




"""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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = None	,				):
			__lowercase			:  List[str]								=       {}

			if train_file is not None:
						__lowercase			:  str								=       [train_file]
			if eval_file is not None:
						__lowercase			:  List[str]								=       [eval_file]
			if test_file is not None:
						__lowercase			:  Union[str, Any]								=       [test_file]

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

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

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

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

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

			__lowercase			:  Tuple								=       (
			    tf.data.Dataset.from_generator(
			        _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			:  Union[str, Any]								=       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

			__lowercase			:  Dict								=       (
			    tf.data.Dataset.from_generator(
			        _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			:  Union[str, Any]								=       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

			__lowercase			:  str								=       (
			    tf.data.Dataset.from_generator(
			        _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			:  Union[str, Any]								=       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

			return train_ds, val_ds, test_ds, labelaid


a_        =       logging.getLogger(__name__)

@dataclass
class   UpperCAmelCase_    :
		UpperCamelCase							=field(metadata={"help": "Which column contains the label"}		)
		UpperCamelCase							=field(default=_A   ,					metadata={"help": "The path of the training file"}		)
		UpperCamelCase							=field(default=_A   ,					metadata={"help": "The path of the development file"}		)
		UpperCamelCase							=field(default=_A   ,					metadata={"help": "The path of the test file"}		)
		UpperCamelCase							=field(
		    default=1_28   ,					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=_A   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}		)


@dataclass
class   UpperCAmelCase_    :
		UpperCamelCase							=field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
		UpperCamelCase							=field(
		    default=_A   ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
		UpperCamelCase							=field(
		    default=_A   ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
		UpperCamelCase							=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.
		UpperCamelCase							=field(
		    default=_A   ,					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			:  str								=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
			__lowercase			,__lowercase			,__lowercase			:  int								=       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			:  Tuple								=       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			:  Optional[Any]								=       get_tfds(
			    train_file=data_args.train_file	,				eval_file=data_args.dev_file	,				test_file=data_args.test_file	,				tokenizer=_snake_case	,				label_column_id=data_args.label_column_id	,				max_seq_length=data_args.max_seq_length	,				)

			__lowercase			:  int								=       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=len(_snake_case	)	,				labelaid=_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			:  Any								=       TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path	,				from_pt=bool('''.bin''' in model_args.model_name_or_path	)	,				config=_snake_case	,				cache_dir=model_args.cache_dir	,				)

			def compute_metrics(__UpperCamelCase	) -> Dict:
						__lowercase			:  Union[str, Any]								=       np.argmax(p.predictions	,				axis=1	)

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

			# Initialize our Trainer
			__lowercase			:  str								=       TFTrainer(
			    model=_snake_case	,				args=_snake_case	,				train_dataset=_snake_case	,				eval_dataset=_snake_case	,				compute_metrics=_snake_case	,				)

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

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

						with open(_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(_snake_case	)

			return results


if __name__ == "__main__":
			main()








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 3				)  ->     qiskit.result.counts.Counts:
   if isinstance(_snake_case	,   _snake_case				):
      raise TypeError('''number of qubits must be a integer.'''				)
   if number_of_qubits <= 0:
      raise ValueError('''number of qubits must be > 0.'''				)
   if math.floor(_snake_case				) != number_of_qubits:
      raise ValueError('''number of qubits must be exact integer.'''				)
   if number_of_qubits > 10:
      raise ValueError('''number of qubits too large to simulate(>10).'''				)

   _A      =       QuantumRegister(_snake_case	,   '''qr'''				)
   _A      =       ClassicalRegister(_snake_case	,   '''cr'''				)

   _A      =       QuantumCircuit(_snake_case	,   _snake_case				)

   _A      =       number_of_qubits

   for i in range(_snake_case				):
      quantum_circuit.h(number_of_qubits - i - 1				)
      counter -= 1
      for j in range(_snake_case				):
         quantum_circuit.cp(np.pi / 2 ** (counter - j)	,   _snake_case	,   _snake_case				)

   for k in range(number_of_qubits // 2				):
      quantum_circuit.swap(_snake_case	,   number_of_qubits - k - 1				)

   # measure all the qubits
   quantum_circuit.measure(_snake_case	,   _snake_case				)
   # simulate with 10000 shots
   _A      =       Aer.get_backend('''qasm_simulator'''				)
   _A      =       execute(_snake_case	,   _snake_case	,   shots=10_000				)

   return job.result().get_counts(_snake_case				)


if __name__ == "__main__":
   print(
       f'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
   )



