



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
  import torch

  from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase="resnet50"	,				__UpperCAmelCase=3	,				__UpperCAmelCase=32	,				__UpperCAmelCase=3	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    out_indices if out_indices is not None else [4]
    __lowerCamelCase													=    stage_names
    __lowerCamelCase													=    out_features
    __lowerCamelCase													=    backbone
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    image_size
    __lowerCamelCase													=    num_channels
    __lowerCamelCase													=    use_pretrained_backbone
    __lowerCamelCase													=    is_training




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
    __lowerCamelCase													=    self.get_config()

    return config, pixel_values




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return TimmBackboneConfig(
        image_size=self.image_size	,				num_channels=self.num_channels	,				out_features=self.out_features	,				out_indices=self.out_indices	,				stage_names=self.stage_names	,				use_pretrained_backbone=self.use_pretrained_backbone	,				backbone=self.backbone	,				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    TimmBackbone(config=__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    with torch.no_grad():
       __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(
        result.feature_map[-1].shape	,				(self.batch_size, model.channels[-1], 14, 14)	,				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase													=    config_and_inputs
    __lowerCamelCase													=    {'''pixel_values''': pixel_values}
    return config, inputs_dict



@require_torch
@require_timm
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (TimmBackbone,) if is_torch_available() else ()
 lowerCAmelCase__       					=  {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TimmBackboneModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase	,				has_text_modality=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''resnet18'''
    __lowerCamelCase													=    '''microsoft/resnet-18'''

    __lowerCamelCase													=    AutoBackbone.from_pretrained(__UpperCAmelCase	,				use_timm_backbone=__UpperCAmelCase				)
    __lowerCamelCase													=    AutoBackbone.from_pretrained(__UpperCAmelCase				)

    self.assertEqual(len(timm_model.out_features				)	,				len(transformers_model.out_features				)				)
    self.assertEqual(len(timm_model.stage_names				)	,				len(transformers_model.stage_names				)				)
    self.assertEqual(timm_model.channels	,				transformers_model.channels				)
    # Out indices are set to the last layer by default. For timm models, we don't know
    # the number of layers in advance, so we set it to (-1,), whereas for transformers
    # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
    self.assertEqual(timm_model.out_indices	,				(-1,)				)
    self.assertEqual(transformers_model.out_indices	,				[len(timm_model.stage_names				) - 1]				)

    __lowerCamelCase													=    AutoBackbone.from_pretrained(__UpperCAmelCase	,				use_timm_backbone=__UpperCAmelCase	,				out_indices=[1, 2, 3]				)
    __lowerCamelCase													=    AutoBackbone.from_pretrained(__UpperCAmelCase	,				out_indices=[1, 2, 3]				)

    self.assertEqual(timm_model.out_indices	,				transformers_model.out_indices				)
    self.assertEqual(len(timm_model.out_features				)	,				len(transformers_model.out_features				)				)
    self.assertEqual(timm_model.channels	,				transformers_model.channels				)




 @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''Safetensors is not supported by timm.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCamelCase													=    model_class(__UpperCAmelCase				)
       __lowerCamelCase													=    inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCamelCase													=    [*signature.parameters.keys()]

       __lowerCamelCase													=    ['''pixel_values''']
       self.assertListEqual(arg_names[:1]	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase													=    True
    __lowerCamelCase													=    self.has_attentions

    # no need to test all models as different heads yield the same functionality
    __lowerCamelCase													=    self.all_model_classes[0]
    __lowerCamelCase													=    model_class(__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)

    __lowerCamelCase													=    self._prepare_for_class(__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    model(**__UpperCAmelCase				)
    __lowerCamelCase													=    outputs[0][-1]

    # Encoder-/Decoder-only models
    __lowerCamelCase													=    outputs.hidden_states[0]
    hidden_states.retain_grad()

    if self.has_attentions:
       __lowerCamelCase													=    outputs.attentions[0]
       attentions.retain_grad()

    output.flatten()[0].backward(retain_graph=__UpperCAmelCase				)

    self.assertIsNotNone(hidden_states.grad				)

    if self.has_attentions:
       self.assertIsNotNone(attentions.grad				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCamelCase													=    model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __lowerCamelCase													=    model(**__UpperCAmelCase				)

       self.assertEqual(len(result.feature_maps				)	,				len(config.out_indices				)				)
       self.assertEqual(len(model.channels				)	,				len(config.out_indices				)				)

       # Check output of last stage is taken if out_features=None, out_indices=None
       __lowerCamelCase													=    copy.deepcopy(__UpperCAmelCase				)
       __lowerCamelCase													=    None
       __lowerCamelCase													=    model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __lowerCamelCase													=    model(**__UpperCAmelCase				)

       self.assertEqual(len(result.feature_maps				)	,				1				)
       self.assertEqual(len(model.channels				)	,				1				)

       # Check backbone can be initialized with fresh weights
       __lowerCamelCase													=    copy.deepcopy(__UpperCAmelCase				)
       __lowerCamelCase													=    False
       __lowerCamelCase													=    model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __lowerCamelCase													=    model(**__UpperCAmelCase				)











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  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       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				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





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

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


# Setup Accelerator
a_			=					Accelerator()

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

# Logging
a_			=					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_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

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

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





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






