
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = None ,							):
	lowercase       =    {}

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

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

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

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

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

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

	lowercase       =    (
	    tf.data.Dataset.from_generator(
	        __SCREAMING_SNAKE_CASE ,							({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:
		lowercase       =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

	lowercase       =    (
	    tf.data.Dataset.from_generator(
	        __SCREAMING_SNAKE_CASE ,							({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:
		lowercase       =    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

	lowercase       =    (
	    tf.data.Dataset.from_generator(
	        __SCREAMING_SNAKE_CASE ,							({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:
		lowercase       =    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

	return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase	      = logging.getLogger(__name__)







@dataclass
class        A_   :




	'''simple docstring'''


	_UpperCamelCase     :     int							 =       field(metadata={"""help""": """Which column contains the label"""}   )
	_UpperCamelCase     :     str							 =       field(default=__lowerCamelCase      ,	metadata={"""help""": """The path of the training file"""}   )
	_UpperCamelCase     :     Optional[str]							 =       field(default=__lowerCamelCase      ,	metadata={"""help""": """The path of the development file"""}   )
	_UpperCamelCase     :     Optional[str]							 =       field(default=__lowerCamelCase      ,	metadata={"""help""": """The path of the test file"""}   )
	_UpperCamelCase     :     int							 =       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."""
	        )
	    }      ,	)
	_UpperCamelCase     :     bool							 =       field(
	    default=__lowerCamelCase      ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )







@dataclass
class        A_   :




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
	_UpperCamelCase     :     Optional[str]							 =       field(
	    default=__lowerCamelCase      ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
	_UpperCamelCase     :     Optional[str]							 =       field(
	    default=__lowerCamelCase      ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
	_UpperCamelCase     :     bool							 =       field(default=__lowerCamelCase      ,	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.
	_UpperCamelCase     :     Optional[str]							 =       field(
	    default=__lowerCamelCase      ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,	)






def 							UpperCAmelCase_		(							):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.
	lowercase       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
	lowercase  ,					lowercase  ,					lowercase       =    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.

	lowercase       =    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 ,							)

	lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    get_tfds(
	    train_file=data_args.train_file ,							eval_file=data_args.dev_file ,							test_file=data_args.test_file ,							tokenizer=__SCREAMING_SNAKE_CASE ,							label_column_id=data_args.label_column_id ,							max_seq_length=data_args.max_seq_length ,							)

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

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

	def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
		lowercase       =    np.argmax(p.predictions ,							axis=1							)

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

	# Initialize our Trainer
	lowercase       =    TFTrainer(
	    model=__SCREAMING_SNAKE_CASE ,							args=__SCREAMING_SNAKE_CASE ,							train_dataset=__SCREAMING_SNAKE_CASE ,							eval_dataset=__SCREAMING_SNAKE_CASE ,							compute_metrics=__SCREAMING_SNAKE_CASE ,							)

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

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

		with open(__SCREAMING_SNAKE_CASE ,							'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(__SCREAMING_SNAKE_CASE							)

	return results


if __name__ == "__main__":
	main()



import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

