

"""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
)
__snake_case							:     Optional[Any] 						=	logging.getLogger(__name__)


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				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=1_0       ,					help="""batch size""")
			parser.add_argument("""--tgt_len"""       ,					type=a__       ,					default=1_2_8       ,					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_6_0_0       ,					help="""length of the retained previous heads""")
			parser.add_argument("""--clamp_len"""       ,					type=a__       ,					default=1_0_0_0       ,					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.""")
			a_		:		int				    =				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()

			a_		:		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 )
			a_		:		List[Any]				    =				TransfoXLCorpus.from_pretrained(args.model_name)

			a_		:		str				    =				corpus.get_iterator("""valid"""       ,					args.batch_size       ,					args.tgt_len       ,					device=a__       ,					ext_len=args.ext_len)
			a_		:		str				    =				corpus.get_iterator("""test"""       ,					args.batch_size       ,					args.tgt_len       ,					device=a__       ,					ext_len=args.ext_len)

			# Load a pre-trained model
			a_		:		Tuple				    =				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:
						a_		:		List[Any]				    =				args.clamp_len
			if args.same_length:
						a_		:		List[Any]				    =				True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(a__):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						a_		:		Optional[Any]				    =				0, 0.0
						a_		:		Any				    =				time.time()
						with torch.no_grad():
									a_		:		str				    =				None
									for idx, (data, target, seq_len) in enumerate(a__):
												a_		:		Optional[int]				    =				model(a__       ,					lm_labels=a__       ,					mems=a__)
												a_		:		List[Any]				    =				ret
												a_		:		str				    =				loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									a_		:		Union[str, Any]				    =				time.time() - start_time
						logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(a__       ,					1_0_0_0 * total_time / (idx + 1)))
						return total_loss / total_len

			# Run on test data.
			if args.split == "all":
						a_		:		Optional[int]				    =				evaluate(a__)
						a_		:		List[str]				    =				evaluate(a__)
			elif args.split == "valid":
						a_		:		List[Any]				    =				evaluate(a__)
						a_		:		List[str]				    =				None
			elif args.split == "test":
						a_		:		int				    =				evaluate(a__)
						a_		:		str				    =				None

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

			a_		:		int				    =				''
			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("""=""" * 1_0_0)
			logger.info(a__)
			logger.info("""=""" * 1_0_0)


if __name__ == "__main__":
	main()





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


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

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch



class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase=14       ,     _lowercase=7       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=True       ,     _lowercase=99       ,     _lowercase=32       ,     _lowercase=5       ,     _lowercase=4       ,     _lowercase=37       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=512       ,     _lowercase=0.0_2       ,     )      ->				Dict:
									a_		:		List[str]				    =				parent
									a_		:		Tuple				    =				batch_size
									a_		:		int				    =				seq_length
									a_		:		Tuple				    =				is_training
									a_		:		List[Any]				    =				use_input_mask
									a_		:		Dict				    =				use_token_type_ids
									a_		:		List[Any]				    =				use_labels
									a_		:		Optional[int]				    =				vocab_size
									a_		:		Any				    =				hidden_size
									a_		:		List[Any]				    =				num_hidden_layers
									a_		:		List[str]				    =				num_attention_heads
									a_		:		List[str]				    =				intermediate_size
									a_		:		List[str]				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		Tuple				    =				attention_probs_dropout_prob
									a_		:		Union[str, Any]				    =				max_position_embeddings
									a_		:		Tuple				    =				initializer_range
									a_		:		Any				    =				None
									a_		:		str				    =				vocab_size - 1
									a_		:		Optional[Any]				    =				vocab_size - 1
									a_		:		List[Any]				    =				vocab_size - 1





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Dict				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size  )

									a_		:		List[str]				    =				None
									if self.use_input_mask:
												a_		:		Dict				    =				random_attention_mask([self.batch_size, self.seq_length]  )

									a_		:		Optional[int]				    =				GPTaConfig(
									    vocab_size=self.vocab_size       ,     n_embd=self.hidden_size       ,     n_layer=self.num_hidden_layers       ,     n_head=self.num_attention_heads       ,     n_positions=self.max_position_embeddings       ,     use_cache=_lowercase       ,     bos_token_id=self.bos_token_id       ,     eos_token_id=self.eos_token_id       ,     pad_token_id=self.pad_token_id       ,     )

									return (config, input_ids, input_mask)





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Any				    =				self.prepare_config_and_inputs()
									a_    ,			a_    ,			a_		:		List[str]				    =				config_and_inputs
									a_		:		Optional[Any]				    =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_    ,			a_    ,			a_		:		Dict				    =				self.prepare_config_and_inputs()

									a_		:		List[str]				    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a_		:		Optional[int]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2  )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Optional[Any]:
									a_		:		Dict				    =				20
									a_		:		Tuple				    =				model_class_name(_lowercase  )

									a_		:		Tuple				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype="""i4"""  )

									a_		:		str				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )
									a_		:		int				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		int				    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""  )
									a_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     attention_mask=_lowercase       ,     past_key_values=outputs_cache.past_key_values       ,     position_ids=_lowercase       ,     )

									a_		:		Dict				    =				model(_lowercase  )

									a_		:		Tuple				    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3       ,     msg=F'''Max diff is {diff}'''  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				int:
									a_		:		Any				    =				20
									a_		:		Optional[int]				    =				model_class_name(_lowercase  )

									a_		:		Any				    =				jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]       ,     axis=-1       ,     )

									a_		:		Optional[Any]				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )

									a_		:		Optional[Any]				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )
									a_		:		List[str]				    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""  )
									a_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		str				    =				model(_lowercase       ,     attention_mask=_lowercase  )

									a_		:		List[str]				    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3       ,     msg=F'''Max diff is {diff}'''  )







@require_flax
class    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							List[str]   			=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_A  :							Dict   			=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		List[str]				    =				FlaxGPTaModelTester(self  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Any				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Optional[Any]				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		str				    =				GPTaTokenizer.from_pretrained("""gpt2"""       ,     pad_token="""</s>"""       ,     padding_side="""left"""  )
									a_		:		Optional[int]				    =				tokenizer(["""Hello this is a long string""", """Hey"""]       ,     return_tensors="""np"""       ,     padding=_lowercase       ,     truncation=_lowercase  )

									a_		:		Any				    =				FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""  )
									a_		:		List[Any]				    =				False
									a_		:		List[Any]				    =				model.config.eos_token_id

									a_		:		Tuple				    =				jax.jit(model.generate  )

									a_		:		Union[str, Any]				    =				jit_generate(inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]  ).sequences

									a_		:		Dict				    =				tokenizer.batch_decode(_lowercase       ,     skip_special_tokens=_lowercase  )

									a_		:		Tuple				    =				[
									    """Hello this is a long string of words. I'm going to start with the first one.\n""",
									    """Hey, I'm not sure if I'm going to be able to do""",
									]

									self.assertListEqual(_lowercase       ,     _lowercase  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_    ,			a_		:		Optional[int]				    =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															# prepare inputs
															a_		:		List[Any]				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		List[str]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		List[str]				    =				getattr(_lowercase       ,     _lowercase  )

															a_    ,			a_		:		Any				    =				pt_inputs["""input_ids"""].shape
															a_		:		Any				    =				np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)  )
															for batch_idx, start_index in enumerate(_lowercase  ):
																		a_		:		Dict				    =				0
																		a_		:		int				    =				1
																		a_		:		int				    =				0
																		a_		:		Optional[Any]				    =				1
															a_		:		Optional[int]				    =				pt_model_class(_lowercase  ).eval()
															a_		:		int				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

															a_		:		Optional[int]				    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,     _lowercase  )
															a_		:		Union[str, Any]				    =				fx_state

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		List[str]				    =				fx_model(**_lowercase  ).to_tuple()
															self.assertEqual(len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )

															with tempfile.TemporaryDirectory() as tmpdirname:
																		pt_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				model_class.from_pretrained(_lowercase       ,     from_pt=_lowercase  )

															a_		:		int				    =				fx_model_loaded(**_lowercase  ).to_tuple()
															self.assertEqual(
															    len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output_loaded, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output_loaded[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_    ,			a_		:		str				    =				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															# prepare inputs
															a_		:		int				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		Optional[Any]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		Union[str, Any]				    =				getattr(_lowercase       ,     _lowercase  )

															a_		:		Tuple				    =				pt_model_class(_lowercase  ).eval()
															a_		:		Dict				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

															a_		:		Optional[Any]				    =				load_flax_weights_in_pytorch_model(_lowercase       ,     fx_model.params  )
															a_    ,			a_		:		Dict				    =				pt_inputs["""input_ids"""].shape
															a_		:		Any				    =				np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)  )
															for batch_idx, start_index in enumerate(_lowercase  ):
																		a_		:		Optional[int]				    =				0
																		a_		:		Dict				    =				1
																		a_		:		Union[str, Any]				    =				0
																		a_		:		Tuple				    =				1

															# make sure weights are tied in PyTorch
															pt_model.tie_weights()

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		str				    =				fx_model(**_lowercase  ).to_tuple()
															self.assertEqual(len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )

															with tempfile.TemporaryDirectory() as tmpdirname:
																		fx_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				pt_model_class.from_pretrained(_lowercase       ,     from_flax=_lowercase  )

															with torch.no_grad():
																		a_		:		List[str]				    =				pt_model_loaded(**_lowercase  ).to_tuple()

															self.assertEqual(
															    len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Any:
									for model_class_name in self.all_model_classes:
												a_		:		List[str]				    =				model_class_name.from_pretrained("""gpt2"""       ,     from_pt=_lowercase  )
												a_		:		List[str]				    =				model(np.ones((1, 1)  )  )
												self.assertIsNotNone(_lowercase  )


