




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__A						:							int							=  logging.get_logger(__name__)

# General docstring
__A						:							str							=  """ResNetConfig"""

# Base docstring
__A						:							List[Any]							=  """microsoft/resnet-50"""
__A						:							Union[str, Any]							=  [1, 2_0_4_8, 7, 7]

# Image classification docstring
__A						:							Tuple							=  """microsoft/resnet-50"""
__A						:							Union[str, Any]							=  """tiger cat"""

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




class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	List[str]       ,     a       :	int       ,     a       :	Union[str, Any] = 3       ,     a       :	Dict = 1       ,     a       :	Any = "relu"			)			->					Dict:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					nn.Convad(
										    a       ,     a       ,     kernel_size=a       ,     stride=a       ,     padding=kernel_size // 2       ,     bias=a			)
										SCREAMING_SNAKE_CASE           =					nn.BatchNormad(a			)
										SCREAMING_SNAKE_CASE           =					ACTaFN[activation] if activation is not None else nn.Identity()







							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.convolution(a			)
										SCREAMING_SNAKE_CASE           =					self.normalization(a			)
										SCREAMING_SNAKE_CASE           =					self.activation(a			)
										return hidden_state





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	List[Any]       ,     a       :	Union[str, Any]			)			->					Optional[Any]:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					ResNetConvLayer(
										    config.num_channels       ,     config.embedding_size       ,     kernel_size=7       ,     stride=2       ,     activation=config.hidden_act			)
										SCREAMING_SNAKE_CASE           =					nn.MaxPoolad(kernel_size=3       ,     stride=2       ,     padding=1			)
										SCREAMING_SNAKE_CASE           =					config.num_channels







							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					pixel_values.shape[1]
										if 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."""			)
										SCREAMING_SNAKE_CASE           =					self.embedder(a			)
										SCREAMING_SNAKE_CASE           =					self.pooler(a			)
										return embedding





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	List[str]       ,     a       :	str       ,     a       :	Optional[int]       ,     a       :	Any = 2			)			->					Optional[Any]:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					nn.Convad(a       ,     a       ,     kernel_size=1       ,     stride=a       ,     bias=a			)
										SCREAMING_SNAKE_CASE           =					nn.BatchNormad(a			)







							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[str]			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.convolution(a			)
										SCREAMING_SNAKE_CASE           =					self.normalization(a			)
										return hidden_state





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	List[Any]       ,     a       :	Tuple       ,     a       :	Optional[Any] = 1       ,     a       :	int = "relu"			)			->					Tuple:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE           =					(
										    ResNetShortCut(a       ,     a       ,     stride=a			) if should_apply_shortcut else nn.Identity()
										)
										SCREAMING_SNAKE_CASE           =					nn.Sequential(
										    ResNetConvLayer(a       ,     a       ,     stride=a			)       ,     ResNetConvLayer(a       ,     a       ,     activation=a			)       ,     )
										SCREAMING_SNAKE_CASE           =					ACTaFN[activation]







							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Optional[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					hidden_state
										SCREAMING_SNAKE_CASE           =					self.layer(a			)
										SCREAMING_SNAKE_CASE           =					self.shortcut(a			)
										hidden_state += residual
										SCREAMING_SNAKE_CASE           =					self.activation(a			)
										return hidden_state





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Union[str, Any]       ,     a       :	int       ,     a       :	Union[str, Any]       ,     a       :	Optional[int] = 1       ,     a       :	Optional[int] = "relu"       ,     a       :	Dict = 4			)			->					Union[str, Any]:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE           =					out_channels // reduction
										SCREAMING_SNAKE_CASE           =					(
										    ResNetShortCut(a       ,     a       ,     stride=a			) if should_apply_shortcut else nn.Identity()
										)
										SCREAMING_SNAKE_CASE           =					nn.Sequential(
										    ResNetConvLayer(a       ,     a       ,     kernel_size=1			)       ,     ResNetConvLayer(a       ,     a       ,     stride=a			)       ,     ResNetConvLayer(a       ,     a       ,     kernel_size=1       ,     activation=a			)       ,     )
										SCREAMING_SNAKE_CASE           =					ACTaFN[activation]







							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Optional[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					hidden_state
										SCREAMING_SNAKE_CASE           =					self.layer(a			)
										SCREAMING_SNAKE_CASE           =					self.shortcut(a			)
										hidden_state += residual
										SCREAMING_SNAKE_CASE           =					self.activation(a			)
										return hidden_state





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Union[str, Any]       ,     a       :	Tuple       ,     a       :	List[str]       ,     a       :	Dict       ,     a       :	Optional[Any] = 2       ,     a       :	List[str] = 2       ,     )			->					str:
										super().__init__()

										SCREAMING_SNAKE_CASE           =					ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

										SCREAMING_SNAKE_CASE           =					nn.Sequential(
										    # downsampling is done in the first layer with stride of 2
										    layer(a       ,     a       ,     stride=a       ,     activation=config.hidden_act			)       ,     *[layer(a       ,     a       ,     activation=config.hidden_act			) for _ in range(depth - 1			)]       ,     )







							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Dict			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					input
										for layer in self.layers:
													SCREAMING_SNAKE_CASE           =					layer(a			)
										return hidden_state





class 			UpperCAmelCase_      (   nn.Module       ):




							'''simple docstring'''



							def __init__(					self       :	Union[str, Any]       ,     a       :	Union[str, Any]			)			->					Any:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					nn.ModuleList([]			)
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    ResNetStage(
										        a       ,     config.embedding_size       ,     config.hidden_sizes[0]       ,     stride=2 if config.downsample_in_first_stage else 1       ,     depth=config.depths[0]       ,     )			)
										SCREAMING_SNAKE_CASE           =					zip(config.hidden_sizes       ,     config.hidden_sizes[1:]			)
										for (in_channels, out_channels), depth in zip(a       ,     config.depths[1:]			):
													self.stages.append(ResNetStage(a       ,     a       ,     a       ,     depth=a			)			)







							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Dict       ,     a       :	Union[str, Any] = False       ,     a       :	Any = True			)			->					Any:
										SCREAMING_SNAKE_CASE           =					() if output_hidden_states else None

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

													SCREAMING_SNAKE_CASE           =					stage_module(a			)

										if output_hidden_states:
													SCREAMING_SNAKE_CASE           =					hidden_states + (hidden_state,)

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

										return BaseModelOutputWithNoAttention(
										    last_hidden_state=a       ,     hidden_states=a       ,     )





class 			UpperCAmelCase_      (   snake_case_       ):




							'''simple docstring'''



							a__						  =     ResNetConfig
							a__						  =     """resnet"""
							a__						  =     """pixel_values"""
							a__						  =     True





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	List[str]			)			->					Optional[Any]:
										if isinstance(a       ,     nn.Convad			):
													nn.init.kaiming_normal_(module.weight       ,     mode="""fan_out"""       ,     nonlinearity="""relu"""			)
										elif isinstance(a       ,     (nn.BatchNormad, nn.GroupNorm)			):
													nn.init.constant_(module.weight       ,     1			)
													nn.init.constant_(module.bias       ,     0			)







							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[Any]       ,     a       :	Tuple=False			)			->					List[Any]:
										if isinstance(a       ,     a			):
													SCREAMING_SNAKE_CASE           =					value





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

__A						:							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
            [`ConvNextImageProcessor.__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 ResNet model outputting raw features without any specific head on top.'''		,      snake_case_		,      )
class 			UpperCAmelCase_      (   snake_case_       ):




							'''simple docstring'''



							def __init__(					self       :	Optional[Any]       ,     a       :	int			)			->					List[str]:
										super().__init__(a			)
										SCREAMING_SNAKE_CASE           =					config
										SCREAMING_SNAKE_CASE           =					ResNetEmbeddings(a			)
										SCREAMING_SNAKE_CASE           =					ResNetEncoder(a			)
										SCREAMING_SNAKE_CASE           =					nn.AdaptiveAvgPoolad((1, 1)			)
										# Initialize weights and apply final processing
										self.post_init()







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

										SCREAMING_SNAKE_CASE           =					self.embedder(a			)

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

										SCREAMING_SNAKE_CASE           =					encoder_outputs[0]

										SCREAMING_SNAKE_CASE           =					self.pooler(a			)

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

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





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




							'''simple docstring'''



							def __init__(					self       :	Optional[Any]       ,     a       :	Any			)			->					List[Any]:
										super().__init__(a			)
										SCREAMING_SNAKE_CASE           =					config.num_labels
										SCREAMING_SNAKE_CASE           =					ResNetModel(a			)
										# classification head
										SCREAMING_SNAKE_CASE           =					nn.Sequential(
										    nn.Flatten()       ,     nn.Linear(config.hidden_sizes[-1]       ,     config.num_labels			) if config.num_labels > 0 else nn.Identity()       ,     )
										# initialize weights and apply final processing
										self.post_init()







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

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

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

										SCREAMING_SNAKE_CASE           =					self.classifier(a			)

										SCREAMING_SNAKE_CASE           =					None

										if labels is not None:
													if self.config.problem_type is None:
																if self.num_labels == 1:
																			SCREAMING_SNAKE_CASE           =					"""regression"""
																elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																			SCREAMING_SNAKE_CASE           =					"""single_label_classification"""
																else:
																			SCREAMING_SNAKE_CASE           =					"""multi_label_classification"""
													if self.config.problem_type == "regression":
																SCREAMING_SNAKE_CASE           =					MSELoss()
																if self.num_labels == 1:
																			SCREAMING_SNAKE_CASE           =					loss_fct(logits.squeeze()       ,     labels.squeeze()			)
																else:
																			SCREAMING_SNAKE_CASE           =					loss_fct(a       ,     a			)
													elif self.config.problem_type == "single_label_classification":
																SCREAMING_SNAKE_CASE           =					CrossEntropyLoss()
																SCREAMING_SNAKE_CASE           =					loss_fct(logits.view(-1       ,     self.num_labels			)       ,     labels.view(-1			)			)
													elif self.config.problem_type == "multi_label_classification":
																SCREAMING_SNAKE_CASE           =					BCEWithLogitsLoss()
																SCREAMING_SNAKE_CASE           =					loss_fct(a       ,     a			)

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

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





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''		,      snake_case_		,      )
class 			UpperCAmelCase_      (   snake_case_		,      snake_case_       ):




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	str			)			->					Optional[Any]:
										super().__init__(a			)
										super()._init_backbone(a			)

										SCREAMING_SNAKE_CASE           =					[config.embedding_size] + config.hidden_sizes
										SCREAMING_SNAKE_CASE           =					ResNetEmbeddings(a			)
										SCREAMING_SNAKE_CASE           =					ResNetEncoder(a			)

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







							@add_start_docstrings_to_model_forward(a			)
							@replace_return_docstrings(output_type=a       ,     config_class=_CONFIG_FOR_DOC			)
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Union[str, Any]       ,     a       :	Optional[Any] = None       ,     a       :	List[str] = None			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					return_dict if return_dict is not None else self.config.use_return_dict
										SCREAMING_SNAKE_CASE           =					(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										SCREAMING_SNAKE_CASE           =					self.embedder(a			)

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

										SCREAMING_SNAKE_CASE           =					outputs.hidden_states

										SCREAMING_SNAKE_CASE           =					()
										for idx, stage in enumerate(self.stage_names			):
													if stage in self.out_features:
																feature_maps += (hidden_states[idx],)

										if not return_dict:
													SCREAMING_SNAKE_CASE           =					(feature_maps,)
													if output_hidden_states:
																output += (outputs.hidden_states,)
													return output

										return BackboneOutput(
										    feature_maps=a       ,     hidden_states=outputs.hidden_states if output_hidden_states else None       ,     attentions=a       ,     )












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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=True					,	SCREAMING_SNAKE_CASE="pt"   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{"""add_prefix_space""": True} if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) and not line.startswith(""" """   ) else {}
			SCREAMING_SNAKE_CASE           =					padding_side
			return tokenizer(
			    [line]					,	max_length=SCREAMING_SNAKE_CASE					,	padding="""max_length""" if pad_to_max_length else None					,	truncation=SCREAMING_SNAKE_CASE					,	return_tensors=SCREAMING_SNAKE_CASE					,	add_special_tokens=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE					,	)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					input_ids.ne(SCREAMING_SNAKE_CASE   ).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_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Any       ,     a       :	int       ,     a       :	List[str]       ,     a       :	Dict       ,     a       :	List[Any]       ,     a       :	Optional[Any]="train"       ,     a       :	Tuple=None       ,     a       :	List[str]=None       ,     a       :	Optional[int]=None       ,     a       :	List[str]=""       ,     )			->					Optional[Any]:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(type_path + """.source"""			)
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(type_path + """.target"""			)
										SCREAMING_SNAKE_CASE           =					self.get_char_lens(self.src_file			)
										SCREAMING_SNAKE_CASE           =					max_source_length
										SCREAMING_SNAKE_CASE           =					max_target_length
										assert min(self.src_lens			) > 0, f"""found empty line in {self.src_file}"""
										SCREAMING_SNAKE_CASE           =					tokenizer
										SCREAMING_SNAKE_CASE           =					prefix
										if n_obs is not None:
													SCREAMING_SNAKE_CASE           =					self.src_lens[:n_obs]
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang





							def __len__(					self       :	Union[str, Any]			)			->					Dict:
										return len(self.src_lens			)





							def __getitem__(					self       :	Any       ,     a       :	Optional[int]			)			->					Dict[str, torch.Tensor]:
										SCREAMING_SNAKE_CASE           =					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE           =					self.prefix + linecache.getline(str(self.src_file			)       ,     a			).rstrip("""\n"""			)
										SCREAMING_SNAKE_CASE           =					linecache.getline(str(self.tgt_file			)       ,     a			).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       ,     a			):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

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

										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_source_length       ,     """right"""			)
										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_target_length       ,     """right"""			)

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





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Optional[int]			)			->					List[Any]:
										return [len(a			) for x in Path(a			).open().readlines()]







							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	List[Any]			)			->					Dict[str, torch.Tensor]:
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""input_ids"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""attention_mask"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""decoder_input_ids"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer       ,     a			)
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer       ,     a			)
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE           =					trim_batch(a       ,     a			)
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					trim_batch(a       ,     a       ,     attention_mask=a			)
										SCREAMING_SNAKE_CASE           =					{
										    """input_ids""": source_ids,
										    """attention_mask""": source_mask,
										    """decoder_input_ids""": y,
										}
										return batch





__A						:							Tuple							=  getLogger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					get_git_info()
			save_json(SCREAMING_SNAKE_CASE					,	os.path.join(SCREAMING_SNAKE_CASE					,	"""git_log.json"""   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=4					,	**SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as f:
						json.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	indent=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE   ) as f:
						return json.load(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					{
			    """repo_id""": str(SCREAMING_SNAKE_CASE   ),
			    """repo_sha""": str(repo.head.object.hexsha   ),
			    """repo_branch""": str(repo.active_branch   ),
			    """hostname""": str(socket.gethostname()   ),
			}
			return repo_infos
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(map(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""wb"""   ) as f:
						return pickle.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					Counter(SCREAMING_SNAKE_CASE   ) & Counter(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					sum(common.values()   )
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					(2 * precision * recall) / (precision + recall)
			return fa
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			assert len(SCREAMING_SNAKE_CASE   ) == len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					0
			for hypo, pred in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						em += exact_match_score(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			if len(SCREAMING_SNAKE_CASE   ) > 0:
						em /= len(SCREAMING_SNAKE_CASE   )
			return {"em": em}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return model_prefix.startswith("""rag"""   )




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






