
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
					import torch

if is_tf_available():
					import tensorflow as tf







class  UpperCamelCase__		(    _snake_case   ):
	_lowerCAmelCase							       =				["image_processor"]
	_lowerCAmelCase							       =				"SamImageProcessor"





	def __init__(			self				,       UpperCamelCase__		):
					super().__init__(lowerCAmelCase__		)
					A__  :	Optional[int]         =							self.image_processor
					A__  :	Any         =							-10
					A__  :	Dict         =							self.image_processor.size['''longest_edge''']





	def __call__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					A__  :	Any         =							self.image_processor(
					    lowerCAmelCase__				,       return_tensors=lowerCAmelCase__				,       **lowerCAmelCase__				,       )

					# pop arguments that are not used in the foward but used nevertheless
					A__  :	Optional[Any]         =							encoding_image_processor['''original_sizes''']

					if hasattr(lowerCAmelCase__				,       '''numpy'''		):  # Checks if Torch or TF tensor
									A__  :	Optional[int]         =							original_sizes.numpy()

					A__       ,							A__       ,							A__  :	Optional[Any]         =							self._check_and_preprocess_points(
					    input_points=lowerCAmelCase__				,       input_labels=lowerCAmelCase__				,       input_boxes=lowerCAmelCase__				,       )

					A__  :	Optional[int]         =							self._normalize_and_convert(
					    lowerCAmelCase__				,       lowerCAmelCase__				,       input_points=lowerCAmelCase__				,       input_labels=lowerCAmelCase__				,       input_boxes=lowerCAmelCase__				,       return_tensors=lowerCAmelCase__				,       )

					return encoding_image_processor





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="pt"				,       ):
					if input_points is not None:
									if len(lowerCAmelCase__		) != len(lowerCAmelCase__		):
													A__  :	Union[str, Any]         =							[
													    self._normalize_coordinates(self.target_size				,       lowerCAmelCase__				,       original_sizes[0]		) for point in input_points
													]
									else:
													A__  :	Optional[Any]         =							[
													    self._normalize_coordinates(self.target_size				,       lowerCAmelCase__				,       lowerCAmelCase__		)
													    for point, original_size in zip(lowerCAmelCase__				,       lowerCAmelCase__		)
													]
									# check that all arrays have the same shape
									if not all(point.shape == input_points[0].shape for point in input_points		):
													if input_labels is not None:
																	A__       ,							A__  :	Tuple         =							self._pad_points_and_labels(lowerCAmelCase__				,       lowerCAmelCase__		)

									A__  :	str         =							np.array(lowerCAmelCase__		)

					if input_labels is not None:
									A__  :	Optional[int]         =							np.array(lowerCAmelCase__		)

					if input_boxes is not None:
									if len(lowerCAmelCase__		) != len(lowerCAmelCase__		):
													A__  :	Optional[int]         =							[
													    self._normalize_coordinates(self.target_size				,       lowerCAmelCase__				,       original_sizes[0]				,       is_bounding_box=lowerCAmelCase__		)
													    for box in input_boxes
													]
									else:
													A__  :	Optional[int]         =							[
													    self._normalize_coordinates(self.target_size				,       lowerCAmelCase__				,       lowerCAmelCase__				,       is_bounding_box=lowerCAmelCase__		)
													    for box, original_size in zip(lowerCAmelCase__				,       lowerCAmelCase__		)
													]
									A__  :	List[str]         =							np.array(lowerCAmelCase__		)

					if input_boxes is not None:
									if return_tensors == "pt":
													A__  :	int         =							torch.from_numpy(lowerCAmelCase__		)
													# boxes batch size of 1 by default
													A__  :	Any         =							input_boxes.unsqueeze(1		) if len(input_boxes.shape		) != 3 else input_boxes
									elif return_tensors == "tf":
													A__  :	Optional[Any]         =							tf.convert_to_tensor(lowerCAmelCase__		)
													# boxes batch size of 1 by default
													A__  :	str         =							tf.expand_dims(lowerCAmelCase__				,       1		) if len(input_boxes.shape		) != 3 else input_boxes
									encoding_image_processor.update({'''input_boxes''': input_boxes}		)
					if input_points is not None:
									if return_tensors == "pt":
													A__  :	Optional[Any]         =							torch.from_numpy(lowerCAmelCase__		)
													# point batch size of 1 by default
													A__  :	Union[str, Any]         =							input_points.unsqueeze(1		) if len(input_points.shape		) != 4 else input_points
									elif return_tensors == "tf":
													A__  :	Union[str, Any]         =							tf.convert_to_tensor(lowerCAmelCase__		)
													# point batch size of 1 by default
													A__  :	Any         =							tf.expand_dims(lowerCAmelCase__				,       1		) if len(input_points.shape		) != 4 else input_points
									encoding_image_processor.update({'''input_points''': input_points}		)
					if input_labels is not None:
									if return_tensors == "pt":
													A__  :	Dict         =							torch.from_numpy(lowerCAmelCase__		)
													# point batch size of 1 by default
													A__  :	Dict         =							input_labels.unsqueeze(1		) if len(input_labels.shape		) != 3 else input_labels
									elif return_tensors == "tf":
													A__  :	int         =							tf.convert_to_tensor(lowerCAmelCase__		)
													# point batch size of 1 by default
													A__  :	Tuple         =							tf.expand_dims(lowerCAmelCase__				,       1		) if len(input_labels.shape		) != 3 else input_labels
									encoding_image_processor.update({'''input_labels''': input_labels}		)

					return encoding_image_processor





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							max([point.shape[0] for point in input_points]		)
					A__  :	Optional[Any]         =							[]
					for i, point in enumerate(lowerCAmelCase__		):
									if point.shape[0] != expected_nb_points:
													A__  :	Any         =							np.concatenate(
													    [point, np.zeros((expected_nb_points - point.shape[0], 2)		) + self.point_pad_value]				,       axis=0		)
													A__  :	Dict         =							np.append(input_labels[i]				,       [self.point_pad_value]		)
									processed_input_points.append(lowerCAmelCase__		)
					A__  :	Union[str, Any]         =							processed_input_points
					return input_points, input_labels





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__       ,							A__  :	int         =							original_size
					A__       ,							A__  :	Any         =							self.image_processor._get_preprocess_shape(lowerCAmelCase__				,       longest_edge=lowerCAmelCase__		)
					A__  :	int         =							deepcopy(lowerCAmelCase__		).astype(lowerCAmelCase__		)

					if is_bounding_box:
									A__  :	Optional[Any]         =							coords.reshape(-1				,       2				,       2		)

					A__  :	List[Any]         =							coords[..., 0] * (new_w / old_w)
					A__  :	Any         =							coords[..., 1] * (new_h / old_h)

					if is_bounding_box:
									A__  :	Any         =							coords.reshape(-1				,       4		)

					return coords





	def   __snake_case				(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       ):
					if input_points is not None:
									if hasattr(lowerCAmelCase__				,       '''numpy'''		):  # Checks for TF or Torch tensor
													A__  :	List[str]         =							input_points.numpy().tolist()

									if not isinstance(lowerCAmelCase__				,       lowerCAmelCase__		) or not isinstance(input_points[0]				,       lowerCAmelCase__		):
													raise ValueError('''Input points must be a list of list of floating points.'''		)
									A__  :	Union[str, Any]         =							[np.array(lowerCAmelCase__		) for input_point in input_points]
					else:
									A__  :	Dict         =							None

					if input_labels is not None:
									if hasattr(lowerCAmelCase__				,       '''numpy'''		):
													A__  :	str         =							input_labels.numpy().tolist()

									if not isinstance(lowerCAmelCase__				,       lowerCAmelCase__		) or not isinstance(input_labels[0]				,       lowerCAmelCase__		):
													raise ValueError('''Input labels must be a list of list integers.'''		)
									A__  :	List[Any]         =							[np.array(lowerCAmelCase__		) for label in input_labels]
					else:
									A__  :	Optional[Any]         =							None

					if input_boxes is not None:
									if hasattr(lowerCAmelCase__				,       '''numpy'''		):
													A__  :	Tuple         =							input_boxes.numpy().tolist()

									if (
									    not isinstance(lowerCAmelCase__				,       lowerCAmelCase__		)
									    or not isinstance(input_boxes[0]				,       lowerCAmelCase__		)
									    or not isinstance(input_boxes[0][0]				,       lowerCAmelCase__		)
									):
													raise ValueError('''Input boxes must be a list of list of list of floating points.'''		)
									A__  :	Dict         =							[np.array(lowerCAmelCase__		).astype(np.floataa		) for box in input_boxes]
					else:
									A__  :	List[Any]         =							None

					return input_points, input_labels, input_boxes





	@property
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.image_processor.model_input_names
					return list(dict.fromkeys(lowerCAmelCase__		)		)





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.image_processor.post_process_masks(*lowerCAmelCase__				,       **lowerCAmelCase__		)

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





	def __iter__(			self		):
					A__  :	Dict         =							iter(self.dataset		)
					A__  :	Tuple         =							True
					while more_examples:
									A__       ,							A__  :	Optional[Any]         =							[], 0
									while True:
													if buffer_len >= self.input_characters:
																	break
													try:
																	buffer.append(next(UpperCamelCase__		)['''content''']		)
																	buffer_len += len(buffer[-1]		)
													except StopIteration:
																	A__  :	Dict         =							False
																	break
									A__  :	str         =							tokenizer(UpperCamelCase__				,       truncation=UpperCamelCase__		)['''input_ids''']
									A__  :	Optional[int]         =							[]
									for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
									for i in range(0				,       len(UpperCamelCase__		)				,       self.seq_length		):
													A__  :	Optional[int]         =							all_token_ids[i : i + self.seq_length]
													if len(UpperCamelCase__		) == self.seq_length:
																	yield torch.tensor(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")