
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
	from .data.data_collator import DataCollator
	from .modeling_utils import PreTrainedModel
	from .tokenization_utils_base import PreTrainedTokenizerBase
	from .trainer_callback import TrainerCallback
	from .trainer_utils import EvalPrediction, PredictionOutput
	from .training_args import TrainingArguments


UpperCAmelCase     				=			logging.get_logger(__name__)







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = (None, None)		,   snake_case = None		,   ):
		super().__init__(
		    model=snake_case		,   args=snake_case		,   data_collator=snake_case		,   train_dataset=snake_case		,   eval_dataset=snake_case		,   tokenizer=snake_case		,   model_init=snake_case		,   compute_metrics=snake_case		,   callbacks=snake_case		,   optimizers=snake_case		,   preprocess_logits_for_metrics=snake_case		,   )

		# Override self.model.generation_config if a GenerationConfig is specified in args.
		# Priority: args.generation_config > model.generation_config > default GenerationConfig.
		if self.args.generation_config is not None:
			lowercase					  =   self.load_generation_config(self.args.generation_config	)
			lowercase					  =   gen_config





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):

		# GenerationConfig provided, nothing to do
		if isinstance(snake_case		,   snake_case	):
			return deepcopy(snake_case	)

		# str or Path
		lowercase					  =   Path(snake_case	) if isinstance(snake_case		,   snake_case	) else gen_config_arg
		lowercase					  =   None

		# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
		# This step is required in order to determine config_file_name
		if pretrained_model_name.is_file():
			lowercase					  =   pretrained_model_name.name
			lowercase					  =   pretrained_model_name.parent
		# dir path
		elif pretrained_model_name.is_dir():
			pass
		# model id or URL
		else:
			lowercase					  =   gen_config_arg

		lowercase					  =   GenerationConfig.from_pretrained(snake_case		,   snake_case	)
		return gen_config





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case = None		,   snake_case = None		,   snake_case = "eval"		,   **snake_case		,   ):
		lowercase					  =   gen_kwargs.copy()
		if gen_kwargs.get('max_length'	) is None and gen_kwargs.get('max_new_tokens'	) is None:
			lowercase					  =   self.args.generation_max_length
		lowercase					  =   (
		    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'	) is not None else self.args.generation_num_beams
		)
		lowercase					  =   gen_kwargs

		return super().evaluate(snake_case		,   ignore_keys=snake_case		,   metric_key_prefix=snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = "test"		,   **snake_case		,   ):
		lowercase					  =   gen_kwargs.copy()
		if gen_kwargs.get('max_length'	) is None and gen_kwargs.get('max_new_tokens'	) is None:
			lowercase					  =   self.args.generation_max_length
		lowercase					  =   (
		    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'	) is not None else self.args.generation_num_beams
		)
		lowercase					  =   gen_kwargs

		return super().predict(snake_case		,   ignore_keys=snake_case		,   metric_key_prefix=snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   **snake_case		,   ):

		if not self.args.predict_with_generate or prediction_loss_only:
			return super().prediction_step(
			    snake_case		,   snake_case		,   prediction_loss_only=snake_case		,   ignore_keys=snake_case	)

		lowercase					  =   'labels' in inputs
		lowercase					  =   self._prepare_inputs(snake_case	)

		# XXX: adapt synced_gpus for fairscale as well
		# Priority (handled in generate):
		# gen_kwargs > model.generation_config > default GenerationConfig()

		if len(snake_case	) == 0 and hasattr(self		,   '_gen_kwargs'	):
			lowercase					  =   self._gen_kwargs.copy()

		if gen_kwargs.get('max_length'	) is None and gen_kwargs.get('max_new_tokens'	) is None:
			lowercase					  =   self.model.config.max_length
		lowercase					  =   (
		    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'	) is not None else self.model.config.num_beams
		)
		lowercase					  =   True if is_deepspeed_zeroa_enabled() else False
		lowercase					  =   (
		    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'	) is not None else default_synced_gpus
		)

		# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
		# (otherwise, it would continue generating from the padded `decoder_input_ids`)
		if (
		    "labels" in inputs
		    and "decoder_input_ids" in inputs
		    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
		):
			lowercase					  =   {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
		lowercase					  =   self.model.generate(**snake_case		,   **snake_case	)

		# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
		# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
		# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
		if self.model.generation_config._from_model_config:
			lowercase					  =   False

		# Retrieves GenerationConfig from model.generation_config
		lowercase					  =   self.model.generation_config
		# in case the batch is shorter than max length, the output should be padded
		if generated_tokens.shape[-1] < gen_config.max_length:
			lowercase					  =   self._pad_tensors_to_max_len(snake_case		,   gen_config.max_length	)
		elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
			lowercase					  =   self._pad_tensors_to_max_len(snake_case		,   gen_config.max_new_tokens + 1	)

		with torch.no_grad():
			if has_labels:
				with self.compute_loss_context_manager():
					lowercase					  =   model(**snake_case	)
				if self.label_smoother is not None:
					lowercase					  =   self.label_smoother(snake_case		,   inputs['labels']	).mean().detach()
				else:
					lowercase					  =   (outputs['loss'] if isinstance(snake_case		,   snake_case	) else outputs[0]).mean().detach()
			else:
				lowercase					  =   None

		if self.args.prediction_loss_only:
			return loss, None, None

		if has_labels:
			lowercase					  =   inputs['labels']
			if labels.shape[-1] < gen_config.max_length:
				lowercase					  =   self._pad_tensors_to_max_len(snake_case		,   gen_config.max_length	)
			elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
				lowercase					  =   self._pad_tensors_to_max_len(snake_case		,   gen_config.max_new_tokens + 1	)
		else:
			lowercase					  =   None

		return loss, generated_tokens, labels





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if self.tokenizer is not None and hasattr(self.tokenizer		,   'pad_token_id'	):
			# If PAD token is not defined at least EOS token has to be defined
			lowercase					  =   (
			    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
			)
		else:
			if self.model.config.pad_token_id is not None:
				lowercase					  =   self.model.config.pad_token_id
			else:
				raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'	)

		lowercase					  =   pad_token_id * torch.ones(
		    (tensor.shape[0], max_length)		,   dtype=tensor.dtype		,   device=tensor.device	)
		lowercase					  =   tensor
		return padded_tensor



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=0							):
	lowercase					  =   np.arange(start=3			,							stop=__SCREAMING_SNAKE_CASE			,							step=4							).tolist() if k > 0 else [10]
	lowercase					  =   _quantize(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowercase					  =   np.unique(__SCREAMING_SNAKE_CASE			,							return_counts=__SCREAMING_SNAKE_CASE							)[1]
	lowercase					  =   [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(__SCREAMING_SNAKE_CASE							)							)
	logger.info('Count of instances per bin: {}'.format(__SCREAMING_SNAKE_CASE							)							)
	return groups







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(snake_case	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			if len(buffer_per_group[group_id]	) == self.batch_size:
				yield buffer_per_group[group_id]  # TODO
				num_batches += 1
				del buffer_per_group[group_id]
			assert len(buffer_per_group[group_id]	) < self.batch_size

		# now we have run out of elements that satisfy
		# the group criteria, let's return the remaining
		# elements so that the size of the sampler is
		# deterministic
		lowercase					  =   len(self	)
		lowercase					  =   expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			lowercase					  =   []
			for group_id, idxs in sorted(buffer_per_group.items()		,   key=lambda snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 0:
				yield batch_idx
				num_remaining -= 1
		assert num_remaining == 0





	def __len__(	self	):
		return (len(self.sampler	) + self.batch_size - 1) // self.batch_size

