


'''simple docstring'''






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 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=3					,    lowerCamelCase_=32					,    lowerCamelCase_=3					,    lowerCamelCase_=10					,    lowerCamelCase_=[10, 20, 30, 40]					,    lowerCamelCase_=[1, 1, 2, 1]					,    lowerCamelCase_=True					,    lowerCamelCase_=True					,    lowerCamelCase_="relu"					,    lowerCamelCase_=3					,    lowerCamelCase_=None					,    )       ->							List[Any]:
														lowerCAmelCase__          =	parent
														lowerCAmelCase__          =	batch_size
														lowerCAmelCase__          =	image_size
														lowerCAmelCase__          =	num_channels
														lowerCAmelCase__          =	embeddings_size
														lowerCAmelCase__          =	hidden_sizes
														lowerCAmelCase__          =	depths
														lowerCAmelCase__          =	is_training
														lowerCAmelCase__          =	use_labels
														lowerCAmelCase__          =	hidden_act
														lowerCAmelCase__          =	num_labels
														lowerCAmelCase__          =	scope
														lowerCAmelCase__          =	len(A_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

														lowerCAmelCase__          =	self.get_config()

														return config, pixel_values







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	FlaxResNetModel(config=A_   )
														lowerCAmelCase__          =	model(A_   )

														# 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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[Any]:
														lowerCAmelCase__          =	self.num_labels
														lowerCAmelCase__          =	FlaxResNetForImageClassification(config=A_   )
														lowerCAmelCase__          =	model(A_   )
														self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.num_labels)   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														lowerCAmelCase__          =	self.prepare_config_and_inputs()
														lowerCAmelCase__   ,					lowerCAmelCase__          =	config_and_inputs
														lowerCAmelCase__          =	{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_flax
class 		a__				(							snake_case_				,				unittest.TestCase  ):





							'''simple docstring'''






							lowercase__			:   Optional[Any]							=      (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							lowercase__			:   str							=      False
							lowercase__			:   int							=      False
							lowercase__			:   Union[str, Any]							=      False







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							None:
														lowerCAmelCase__          =	FlaxResNetModelTester(self   )
														lowerCAmelCase__          =	ConfigTester(self					,    config_class=A_					,    has_text_modality=A_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														return







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*A_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*A_   )







							@unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														pass







							@unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__          =	['''pixel_values''']
																					self.assertListEqual(arg_names[:1]					,    A_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:






														def check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   ):
																					lowerCAmelCase__          =	model_class(A_   )

																					lowerCAmelCase__          =	model(**self._prepare_for_class(A_					,    A_   )   )

																					lowerCAmelCase__          =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					lowerCAmelCase__          =	self.model_tester.num_stages
																					self.assertEqual(len(A_   )					,    expected_num_stages + 1   )







							@unittest.skip(reason='''ResNet does not use feedforward chunking'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__   ):
																												lowerCAmelCase__          =	self._prepare_for_class(A_					,    A_   )
																												lowerCAmelCase__          =	model_class(A_   )

																												@jax.jit
																												def model_jitted(lowerCamelCase_					,    **lowerCamelCase_   ):
																																			return model(pixel_values=A_					,    **A_   )

																												with self.subTest('''JIT Enabled'''   ):
																																			lowerCAmelCase__          =	model_jitted(**A_   ).to_tuple()

																												with self.subTest('''JIT Disabled'''   ):
																																			with jax.disable_jit():
																																										lowerCAmelCase__          =	model_jitted(**A_   ).to_tuple()

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


def 					_snake_case   (     )						->    Dict:
							lowerCAmelCase__          =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
							return image






@require_flax
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''   ) if is_vision_available() else None







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''   )

														lowerCAmelCase__          =	self.default_image_processor
														lowerCAmelCase__          =	prepare_img()
														lowerCAmelCase__          =	image_processor(images=A_					,    return_tensors='''np'''   )

														lowerCAmelCase__          =	model(**A_   )

														# verify the logits
														lowerCAmelCase__          =	(1, 10_00)
														self.assertEqual(outputs.logits.shape					,    A_   )

														lowerCAmelCase__          =	jnp.array([-11.1_069, -9.7_877, -8.3_777]   )

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

'''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
)
lowerCAmelCase :  Optional[Any]      =      logging.getLogger(__name__)







def 						A_(			):
							UpperCamelCase					       =			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=1600						,			help='length of the retained previous heads')
							parser.add_argument('--clamp_len'						,			type=A						,			default=1000						,			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.')
							UpperCamelCase					       =			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()

							UpperCamelCase					       =			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 )
							UpperCamelCase					       =			TransfoXLCorpus.from_pretrained(args.model_name)

							UpperCamelCase					       =			corpus.get_iterator('valid'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)
							UpperCamelCase					       =			corpus.get_iterator('test'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)

							# Load a pre-trained model
							UpperCamelCase					       =			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:
														UpperCamelCase					       =			args.clamp_len
							if args.same_length:
														UpperCamelCase					       =			True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(A		:						Dict):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														UpperCamelCase       ,     UpperCamelCase					       =			0, 0.0
														UpperCamelCase					       =			time.time()
														with torch.no_grad():
																					UpperCamelCase					       =			None
																					for idx, (data, target, seq_len) in enumerate(A):
																												UpperCamelCase					       =			model(A						,			lm_labels=A						,			mems=A)
																												UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			ret
																												UpperCamelCase					       =			loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					UpperCamelCase					       =			time.time() - start_time
														logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A						,			1000 * total_time / (idx + 1)))
														return total_loss / total_len

							# Run on test data.
							if args.split == "all":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			evaluate(A)
							elif args.split == "valid":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None
							elif args.split == "test":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None

							def format_log(A		:						Any						,			A		:						Optional[Any]):
														UpperCamelCase					       =			'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(A						,			A						,			math.exp(A))
														return log_str

							UpperCamelCase					       =			''
							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()




