




'''simple docstring'''


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class      A       (						_a       ):
							lowercase_							     =							42



class      A       (						nn.Module       ):







							def __init__(    self			:    str				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Dict=("DownEncoderBlock2D",)				,     lowerCAmelCase_			:    int=(64,)				,     lowerCAmelCase_			:    Tuple=2				,     lowerCAmelCase_			:    Optional[int]=32				,     lowerCAmelCase_			:    int="silu"				,     lowerCAmelCase_			:    Optional[Any]=True				,     )     ->				List[str]:


														"""simple docstring"""


														super().__init__()
														_a      	=							layers_per_block

														_a      	=							torch.nn.Convad(
														    lowerCAmelCase_				,     block_out_channels[0]				,     kernel_size=3				,     stride=1				,     padding=1				,     )

														_a      	=							None
														_a      	=							nn.ModuleList([]   )

														# down
														_a      	=							block_out_channels[0]
														for i, down_block_type in enumerate(lowerCAmelCase_   ):
																					_a      	=							output_channel
																					_a      	=							block_out_channels[i]
																					_a      	=							i == len(lowerCAmelCase_   ) - 1

																					_a      	=							get_down_block(
																					    lowerCAmelCase_				,     num_layers=self.layers_per_block				,     in_channels=lowerCAmelCase_				,     out_channels=lowerCAmelCase_				,     add_downsample=not is_final_block				,     resnet_eps=1e-6				,     downsample_padding=0				,     resnet_act_fn=lowerCAmelCase_				,     resnet_groups=lowerCAmelCase_				,     attention_head_dim=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )
																					self.down_blocks.append(lowerCAmelCase_   )

														# mid
														_a      	=							UNetMidBlockaD(
														    in_channels=block_out_channels[-1]				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     output_scale_factor=1				,     resnet_time_scale_shift='''default'''				,     attention_head_dim=block_out_channels[-1]				,     resnet_groups=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )

														# out
														_a      	=							nn.GroupNorm(num_channels=block_out_channels[-1]				,     num_groups=lowerCAmelCase_				,     eps=1e-6   )
														_a      	=							nn.SiLU()

														_a      	=							2 * out_channels if double_z else out_channels
														_a      	=							nn.Convad(block_out_channels[-1]				,     lowerCAmelCase_				,     3				,     padding=1   )

														_a      	=							False







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							x
														_a      	=							self.conv_in(lowerCAmelCase_   )

														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCAmelCase_			:    Tuple   ):
																												def custom_forward(*lowerCAmelCase_			:    Tuple   ):
																																			return module(*lowerCAmelCase_   )

																												return custom_forward

																					# down
																					if is_torch_version('''>='''				,     '''1.11.0'''   ):
																												for down_block in self.down_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																					else:
																												for down_block in self.down_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_   )
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block   )				,     lowerCAmelCase_   )

														else:
																					# down
																					for down_block in self.down_blocks:
																												_a      	=							down_block(lowerCAmelCase_   )

																					# middle
																					_a      	=							self.mid_block(lowerCAmelCase_   )

														# post-process
														_a      	=							self.conv_norm_out(lowerCAmelCase_   )
														_a      	=							self.conv_act(lowerCAmelCase_   )
														_a      	=							self.conv_out(lowerCAmelCase_   )

														return sample



class      A       (						nn.Module       ):







							def __init__(    self			:    int				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Union[str, Any]=3				,     lowerCAmelCase_			:    Union[str, Any]=("UpDecoderBlock2D",)				,     lowerCAmelCase_			:    List[str]=(64,)				,     lowerCAmelCase_			:    Dict=2				,     lowerCAmelCase_			:    Optional[int]=32				,     lowerCAmelCase_			:    Union[str, Any]="silu"				,     lowerCAmelCase_			:    Dict="group"				,     )     ->				Dict:


														"""simple docstring"""


														super().__init__()
														_a      	=							layers_per_block

														_a      	=							nn.Convad(
														    lowerCAmelCase_				,     block_out_channels[-1]				,     kernel_size=3				,     stride=1				,     padding=1				,     )

														_a      	=							None
														_a      	=							nn.ModuleList([]   )

														_a      	=							in_channels if norm_type == '''spatial''' else None

														# mid
														_a      	=							UNetMidBlockaD(
														    in_channels=block_out_channels[-1]				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     output_scale_factor=1				,     resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type				,     attention_head_dim=block_out_channels[-1]				,     resnet_groups=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )

														# up
														_a      	=							list(reversed(lowerCAmelCase_   )   )
														_a      	=							reversed_block_out_channels[0]
														for i, up_block_type in enumerate(lowerCAmelCase_   ):
																					_a      	=							output_channel
																					_a      	=							reversed_block_out_channels[i]

																					_a      	=							i == len(lowerCAmelCase_   ) - 1

																					_a      	=							get_up_block(
																					    lowerCAmelCase_				,     num_layers=self.layers_per_block + 1				,     in_channels=lowerCAmelCase_				,     out_channels=lowerCAmelCase_				,     prev_output_channel=lowerCAmelCase_				,     add_upsample=not is_final_block				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     resnet_groups=lowerCAmelCase_				,     attention_head_dim=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     resnet_time_scale_shift=lowerCAmelCase_				,     )
																					self.up_blocks.append(lowerCAmelCase_   )
																					_a      	=							output_channel

														# out
														if norm_type == "spatial":
																					_a      	=							SpatialNorm(block_out_channels[0]				,     lowerCAmelCase_   )
														else:
																					_a      	=							nn.GroupNorm(num_channels=block_out_channels[0]				,     num_groups=lowerCAmelCase_				,     eps=1e-6   )
														_a      	=							nn.SiLU()
														_a      	=							nn.Convad(block_out_channels[0]				,     lowerCAmelCase_				,     3				,     padding=1   )

														_a      	=							False







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Union[str, Any]=None   )     ->				Any:


														"""simple docstring"""


														_a      	=							z
														_a      	=							self.conv_in(lowerCAmelCase_   )

														_a      	=							next(iter(self.up_blocks.parameters()   )   ).dtype
														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCAmelCase_			:    Dict   ):
																												def custom_forward(*lowerCAmelCase_			:    Tuple   ):
																																			return module(*lowerCAmelCase_   )

																												return custom_forward

																					if is_torch_version('''>='''				,     '''1.11.0'''   ):
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																												_a      	=							sample.to(lowerCAmelCase_   )

																												# up
																												for up_block in self.up_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																					else:
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     lowerCAmelCase_   )
																												_a      	=							sample.to(lowerCAmelCase_   )

																												# up
																												for up_block in self.up_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     lowerCAmelCase_   )
														else:
																					# middle
																					_a      	=							self.mid_block(lowerCAmelCase_				,     lowerCAmelCase_   )
																					_a      	=							sample.to(lowerCAmelCase_   )

																					# up
																					for up_block in self.up_blocks:
																												_a      	=							up_block(lowerCAmelCase_				,     lowerCAmelCase_   )

        # post-process
														if latent_embeds is None:
																					_a      	=							self.conv_norm_out(lowerCAmelCase_   )
														else:
																					_a      	=							self.conv_norm_out(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.conv_act(lowerCAmelCase_   )
														_a      	=							self.conv_out(lowerCAmelCase_   )

														return sample



class      A       (						nn.Module       ):







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    int=None				,     lowerCAmelCase_			:    Tuple="random"				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    Optional[Any]=True   )     ->				List[Any]:


														"""simple docstring"""


														super().__init__()
														_a      	=							n_e
														_a      	=							vq_embed_dim
														_a      	=							beta
														_a      	=							legacy

														_a      	=							nn.Embedding(self.n_e				,     self.vq_embed_dim   )
														self.embedding.weight.data.uniform_(-1.0 / self.n_e				,     1.0 / self.n_e   )

														_a      	=							remap
														if self.remap is not None:
																					self.register_buffer('''used'''				,     torch.tensor(np.load(self.remap   )   )   )
																					_a      	=							self.used.shape[0]
																					_a      	=							unknown_index  # "random" or "extra" or integer
																					if self.unknown_index == "extra":
																												_a      	=							self.re_embed
																												_a      	=							self.re_embed + 1
																					print(
																					    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
																					    F'Using {self.unknown_index} for unknown indices.'   )
														else:
																					_a      	=							n_e

														_a      	=							sane_index_shape







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							inds.shape
														assert len(lowerCAmelCase_   ) > 1
														_a      	=							inds.reshape(ishape[0]				,     -1   )
														_a      	=							self.used.to(lowerCAmelCase_   )
														_a      	=							(inds[:, :, None] == used[None, None, ...]).long()
														_a      	=							match.argmax(-1   )
														_a      	=							match.sum(2   ) < 1
														if self.unknown_index == "random":
																					_a      	=							torch.randint(0				,     self.re_embed				,     size=new[unknown].shape   ).to(device=new.device   )
														else:
																					_a      	=							self.unknown_index
														return new.reshape(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							inds.shape
														assert len(lowerCAmelCase_   ) > 1
														_a      	=							inds.reshape(ishape[0]				,     -1   )
														_a      	=							self.used.to(lowerCAmelCase_   )
														if self.re_embed > self.used.shape[0]:  # extra token
																					_a      	=							0  # simply set to zero
														_a      	=							torch.gather(used[None, :][inds.shape[0] * [0], :]				,     1				,     lowerCAmelCase_   )
														return back.reshape(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							z.permute(0				,     2				,     3				,     1   ).contiguous()
														_a      	=							z.view(-1				,     self.vq_embed_dim   )

														# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
														_a      	=							torch.argmin(torch.cdist(lowerCAmelCase_				,     self.embedding.weight   )				,     dim=1   )

														_a      	=							self.embedding(lowerCAmelCase_   ).view(z.shape   )
														_a      	=							None
														_a      	=							None

														# compute loss for embedding
														if not self.legacy:
																					_a      	=							self.beta * torch.mean((z_q.detach() - z) ** 2   ) + torch.mean((z_q - z.detach()) ** 2   )
														else:
																					_a      	=							torch.mean((z_q.detach() - z) ** 2   ) + self.beta * torch.mean((z_q - z.detach()) ** 2   )

														# preserve gradients
														_a      	=							z + (z_q - z).detach()

														# reshape back to match original input shape
														_a      	=							z_q.permute(0				,     3				,     1				,     2   ).contiguous()

														if self.remap is not None:
																					_a      	=							min_encoding_indices.reshape(z.shape[0]				,     -1   )  # add batch axis
																					_a      	=							self.remap_to_used(lowerCAmelCase_   )
																					_a      	=							min_encoding_indices.reshape(-1				,     1   )  # flatten

														if self.sane_index_shape:
																					_a      	=							min_encoding_indices.reshape(z_q.shape[0]				,     z_q.shape[2]				,     z_q.shape[3]   )

														return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														if self.remap is not None:
																					_a      	=							indices.reshape(shape[0]				,     -1   )  # add batch axis
																					_a      	=							self.unmap_to_all(lowerCAmelCase_   )
																					_a      	=							indices.reshape(-1   )  # flatten again

														# get quantized latent vectors
														_a      	=							self.embedding(lowerCAmelCase_   )

														if shape is not None:
																					_a      	=							z_q.view(lowerCAmelCase_   )
																					# reshape back to match original input shape
																					_a      	=							z_q.permute(0				,     3				,     1				,     2   ).contiguous()

														return z_q



class      A       (						_a       ):







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Tuple=False   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							parameters
														_a	,				_a      	=							torch.chunk(lowerCAmelCase_				,     2				,     dim=1   )
														_a      	=							torch.clamp(self.logvar				,     -3_0.0				,     2_0.0   )
														_a      	=							deterministic
														_a      	=							torch.exp(0.5 * self.logvar   )
														_a      	=							torch.exp(self.logvar   )
														if self.deterministic:
																					_a      	=							_a      	=							torch.zeros_like(
																					    self.mean				,     device=self.parameters.device				,     dtype=self.parameters.dtype   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Optional[torch.Generator] = None   )     ->				torch.FloatTensor:


														"""simple docstring"""


														_a      	=							randn_tensor(
														    self.mean.shape				,     generator=lowerCAmelCase_				,     device=self.parameters.device				,     dtype=self.parameters.dtype   )
														_a      	=							self.mean + self.std * sample
														return x







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[str]=None   )     ->				int:


														"""simple docstring"""


														if self.deterministic:
																					return torch.Tensor([0.0]   )
														else:
																					if other is None:
																												return 0.5 * torch.sum(torch.pow(self.mean				,     2   ) + self.var - 1.0 - self.logvar				,     dim=[1, 2, 3]   )
																					else:
																												return 0.5 * torch.sum(
																												    torch.pow(self.mean - other.mean				,     2   ) / other.var
																												    + self.var / other.var
																												    - 1.0
																												    - self.logvar
																												    + other.logvar				,     dim=[1, 2, 3]				,     )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    List[str]=[1, 2, 3]   )     ->				Any:


														"""simple docstring"""


														if self.deterministic:
																					return torch.Tensor([0.0]   )
														_a      	=							np.log(2.0 * np.pi   )
														return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,     2   ) / self.var				,     dim=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														return self.mean







'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_a      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_a      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_a      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							field(
							    default=128	,metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'Set this flag to use fast tokenization.'}       )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir )
							    and os.listdir(training_args.output_dir )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    ''' --overwrite_output_dir to overcome.''' )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,		datefmt='''%m/%d/%Y %H:%M:%S'''					,		level=logging.INFO					,		)
							logger.info(
							    f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
							    f'16-bits training: {training_args.fpaa}' )
							logger.info(f'Training/evaluation parameters {training_args}' )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_a      	=							AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,		cache_dir=model_args.cache_dir					,		)

							_a	,				_a	,				_a	,				_a      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

							_a      	=							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,		num_labels=len(UpperCamelCase )					,		labelaid=UpperCamelCase					,		idalabel={id: label for label, id in labelaid.items()}					,		finetuning_task='''text-classification'''					,		cache_dir=model_args.cache_dir					,		)

							with training_args.strategy.scope():
														_a      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir )

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''w''' ) as writer:
																					logger.info('''***** Eval results *****''' )

																					for key, value in result.items():
																												logger.info(f'  {key} = {value}' )
																												writer.write(f'{key} = {value}\n' )

																					results.update(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

