"""simple docstring"""







import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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


def   __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase			:     str      					=				argparse.ArgumentParser(description='PyTorch Transformer Language Model'      )
	parser.add_argument('--model_name'  ,type=_A  ,default='transfo-xl-wt103'  ,help='pretrained model name'      )
	parser.add_argument(
	    '--split'  ,type=_A  ,default='test'  ,choices=['all', 'valid', 'test']  ,help='which split to evaluate'      )
	parser.add_argument('--batch_size'  ,type=_A  ,default=10  ,help='batch size'      )
	parser.add_argument('--tgt_len'  ,type=_A  ,default=128  ,help='number of tokens to predict'      )
	parser.add_argument('--ext_len'  ,type=_A  ,default=0  ,help='length of the extended context'      )
	parser.add_argument('--mem_len'  ,type=_A  ,default=1_600  ,help='length of the retained previous heads'      )
	parser.add_argument('--clamp_len'  ,type=_A  ,default=1_000  ,help='max positional embedding index'      )
	parser.add_argument('--no_cuda'  ,action='store_true'  ,help='Do not use CUDA even though CUA is available'      )
	parser.add_argument('--work_dir'  ,type=_A  ,required=_A  ,help='path to the work_dir'      )
	parser.add_argument('--no_log'  ,action='store_true'  ,help='do not log the eval result'      )
	parser.add_argument('--same_length'  ,action='store_true'  ,help='set same length attention with masking'      )
	parser.add_argument('--server_ip'  ,type=_A  ,default=''  ,help='Can be used for distant debugging.'      )
	parser.add_argument('--server_port'  ,type=_A  ,default=''  ,help='Can be used for distant debugging.'      )
	_lowercase			:     str      					=				parser.parse_args()
	assert args.ext_len >= 0, "extended context length must be non-negative"

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print('Waiting for debugger attach'      )
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,redirect_output=_A      )
		ptvsd.wait_for_attach()

	_lowercase			:     Optional[Any]      					=				torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
	logger.info('device: {}'.format(_A      )      )

	# Load a pre-processed dataset
	# You can also build the corpus yourself using TransfoXLCorpus methods
	# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
	# and tokenizing the dataset
	# The pre-processed corpus is a convertion (using the conversion script )
	_lowercase			:     int      					=				TransfoXLCorpus.from_pretrained(args.model_name      )

	_lowercase			:     Tuple      					=				corpus.get_iterator('valid'  ,args.batch_size  ,args.tgt_len  ,device=_A  ,ext_len=args.ext_len      )
	_lowercase			:     Union[str, Any]      					=				corpus.get_iterator('test'  ,args.batch_size  ,args.tgt_len  ,device=_A  ,ext_len=args.ext_len      )

	# Load a pre-trained model
	_lowercase			:     int      					=				TransfoXLLMHeadModel.from_pretrained(args.model_name      )
	model.to(_A      )

	logger.info(
	    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
	        args.batch_size  ,args.tgt_len  ,args.ext_len  ,args.mem_len  ,args.clamp_len      )      )

	model.reset_memory_length(args.mem_len      )
	if args.clamp_len > 0:
		_lowercase			:     Optional[int]      					=				args.clamp_len
	if args.same_length:
		_lowercase			:     Optional[int]      					=				True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(__UpperCAmelCase      ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		_lowercase			:     List[str]      					=				0, 0.0
		_lowercase			:     List[Any]      					=				time.time()
		with torch.no_grad():
			_lowercase			:     Union[str, Any]      					=				None
			for idx, (data, target, seq_len) in enumerate(_A      ):
				_lowercase			:     str      					=				model(_A  ,lm_labels=_A  ,mems=_A      )
				_lowercase			:     Optional[int]      					=				ret
				_lowercase			:     List[Any]      					=				loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			_lowercase			:     List[Any]      					=				time.time() - start_time
		logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A  ,1_000 * total_time / (idx + 1)      )      )
		return total_loss / total_len

	# Run on test data.
	if args.split == "all":
		_lowercase			:     Tuple      					=				evaluate(_A      )
		_lowercase			:     Optional[int]      					=				evaluate(_A      )
	elif args.split == "valid":
		_lowercase			:     List[str]      					=				evaluate(_A      )
		_lowercase			:     str      					=				None
	elif args.split == "test":
		_lowercase			:     Union[str, Any]      					=				evaluate(_A      )
		_lowercase			:     Any      					=				None

	def format_log(__UpperCAmelCase  ,__UpperCAmelCase      ):
		_lowercase			:     Dict      					=				"| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(_A  ,_A  ,math.exp(_A      )      )
		return log_str

	_lowercase			:     List[str]      					=				""
	if valid_loss is not None:
		log_str += format_log(_A  ,'valid'      )
	if test_loss is not None:
		log_str += format_log(_A  ,'test'      )

	logger.info('=' * 100      )
	logger.info(_A      )
	logger.info('=' * 100      )


if __name__ == "__main__":
						main()







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[Any]=3   , __lowerCamelCase				:     Dict=32   , __lowerCamelCase				:     Optional[Any]=3   , __lowerCamelCase				:     List[str]=10   , __lowerCamelCase				:     List[str]=[10, 20, 30, 40]   , __lowerCamelCase				:     List[Any]=[1, 1, 2, 1]   , __lowerCamelCase				:     Any=True   , __lowerCamelCase				:     Optional[int]=True   , __lowerCamelCase				:     int="relu"   , __lowerCamelCase				:     List[str]=3   , __lowerCamelCase				:     Any=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    parent
             lowerCamelCase__						:							Optional[int]       =    batch_size
             lowerCamelCase__						:							List[Any]       =    image_size
             lowerCamelCase__						:							Union[str, Any]       =    num_channels
             lowerCamelCase__						:							Any       =    embeddings_size
             lowerCamelCase__						:							Tuple       =    hidden_sizes
             lowerCamelCase__						:							Tuple       =    depths
             lowerCamelCase__						:							List[str]       =    is_training
             lowerCamelCase__						:							Optional[Any]       =    use_labels
             lowerCamelCase__						:							str       =    hidden_act
             lowerCamelCase__						:							Any       =    num_labels
             lowerCamelCase__						:							List[str]       =    scope
             lowerCamelCase__						:							Any       =    len(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

             lowerCamelCase__						:							Any       =    self.get_config()

             return config, pixel_values





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             return ResNetConfig(
                 num_channels=self.num_channels   , embeddings_size=self.embeddings_size   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , hidden_act=self.hidden_act   , num_labels=self.num_labels   , image_size=self.image_size   , )





       def 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    FlaxResNetModel(config=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)

             # Output shape (b, c, h, w)
             self.parent.assertEqual(
                 result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   , )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							int       =    FlaxResNetForImageClassification(config=__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    config_and_inputs
             lowerCamelCase__						:							Optional[int]       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_flax
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    FlaxResNetModelTester(self			)
             lowerCamelCase__						:							Dict       =    ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             self.create_and_test_config_common_properties()
             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				:     Dict			):


             '''simple docstring'''



             return





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





       @unittest.skip(reason="ResNet does not use inputs_embeds"			)
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ResNet does not support input and output embeddings"			)
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase__						:							List[Any]       =    ["pixel_values"]
                   self.assertListEqual(arg_names[:1]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str			):
                   lowerCamelCase__						:							Any       =    model_class(__lowerCamelCase			)

                   lowerCamelCase__						:							Dict       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   lowerCamelCase__						:							str       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   lowerCamelCase__						:							Tuple       =    self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase			)   , expected_num_stages + 1			)





       @unittest.skip(reason="ResNet does not use feedforward chunking"			)
       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__			):
                         lowerCamelCase__						:							Any       =    self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)
                         lowerCamelCase__						:							Optional[int]       =    model_class(__lowerCamelCase			)

                         @jax.jit
                         def model_jitted(__lowerCamelCase				:     Optional[Any]   , **__lowerCamelCase				:     List[Any]			):
                               return model(pixel_values=__lowerCamelCase   , **__lowerCamelCase			)

                         with self.subTest("JIT Enabled"			):
                               lowerCamelCase__						:							Any       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         with self.subTest("JIT Disabled"			):
                               with jax.disable_jit():
                                     lowerCamelCase__						:							Union[str, Any]       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         self.assertEqual(len(__lowerCamelCase			)   , len(__lowerCamelCase			)			)
                         for jitted_output, output in zip(__lowerCamelCase   , __lowerCamelCase			):
                               self.assertEqual(jitted_output.shape   , output.shape			)



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
      return image







@require_flax
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             return AutoImageProcessor.from_pretrained("microsoft/resnet-50"			) if is_vision_available() else None





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"			)

             lowerCamelCase__						:							List[str]       =    self.default_image_processor
             lowerCamelCase__						:							Optional[Any]       =    prepare_img()
             lowerCamelCase__						:							str       =    image_processor(images=__lowerCamelCase   , return_tensors="np"			)

             lowerCamelCase__						:							str       =    model(**__lowerCamelCase			)

             # verify the logits
             lowerCamelCase__						:							int       =    (1, 1000)
             self.assertEqual(outputs.logits.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Optional[int]       =    jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

             self.assertTrue(jnp.allclose(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1E-4			)			)


