


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 						a__			(				A__,      A__,      A__,      A__,      A__,      A__ = None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					{}

			if train_file is not None:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[train_file]
			if eval_file is not None:
						SCREAMING_SNAKE_CASE_		:     str					=					[eval_file]
			if test_file is not None:
						SCREAMING_SNAKE_CASE_		:     str					=					[test_file]

			SCREAMING_SNAKE_CASE_		:     Tuple					=					datasets.load_dataset('csv',      data_files=A__				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					list(ds[list(files.keys()				)[0]].features.keys()				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					features_name.pop(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					list(set(ds[list(files.keys()				)[0]][label_name]				)				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{label: i for i, label in enumerate(A__				)}
			SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.model_input_names
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{}

			if len(A__				) == 1:
						for k in files.keys():
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ds[k].map(
									    lambda A__				: tokenizer.batch_encode_plus(
									        example[features_name[0]],      truncation=A__,      max_length=A__,      padding='max_length'				),      batched=A__,      )
			elif len(A__				) == 2:
						for k in files.keys():
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					ds[k].map(
									    lambda A__				: tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]]),      truncation=A__,      max_length=A__,      padding='max_length',      ),      batched=A__,      )

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

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

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

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({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:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

			SCREAMING_SNAKE_CASE_		:     int					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({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:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({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:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

			return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__			:      Union[str, Any]								=logging.getLogger(__name__)







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						field(metadata={"""help""": """Which column contains the label"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the training file"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the development file"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the test file"""}  )
							_UpperCAmelCase      =						field(
							    default=1_2_8	,       metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }	,       )
							_UpperCAmelCase      =						field(
							    default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







@dataclass
class 	__lowercase    :





							"""simple docstring"""







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







def 						a__			(				):
			# 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.
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Any					=					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.

			SCREAMING_SNAKE_CASE_		:     Dict					=					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,      )

			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					get_tfds(
			    train_file=data_args.train_file,      eval_file=data_args.dev_file,      test_file=data_args.test_file,      tokenizer=A__,      label_column_id=data_args.label_column_id,      max_seq_length=data_args.max_seq_length,      )

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

			with training_args.strategy.scope():
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path,      from_pt=bool('.bin' in model_args.model_name_or_path				),      config=A__,      cache_dir=model_args.cache_dir,      )

			def compute_metrics(A__				) -> Dict:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					np.argmax(p.predictions,      axis=1				)

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

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE_		:     int					=					TFTrainer(
			    model=A__,      args=A__,      train_dataset=A__,      eval_dataset=A__,      compute_metrics=A__,      )

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

			# Evaluation
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'				)
						SCREAMING_SNAKE_CASE_		:     Dict					=					trainer.evaluate()
						SCREAMING_SNAKE_CASE_		:     int					=					os.path.join(training_args.output_dir,      'eval_results.txt'				)

						with open(A__,      '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(A__				)

			return results


if __name__ == "__main__":
				main()













"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard


@slow
@require_flax
class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

