





'''simple docstring'''







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 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__=10_24    ,   A__=10_24    ,   A__=3.6    ) ->	Dict:
      snake_case				=			tokenizer
      snake_case				=			tokenizer.bos_token_id
      snake_case				=			dataset
      snake_case				=			seq_length
      snake_case				=			seq_length * chars_per_token * num_of_sequences







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




def    __UpperCamelCase	(	a    : Optional[int]     ) ->str:
 snake_case				=			{'''streaming''': True}
 snake_case				=			load_dataset(args.dataset_name					,   split='''train'''					,   **a     )
 snake_case				=			ConstantLengthDataset(a					,   a					,   seq_length=args.seq_length     )
 snake_case				=			DataLoader(a					,   batch_size=args.batch_size     )
 return eval_dataloader

def    __UpperCamelCase	(	a    : List[Any]     ) ->Dict:
 model.eval()
 snake_case				=			[]
 for step, batch in enumerate(a     ):
  with torch.no_grad():
   snake_case				=			model(a					,   labels=a     )
  snake_case				=			outputs.loss.repeat(args.batch_size     )
  losses.append(accelerator.gather(a     )     )

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


# Setup Accelerator
_lowercase       						=			Accelerator()

# Parse configuration
_lowercase       						=			HfArgumentParser(EvaluationArguments)
_lowercase       						=			parser.parse_args()
set_seed(args.seed)

# Logging
_lowercase       						=			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
_lowercase       						=			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowercase       						=			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_lowercase       						=			create_dataloader(args)

# Prepare everything with our `accelerator`.
_lowercase						,				_lowercase       						=			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_lowercase						,				_lowercase       						=			evaluate(args)






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








'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowercase       						=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

_lowercase       						=			logging.getLogger()

def    __UpperCamelCase	(	) ->Tuple:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''-f'''     )
 snake_case				=			parser.parse_args()
 return args.f

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple="eval"     ) ->List[Any]:
 snake_case				=			os.path.join(a					,   f"""{split}_results.json"""     )
 if os.path.exists(a     ):
  with open(a					,   '''r'''     ) as f:
   return json.load(a     )
 raise ValueError(f"""can't find {path}"""     )


_lowercase       						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			_lowercase						(       __a					):







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_glue.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_clm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   1_00    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_summarization_flax.main()
       snake_case				=			get_results(A__    ,   split='''test'''    )
       self.assertGreaterEqual(result['''test_rouge1''']    ,   10    )
       self.assertGreaterEqual(result['''test_rouge2''']    ,   2    )
       self.assertGreaterEqual(result['''test_rougeL''']    ,   7    )
       self.assertGreaterEqual(result['''test_rougeLsum''']    ,   7    )







     @slow
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   42    )







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_ta_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.4_2    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      snake_case				=			7 if get_gpu_count() > 1 else 2

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_ner.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   0.3    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_qa.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   30    )
       self.assertGreaterEqual(result['''eval_exact''']    ,   30    )

