
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
					from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import GLPNImageProcessor


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.config_class(**self.inputs_dict			)
				self.parent.assertTrue(hasattr(A_							, 'hidden_sizes'			)			)
				self.parent.assertTrue(hasattr(A_							, 'num_attention_heads'			)			)
				self.parent.assertTrue(hasattr(A_							, 'num_encoder_blocks'			)			)






class 							lowercase   :



		def __init__(					self							, A_							, A_=13							, A_=64							, A_=3							, A_=4							, A_=[2, 2, 2, 2]							, A_=[8, 4, 2, 1]							, A_=[16, 32, 64, 128]							, A_=[1, 4, 8, 16]							, A_=[1, 2, 4, 8]							, A_=True							, A_=True							, A_="gelu"							, A_=0.1							, A_=0.1							, A_=0.02							, A_=16							, A_=3							, A_=None							, )    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	parent
				UpperCamelCase            =	batch_size
				UpperCamelCase            =	image_size
				UpperCamelCase            =	num_channels
				UpperCamelCase            =	num_encoder_blocks
				UpperCamelCase            =	sr_ratios
				UpperCamelCase            =	depths
				UpperCamelCase            =	hidden_sizes
				UpperCamelCase            =	downsampling_rates
				UpperCamelCase            =	num_attention_heads
				UpperCamelCase            =	is_training
				UpperCamelCase            =	use_labels
				UpperCamelCase            =	hidden_act
				UpperCamelCase            =	hidden_dropout_prob
				UpperCamelCase            =	attention_probs_dropout_prob
				UpperCamelCase            =	initializer_range
				UpperCamelCase            =	decoder_hidden_size
				UpperCamelCase            =	num_labels
				UpperCamelCase            =	scope



		def __UpperCamelCase					(					self			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

				UpperCamelCase            =	None
				if self.use_labels:
						UpperCamelCase            =	ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels			)

				UpperCamelCase            =	self.get_config()
				return config, pixel_values, labels



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				return GLPNConfig(
				    image_size=self.image_size							, num_channels=self.num_channels							, num_encoder_blocks=self.num_encoder_blocks							, depths=self.depths							, hidden_sizes=self.hidden_sizes							, num_attention_heads=self.num_attention_heads							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, initializer_range=self.initializer_range							, decoder_hidden_size=self.decoder_hidden_size							, )



		def __UpperCamelCase					(					self							, A_							, A_							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	GLPNModel(config=A_			)
				model.to(A_			)
				model.eval()
				UpperCamelCase            =	model(A_			)
				UpperCamelCase            =	UpperCamelCase            =	self.image_size // (self.downsampling_rates[-1] * 2)
				self.parent.assertEqual(
				    result.last_hidden_state.shape							, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.num_labels
				UpperCamelCase            =	GLPNForDepthEstimation(A_			)
				model.to(A_			)
				model.eval()
				UpperCamelCase            =	model(A_			)
				self.parent.assertEqual(result.predicted_depth.shape							, (self.batch_size, self.image_size, self.image_size)			)
				UpperCamelCase            =	model(A_							, labels=A_			)
				self.parent.assertEqual(result.predicted_depth.shape							, (self.batch_size, self.image_size, self.image_size)			)



		def __UpperCamelCase					(					self			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.prepare_config_and_inputs()
				UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase            =	config_and_inputs
				UpperCamelCase            =	{'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 							lowercase   (						_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  unittest.TestCase  ):
		__lowercase			:			Tuple      	=							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
		__lowercase			:			Dict      	=							(
		    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
		)

		__lowercase			:			List[Any]      	=							False
		__lowercase			:			str      	=							False
		__lowercase			:			Union[str, Any]      	=							False



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	GLPNModelTester(self			)
				UpperCamelCase            =	GLPNConfigTester(self							, config_class=A_			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*A_			)



		@unittest.skip('GLPN does not use inputs_embeds'			)
		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				pass



		@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'			)
		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				pass



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	self.model_tester.prepare_config_and_inputs_for_common()

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

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



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase            =	True

				for model_class in self.all_model_classes:
						UpperCamelCase            =	True
						UpperCamelCase            =	False
						UpperCamelCase            =	True
						UpperCamelCase            =	model_class(A_			)
						model.to(A_			)
						model.eval()
						with torch.no_grad():
								UpperCamelCase            =	model(**self._prepare_for_class(A_							, A_			)			)
						UpperCamelCase            =	outputs.attentions

						UpperCamelCase            =	sum(self.model_tester.depths			)
						self.assertEqual(len(A_			)							, A_			)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						UpperCamelCase            =	True
						UpperCamelCase            =	model_class(A_			)
						model.to(A_			)
						model.eval()
						with torch.no_grad():
								UpperCamelCase            =	model(**self._prepare_for_class(A_							, A_			)			)
						UpperCamelCase            =	outputs.attentions

						self.assertEqual(len(A_			)							, A_			)

						# verify the first attentions (first block, first layer)
						UpperCamelCase            =	(self.model_tester.image_size // 4) ** 2
						UpperCamelCase            =	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(attentions[0].shape[-3:]			)							, [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							, )

						# verify the last attentions (last block, last layer)
						UpperCamelCase            =	(self.model_tester.image_size // 32) ** 2
						UpperCamelCase            =	(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
						self.assertListEqual(
						    list(attentions[-1].shape[-3:]			)							, [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]							, )
						UpperCamelCase            =	len(A_			)

						# Check attention is always last and order is fine
						UpperCamelCase            =	True
						UpperCamelCase            =	True
						UpperCamelCase            =	model_class(A_			)
						model.to(A_			)
						model.eval()
						with torch.no_grad():
								UpperCamelCase            =	model(**self._prepare_for_class(A_							, A_			)			)

						self.assertEqual(out_len + 1							, len(A_			)			)

						UpperCamelCase            =	outputs.attentions

						self.assertEqual(len(A_			)							, A_			)
						# verify the first attentions (first block, first layer)
						UpperCamelCase            =	(self.model_tester.image_size // 4) ** 2
						UpperCamelCase            =	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]			)							, [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							, )



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				def check_hidden_states_output(A_							, A_							, A_			):
						UpperCamelCase            =	model_class(A_			)
						model.to(A_			)
						model.eval()

						with torch.no_grad():
								UpperCamelCase            =	model(**self._prepare_for_class(A_							, A_			)			)

						UpperCamelCase            =	outputs.hidden_states

						UpperCamelCase            =	self.model_tester.num_encoder_blocks
						self.assertEqual(len(A_			)							, A_			)

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]			)							, [
						        self.model_tester.hidden_sizes[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]							, )

				UpperCamelCase      ,    UpperCamelCase            =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase            =	True
						check_hidden_states_output(A_							, A_							, A_			)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase            =	True

						check_hidden_states_output(A_							, A_							, A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

				UpperCamelCase      ,    UpperCamelCase            =	self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase            =	True

				for model_class in self.all_model_classes:
						if model_class in get_values(A_			):
								continue
						# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
						# this can then be incorporated into _prepare_for_class in test_modeling_common.py
						if model_class.__name__ == "GLPNForDepthEstimation":
								UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase            =	inputs_dict['pixel_values'].shape
								UpperCamelCase            =	torch.zeros(
								    [self.model_tester.batch_size, height, width]							, device=A_			).long()
						UpperCamelCase            =	model_class(A_			)
						model.to(A_			)
						model.train()
						UpperCamelCase            =	self._prepare_for_class(A_							, A_							, return_labels=A_			)
						UpperCamelCase            =	model(**A_			).loss
						loss.backward()



		@slow
		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase            =	GLPNModel.from_pretrained(A_			)
						self.assertIsNotNone(A_			)






def  A			(   )  ->    int:
		'''simple docstring'''

		UpperCamelCase            =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
		return image


@require_torch
@require_vision
@slow
class 							lowercase   (						unittest.TestCase  ):



		@slow
		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
				UpperCamelCase            =	GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(A_			)

				UpperCamelCase            =	prepare_img()
				UpperCamelCase            =	image_processor(images=A_							, return_tensors='pt'			).to(A_			)

				# forward pass
				with torch.no_grad():
						UpperCamelCase            =	model(**A_			)

				# verify the predicted depth
				UpperCamelCase            =	torch.Size([1, 480, 640]			)
				self.assertEqual(outputs.predicted_depth.shape							, A_			)

				UpperCamelCase            =	torch.tensor(
				    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]			).to(A_			)

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


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
)
_UpperCAmelCase   :					List[str]    =			logging.getLogger(__name__)


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
		parser.add_argument('--model_name'       ,			type=lowercase       ,			default='transfo-xl-wt103'       ,			help='pretrained model name'					)
		parser.add_argument(
		    '--split'       ,			type=lowercase       ,			default='test'       ,			choices=['all', 'valid', 'test']       ,			help='which split to evaluate'					)
		parser.add_argument('--batch_size'       ,			type=lowercase       ,			default=10       ,			help='batch size'					)
		parser.add_argument('--tgt_len'       ,			type=lowercase       ,			default=128       ,			help='number of tokens to predict'					)
		parser.add_argument('--ext_len'       ,			type=lowercase       ,			default=0       ,			help='length of the extended context'					)
		parser.add_argument('--mem_len'       ,			type=lowercase       ,			default=1_600       ,			help='length of the retained previous heads'					)
		parser.add_argument('--clamp_len'       ,			type=lowercase       ,			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=lowercase       ,			required=lowercase       ,			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=lowercase       ,			default=''       ,			help='Can be used for distant debugging.'					)
		parser.add_argument('--server_port'       ,			type=lowercase       ,			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=lowercase					)
				ptvsd.wait_for_attach()

		UpperCamelCase            =	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
		logger.info('device: {}'.format(lowercase					)					)

		# 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=lowercase       ,			ext_len=args.ext_len					)
		UpperCamelCase            =	corpus.get_iterator('test'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowercase       ,			ext_len=args.ext_len					)

		# Load a pre-trained model
		UpperCamelCase            =	TransfoXLLMHeadModel.from_pretrained(args.model_name					)
		model.to(lowercase					)

		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(lowercase					):
				# 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(lowercase					):
								UpperCamelCase            =	model(lowercase       ,			lm_labels=lowercase       ,			mems=lowercase					)
								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(lowercase       ,			1_000 * total_time / (idx + 1)					)					)
				return total_loss / total_len

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

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

		UpperCamelCase            =	''
		if valid_loss is not None:
				log_str += format_log(lowercase       ,			'valid'					)
		if test_loss is not None:
				log_str += format_log(lowercase       ,			'test'					)

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


if __name__ == "__main__":
					main()
