        snake_case__						(						__lowercase							:     Optional[int]						):






							"""simple docstring"""






							return [len(__lowercase						) for x in Path(__lowercase						).open().readlines()]






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     torch.stack([x["input_ids"] for x in batch]						)
							snake_case_		    =     torch.stack([x["attention_mask"] for x in batch]						)
							snake_case_		    =     torch.stack([x["decoder_input_ids"] for x in batch]						)
							snake_case_		    =     (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer       ,	__lowercase						)
							    else self.tokenizer.pad_token_id
							)
							snake_case_		    =     (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer       ,	__lowercase						)
							    else self.tokenizer.pad_token_id
							)
							snake_case_		    =     trim_batch(__lowercase       ,	__lowercase						)
							snake_case_    ,      snake_case_		    =     trim_batch(__lowercase       ,	__lowercase       ,	attention_mask=__lowercase						)
							snake_case_		    =     {
							    "input_ids": source_ids,
							    "attention_mask": source_mask,
							    "decoder_input_ids": y,
							}
							return batch







lowercase__				:     str 		=						getLogger(__name__)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return list(itertools.chain.from_iterable(_A							)							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     get_git_info()
	save_json(_A     ,	os.path.join(_A     ,	"git_log.json"							)							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=4     ,	**_A							):







	'''simple docstring'''





	with open(_A     ,	"w"							) as f:
		json.dump(_A     ,	_A     ,	indent=_A     ,	**_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	with open(_A							) as f:
		return json.load(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     git.Repo(search_parent_directories=_A							)
	snake_case_		    =     {
	    "repo_id": str(_A							),
	    "repo_sha": str(repo.head.object.hexsha							),
	    "repo_branch": str(repo.active_branch							),
	    "hostname": str(socket.gethostname()							),
	}
	return repo_infos


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	return list(map(_A     ,	_A							)							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	with open(_A     ,	"wb"							) as f:
		return pickle.dump(_A     ,	_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	def remove_articles(_A							):
		return re.sub(R"\b(a|an|the)\b"     ,	" "     ,	_A							)

	def white_space_fix(_A							):
		return " ".join(text.split()							)

	def remove_punc(_A							):
		snake_case_		    =     set(string.punctuation							)
		return "".join(ch for ch in text if ch not in exclude							)

	def lower(_A							):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_A							)							)							)							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     normalize_answer(_A							).split()
	snake_case_		    =     normalize_answer(_A							).split()
	snake_case_		    =     Counter(_A							) & Counter(_A							)
	snake_case_		    =     sum(common.values()							)
	if num_same == 0:
		return 0
	snake_case_		    =     1.0 * num_same / len(_A							)
	snake_case_		    =     1.0 * num_same / len(_A							)
	snake_case_		    =     (2 * precision * recall) / (precision + recall)
	return fa


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	return normalize_answer(_A							) == normalize_answer(_A							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	assert len(_A							) == len(_A							)
	snake_case_		    =     0
	for hypo, pred in zip(_A     ,	_A							):
		em += exact_match_score(_A     ,	_A							)
	if len(_A							) > 0:
		em /= len(_A							)
	return {"em": em}


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return model_prefix.startswith("rag"							)






def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	snake_case_		    =     "dropout_rate"
	for p in extra_params:
		if getattr(_A     ,	_A     ,	_A							):
			if not hasattr(_A     ,	_A							) and not hasattr(_A     ,	equivalent_param[p]							):
				logger.info("config doesn't have a `{}` attribute".format(_A							)							)
				delattr(_A     ,	_A							)
				continue
			snake_case_		    =     p if hasattr(_A     ,	_A							) else equivalent_param[p]
			setattr(_A     ,	_A     ,	getattr(_A     ,	_A							)							)
			delattr(_A     ,	_A							)
	return hparams, config
