


import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import BlipTextModel
		from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    List[str]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Any=12	,							lowerCamelCase_     :    str=7	,							lowerCamelCase_     :    Tuple=True	,							lowerCamelCase_     :    Union[str, Any]=True	,							lowerCamelCase_     :    List[str]=True	,							lowerCamelCase_     :    Optional[int]=99	,							lowerCamelCase_     :    int=32	,							lowerCamelCase_     :    Tuple=32	,							lowerCamelCase_     :    int=5	,							lowerCamelCase_     :    int=4	,							lowerCamelCase_     :    Any=37	,							lowerCamelCase_     :    List[Any]=0.1	,							lowerCamelCase_     :    Dict=0.1	,							lowerCamelCase_     :    int=512	,							lowerCamelCase_     :    Optional[int]=0.02	,							lowerCamelCase_     :    str=0	,							lowerCamelCase_     :    Union[str, Any]=None	,							):
								_lowerCAmelCase  						=parent
								_lowerCAmelCase  						=batch_size
								_lowerCAmelCase  						=seq_length
								_lowerCAmelCase  						=is_training
								_lowerCAmelCase  						=use_input_mask
								_lowerCAmelCase  						=use_labels
								_lowerCAmelCase  						=vocab_size
								_lowerCAmelCase  						=hidden_size
								_lowerCAmelCase  						=projection_dim
								_lowerCAmelCase  						=num_hidden_layers
								_lowerCAmelCase  						=num_attention_heads
								_lowerCAmelCase  						=intermediate_size
								_lowerCAmelCase  						=dropout
								_lowerCAmelCase  						=attention_dropout
								_lowerCAmelCase  						=max_position_embeddings
								_lowerCAmelCase  						=initializer_range
								_lowerCAmelCase  						=scope
								_lowerCAmelCase  						=bos_token_id






				def        lowerCAmelCase__			(				self     :    List[Any]					):
								_lowerCAmelCase  						=ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size					)

								_lowerCAmelCase  						=None
								if self.use_input_mask:
												_lowerCAmelCase  						=random_attention_mask([self.batch_size, self.seq_length]					)

								if input_mask is not None:
												_lowerCAmelCase						,							_lowerCAmelCase  						=input_mask.shape
												_lowerCAmelCase  						=np.random.randint(1	,							seq_length - 1	,							size=(batch_size,)					)
												for batch_idx, start_index in enumerate(A_					):
																_lowerCAmelCase  						=1
																_lowerCAmelCase  						=0

								_lowerCAmelCase  						=self.get_config()

								return config, input_ids, input_mask






				def        lowerCAmelCase__			(				self     :    List[str]					):
								return BlipTextConfig(
								    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							projection_dim=self.projection_dim	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							dropout=self.dropout	,							attention_dropout=self.attention_dropout	,							max_position_embeddings=self.max_position_embeddings	,							initializer_range=self.initializer_range	,							bos_token_id=self.bos_token_id	,							)






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Any					):
								_lowerCAmelCase  						=BlipTextModel(config=A_					)
								model.to(A_					)
								model.eval()
								with torch.no_grad():
												_lowerCAmelCase  						=model(A_	,							attention_mask=A_					)
												_lowerCAmelCase  						=model(A_					)
								self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)					)
								self.parent.assertEqual(result.pooler_output.shape	,							(self.batch_size, self.hidden_size)					)






				def        lowerCAmelCase__			(				self     :    List[str]					):
								_lowerCAmelCase  						=self.prepare_config_and_inputs()
								_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase  						=config_and_inputs
								_lowerCAmelCase  						={"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict


@require_torch
class        __lowerCamelCase      (				_SCREAMING_SNAKE_CASE				,		unittest.TestCase ):


				"""simple docstring"""







				a_:		Any					      =	(BlipTextModel,) if is_torch_available() else ()
				a_:		List[str]					      =	False
				a_:		Dict					      =	False
				a_:		Union[str, Any]					      =	False






				def        lowerCAmelCase__			(				self     :    Any					):
								_lowerCAmelCase  						=BlipTextModelTester(self					)
								_lowerCAmelCase  						=ConfigTester(self	,							config_class=A_	,							hidden_size=37					)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								self.config_tester.run_common_tests()






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A_					)






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								pass






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								pass






				@unittest.skip(reason="""Blip does not use inputs_embeds"""					)
				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								pass






				@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""					)
				def        lowerCAmelCase__			(				self     :    List[Any]					):
								pass






				@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""					)
				def        lowerCAmelCase__			(				self     :    Dict					):
								pass






				@slow
				def        lowerCAmelCase__			(				self     :    Any					):
								for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase  						=BlipTextModel.from_pretrained(A_					)
												self.assertIsNotNone(A_					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								super().test_pt_tf_model_equivalence(allow_missing_keys=A_					)



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()
