



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__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      List[Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[Any]=10_24      ,					lowerCAmelCase	:      Any=10_24      ,					lowerCAmelCase	:      int=3.6)						->					Any:




         """simple docstring"""

         lowercase__  =	tokenizer
         lowercase__  =	tokenizer.bos_token_id
         lowercase__  =	dataset
         lowercase__  =	seq_length
         lowercase__  =	seq_length * chars_per_token * num_of_sequences



   def __iter__(    self	:      Dict)						->					List[str]:




         """simple docstring"""

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

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	{'streaming': True}
      lowercase__  =	load_dataset(args.dataset_name    ,	split='train'    ,	**A__						)
      lowercase__  =	ConstantLengthDataset(A__    ,	A__    ,	seq_length=args.seq_length						)
      lowercase__  =	DataLoader(A__    ,	batch_size=args.batch_size						)
      return eval_dataloader

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

            if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                  break
      lowercase__  =	torch.mean(torch.cat(A__						)						)
      try:
            lowercase__  =	torch.exp(A__						)
      except OverflowError:
            lowercase__  =	float('inf'						)
      return loss.item(), perplexity.item()


# Setup Accelerator
a__   :					Dict 						=      Accelerator()

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

# Logging
a__   :					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
a__   :					Tuple 						=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__   :					int 						=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a__   :					Optional[int] 						=      create_dataloader(args)

# Prepare everything with our `accelerator`.
a__						,		a__   :					Any 						=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
a__						,		a__   :					List[str] 						=      evaluate(args)

logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__   :					int 						=      logging.get_logger(__name__)

a__   :					int 						=      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     "marian"
   A     :	Dict												=     ["past_key_values"]
   A     :	List[Any]												=     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self	:      Any      ,					lowerCAmelCase	:      List[Any]=5_81_01      ,					lowerCAmelCase	:      Optional[int]=None      ,					lowerCAmelCase	:      List[str]=10_24      ,					lowerCAmelCase	:      int=12      ,					lowerCAmelCase	:      Union[str, Any]=40_96      ,					lowerCAmelCase	:      int=16      ,					lowerCAmelCase	:      Dict=12      ,					lowerCAmelCase	:      Optional[int]=40_96      ,					lowerCAmelCase	:      str=16      ,					lowerCAmelCase	:      List[Any]=0.0      ,					lowerCAmelCase	:      Optional[Any]=0.0      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Union[str, Any]=True      ,					lowerCAmelCase	:      List[Any]="gelu"      ,					lowerCAmelCase	:      int=10_24      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Optional[int]=0.0      ,					lowerCAmelCase	:      Tuple=0.0      ,					lowerCAmelCase	:      List[Any]=0.02      ,					lowerCAmelCase	:      Union[str, Any]=5_81_00      ,					lowerCAmelCase	:      str=False      ,					lowerCAmelCase	:      Optional[Any]=5_81_00      ,					lowerCAmelCase	:      Union[str, Any]=0      ,					lowerCAmelCase	:      Optional[Any]=0      ,					lowerCAmelCase	:      int=True      ,					**lowerCAmelCase	:      List[str]      ,					)						->					Dict:




         """simple docstring"""

         lowercase__  =	vocab_size
         lowercase__  =	decoder_vocab_size or vocab_size
         lowercase__  =	max_position_embeddings
         lowercase__  =	d_model
         lowercase__  =	encoder_ffn_dim
         lowercase__  =	encoder_layers
         lowercase__  =	encoder_attention_heads
         lowercase__  =	decoder_ffn_dim
         lowercase__  =	decoder_layers
         lowercase__  =	decoder_attention_heads
         lowercase__  =	dropout
         lowercase__  =	attention_dropout
         lowercase__  =	activation_dropout
         lowercase__  =	activation_function
         lowercase__  =	init_std
         lowercase__  =	encoder_layerdrop
         lowercase__  =	decoder_layerdrop
         lowercase__  =	use_cache
         lowercase__  =	encoder_layers
         lowercase__  =	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__  =	share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowerCAmelCase      ,					eos_token_id=lowerCAmelCase      ,					is_encoder_decoder=lowerCAmelCase      ,					decoder_start_token_id=lowerCAmelCase      ,					forced_eos_token_id=lowerCAmelCase      ,					**lowerCAmelCase      ,					)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def       UpperCAmelCase		(    self	:      List[Any])						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])

               if self.use_past:
                     lowercase__  =	{0: 'batch'}
                     lowercase__  =	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
               else:
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}

               if self.use_past:
                     self.fill_with_past_key_values_(lowerCAmelCase      ,					direction='inputs')
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         else:
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                       ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                       ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                   ])

         return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def       UpperCAmelCase		(    self	:      Any)						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super().outputs
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self).outputs
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         # Generate decoder inputs
         lowercase__  =	seq_length if not self.use_past else 1
         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         lowercase__  =	dict(**lowerCAmelCase      ,					**lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,						lowercase__  =	common_inputs['input_ids'].shape
               lowercase__  =	common_inputs['decoder_input_ids'].shape[1]
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase__  =	decoder_seq_length + 3
               lowercase__  =	(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               lowercase__  =	torch.cat(
                   [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase)]      ,					dim=1)

               lowercase__  =	[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__  =	min(lowerCAmelCase      ,					lowerCAmelCase)
               lowercase__  =	max(lowerCAmelCase      ,					lowerCAmelCase) - min_num_layers
               lowercase__  =	'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

               for _ in range(lowerCAmelCase):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                         ))
               # TODO: test this.
               lowercase__  =	encoder_shape if remaining_side_name == 'encoder' else decoder_shape
               for _ in range(lowerCAmelCase      ,					lowerCAmelCase):
                     common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,						lowercase__  =	common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               lowercase__  =	seqlen + 2
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               lowercase__  =	common_inputs['attention_mask'].dtype
               lowercase__  =	torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase      ,					dtype=lowerCAmelCase)]      ,					dim=1)
               lowercase__  =	[
                   (torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
               ]
         return common_inputs


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					fixed_dimension=OnnxConfig.default_fixed_batch      ,					num_token_to_add=0)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase__  =	tokenizer.num_special_tokens_to_add(lowerCAmelCase)
         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					fixed_dimension=OnnxConfig.default_fixed_sequence      ,					num_token_to_add=lowerCAmelCase)

         # Generate dummy inputs according to compute batch and sequence
         lowercase__  =	[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase__  =	dict(tokenizer(lowerCAmelCase      ,					return_tensors=lowerCAmelCase))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowerCAmelCase      ,					batch_size=lowerCAmelCase      ,					seq_length=lowerCAmelCase      ,					is_pair=lowerCAmelCase      ,					framework=lowerCAmelCase)

         else:
               lowercase__  =	self._generate_dummy_inputs_for_causal_lm(
                   lowerCAmelCase      ,					batch_size=lowerCAmelCase      ,					seq_length=lowerCAmelCase      ,					is_pair=lowerCAmelCase      ,					framework=lowerCAmelCase)

         return common_inputs


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str)						->					Optional[int]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super()._flatten_past_key_values_(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self)._flatten_past_key_values_(
                   lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)



   @property
   def       UpperCAmelCase		(    self	:      Optional[int])						->					float:




         """simple docstring"""

         return 1E-4
