from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case						):
					if isinstance(__snake_case  ,     np.ndarray						):
										return list(tensor.shape						)

					_UpperCamelCase			     =     tf.shape(__snake_case						)

					if tensor.shape == tf.TensorShape(__snake_case						):
										return dynamic

					_UpperCamelCase			     =     tensor.shape.as_list()

					return [dynamic[i] if s is None else s for i, s in enumerate(__snake_case						)]
def 					_snake_case	(  __snake_case  ,     __snake_case = None  ,     __snake_case = None						):
					return tf.nn.softmax(logits=logits + 1E-9  ,     axis=__snake_case  ,     name=__snake_case						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=1E-5  ,     __snake_case=-1						):
					# This is a very simplified functional layernorm, designed to duplicate
					# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
					# models in Transformers.

					if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__snake_case  ,     __snake_case						):
										raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''						)

					# Get mean and variance on the axis to be normalized
					_UpperCamelCase     ,    _UpperCamelCase			     =     tf.nn.moments(__snake_case  ,     axes=[axis]  ,     keepdims=__snake_case						)

					if axis != -1:
										# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
										# on every dimension except axis
										_UpperCamelCase			     =     [1] * inputs.shape.rank
										_UpperCamelCase			     =     shape_list(__snake_case						)[axis]
										_UpperCamelCase			     =     tf.reshape(__snake_case  ,     __snake_case						)
										_UpperCamelCase			     =     tf.reshape(__snake_case  ,     __snake_case						)

					# Compute layer normalization using the batch_normalization
					# function.
					_UpperCamelCase			     =     tf.nn.batch_normalization(
					    __snake_case  ,     __snake_case  ,     __snake_case  ,     offset=__snake_case  ,     scale=__snake_case  ,     variance_epsilon=__snake_case  ,     )
					return outputs
def 					_snake_case	(  __snake_case  ,     __snake_case=0  ,     __snake_case=-1						):
					# Replicates the behavior of torch.flatten in TF

					# If end_dim or start_dim is negative, count them from the end
					if end_dim < 0:
										end_dim += input.shape.rank
					if start_dim < 0:
										start_dim += input.shape.rank

					if start_dim == end_dim:
										return input

					_UpperCamelCase			     =     tf.shape(__snake_case						)
					_UpperCamelCase			     =     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
					_UpperCamelCase			     =     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,     axis=0						)
					return tf.reshape(__snake_case  ,     __snake_case						)
def 					_snake_case	(  __snake_case						):
					if not isinstance(__snake_case  ,     tf.Tensor						):
										_UpperCamelCase			     =     tf.convert_to_tensor(__snake_case						)  # Catches stray NumPy inputs
					if encoder_attention_mask.shape.rank == 3:
										_UpperCamelCase			     =     encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.shape.rank == 2:
										_UpperCamelCase			     =     encoder_attention_mask[:, None, None, :]
					# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
					# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
					# /transformer/transformer_layers.py#L270
					# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
					# encoder_extended_attention_mask.transpose(-1, -2))
					_UpperCamelCase			     =     (
					    tf.cast(1  ,     encoder_attention_mask.dtype						) - encoder_extended_attention_mask
					) * encoder_extended_attention_mask.dtype.min

					return encoder_extended_attention_mask
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case = "input_ids"						):
					tf.debugging.assert_less(
					    __snake_case  ,     tf.cast(__snake_case  ,     dtype=tensor.dtype						)  ,     message=(
					        f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__snake_case						)}) must be smaller than the embedding """
					        f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
					    )  ,     )
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     64512
					# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
					# because in that case even chunking the array would not make the saving
					# possible.
					_UpperCamelCase			     =     [x for x in data if len(__snake_case						) > HDF5_OBJECT_HEADER_LIMIT]

					# Expecting this to never be true.
					if bad_attributes:
										raise RuntimeError(
										    '''The following attributes cannot be saved to HDF5 file because '''
										    f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
										    f"""bytes: {bad_attributes}"""						)

					_UpperCamelCase			     =     np.asarray(__snake_case						)

					_UpperCamelCase			     =     1
					_UpperCamelCase			     =     np.array_split(__snake_case  ,     __snake_case						)

					# This will never loop forever thanks to the test above.
					while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data						):
										num_chunks += 1
										_UpperCamelCase			     =     np.array_split(__snake_case  ,     __snake_case						)

					if num_chunks > 1:
										for chunk_id, chunk_data in enumerate(__snake_case						):
															_UpperCamelCase			     =     chunk_data
					else:
										_UpperCamelCase			     =     data
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					if name in group.attrs:
										_UpperCamelCase			     =     [n.decode('''utf8'''						) if hasattr(__snake_case  ,     '''decode'''						) else n for n in group.attrs[name]]
					else:
										_UpperCamelCase			     =     []
										_UpperCamelCase			     =     0
										while "%s%d" % (name, chunk_id) in group.attrs:
															data.extend(
															    [n.decode('''utf8'''						) if hasattr(__snake_case  ,     '''decode'''						) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]						)
															chunk_id += 1
					return data







def 					_snake_case	(  __snake_case						):

					def _expand_single_ad_tensor(__snake_case						):
										if isinstance(__snake_case  ,     tf.Tensor						) and t.shape.rank == 1:
															return tf.expand_dims(__snake_case  ,     axis=-1						)
										return t

					return tf.nest.map_structure(_expand_single_ad_tensor  ,     __snake_case						)

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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase       =		logging.getLogger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return (preds == labels).mean()
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained config name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
							UpperCAmelCase				   =     field(metadata={"help": "Should contain the data files for the task."} )
							UpperCAmelCase				   =     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				   =     field(
							    default=__lowercase,  metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 					_snake_case	(  ):
					# 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.

					_UpperCamelCase			     =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
					_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     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 if training_args.local_rank in [-1, 0] else logging.WARN  ,     )
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,     training_args.local_rank  ,     training_args.device  ,     training_args.n_gpu  ,     bool(training_args.local_rank != -1						)  ,     training_args.fpaa  ,     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank						):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s'''  ,     __snake_case						)

					# Set seed
					set_seed(training_args.seed						)

					try:
										_UpperCamelCase			     =     processors[data_args.task_name]()
										_UpperCamelCase			     =     processor.get_labels()
										_UpperCamelCase			     =     len(__snake_case						)
					except KeyError:
										raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

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

					_UpperCamelCase			     =     AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,     num_labels=__snake_case  ,     finetuning_task=data_args.task_name  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     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  ,     )
					_UpperCamelCase			     =     AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path  ,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)  ,     config=__snake_case  ,     cache_dir=model_args.cache_dir  ,     )

					# Get datasets
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.train  ,     )
					    if training_args.do_train
					    else None
					)
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.dev  ,     )
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(__snake_case						) -> Dict:
										_UpperCamelCase			     =     np.argmax(p.predictions  ,     axis=1						)
										return {"acc": simple_accuracy(__snake_case  ,     p.label_ids						)}

					# Data collator
					_UpperCamelCase			     =     DataCollatorWithPadding(__snake_case  ,     pad_to_multiple_of=8						) if training_args.fpaa else None

					# Initialize our Trainer
					_UpperCamelCase			     =     Trainer(
					    model=__snake_case  ,     args=__snake_case  ,     train_dataset=__snake_case  ,     eval_dataset=__snake_case  ,     compute_metrics=__snake_case  ,     data_collator=__snake_case  ,     )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
					_UpperCamelCase			     =     {}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''						)

										_UpperCamelCase			     =     trainer.evaluate()

										_UpperCamelCase			     =     os.path.join(training_args.output_dir  ,     '''eval_results.txt'''						)
										if trainer.is_world_master():
															with open(__snake_case  ,     '''w'''						) as writer:
																				logger.info('''***** Eval results *****'''						)
																				for key, value in result.items():
																									logger.info('''  %s = %s'''  ,     __snake_case  ,     __snake_case						)
																									writer.write('''%s = %s\n''' % (key, value)						)

																				results.update(__snake_case						)

					return results
def 					_snake_case	(  __snake_case						):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()
