
'''simple docstring'''
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch


@require_flax
class        SCREAMING_SNAKE_CASE__	:







				def __init__(    self		,							A_		,							A_=14		,							A_=7		,							A_=True		,							A_=True		,							A_=True		,							A_=99		,							A_=32		,							A_=5		,							A_=4		,							A_=37		,							A_="gelu"		,							A_=0.1		,							A_=0.1		,							A_=512		,							A_=0.02		,							A_=None		,							)->						str:







											'''simple docstring'''






											UpperCamelCase					       =			parent
											UpperCamelCase					       =			batch_size
											UpperCamelCase					       =			seq_length
											UpperCamelCase					       =			is_training
											UpperCamelCase					       =			use_input_mask
											UpperCamelCase					       =			use_labels
											UpperCamelCase					       =			vocab_size
											UpperCamelCase					       =			d_model
											UpperCamelCase					       =			num_hidden_layers
											UpperCamelCase					       =			num_attention_heads
											UpperCamelCase					       =			ffn_dim
											UpperCamelCase					       =			activation_function
											UpperCamelCase					       =			activation_dropout
											UpperCamelCase					       =			attention_dropout
											UpperCamelCase					       =			max_position_embeddings
											UpperCamelCase					       =			initializer_range
											UpperCamelCase					       =			None
											UpperCamelCase					       =			0
											UpperCamelCase					       =			2
											UpperCamelCase					       =			1







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			np.clip(ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size			)		,							3		,							self.vocab_size			)

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

											UpperCamelCase					       =			XGLMConfig(
											    vocab_size=self.vocab_size		,							d_model=self.hidden_size		,							num_layers=self.num_hidden_layers		,							attention_heads=self.num_attention_heads		,							ffn_dim=self.ffn_dim		,							activation_function=self.activation_function		,							activation_dropout=self.activation_dropout		,							attention_dropout=self.attention_dropout		,							max_position_embeddings=self.max_position_embeddings		,							initializer_range=self.initializer_range		,							use_cache=A_		,							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			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			self.prepare_config_and_inputs()
											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			config_and_inputs
											UpperCamelCase					       =			{'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.prepare_config_and_inputs()

											UpperCamelCase					       =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
											UpperCamelCase					       =			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		,							A_		,							A_		,							A_		,							A_			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			20
											UpperCamelCase					       =			model_class_name(A_			)

											UpperCamelCase					       =			model.init_cache(input_ids.shape[0]		,							A_			)
											UpperCamelCase					       =			jnp.ones((input_ids.shape[0], max_decoder_length)		,							dtype='i4'			)

											UpperCamelCase					       =			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,							(input_ids.shape[0], input_ids.shape[-1] - 1)			)
											UpperCamelCase					       =			model(
											    input_ids[:, :-1]		,							attention_mask=A_		,							past_key_values=A_		,							position_ids=A_		,							)

											UpperCamelCase					       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,							dtype='i4'			)
											UpperCamelCase					       =			model(
											    input_ids[:, -1:]		,							attention_mask=A_		,							past_key_values=outputs_cache.past_key_values		,							position_ids=A_		,							)

											UpperCamelCase					       =			model(A_			)

											UpperCamelCase					       =			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		,							A_		,							A_		,							A_		,							A_			)->						Tuple:







											'''simple docstring'''






											UpperCamelCase					       =			20
											UpperCamelCase					       =			model_class_name(A_			)

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

											UpperCamelCase					       =			model.init_cache(input_ids.shape[0]		,							A_			)
											UpperCamelCase					       =			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,							(input_ids.shape[0], input_ids.shape[-1] - 1)			)

											UpperCamelCase					       =			model(
											    input_ids[:, :-1]		,							attention_mask=A_		,							past_key_values=A_		,							position_ids=A_		,							)
											UpperCamelCase					       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,							dtype='i4'			)
											UpperCamelCase					       =			model(
											    input_ids[:, -1:]		,							past_key_values=outputs_cache.past_key_values		,							attention_mask=A_		,							position_ids=A_		,							)

											UpperCamelCase					       =			model(A_		,							attention_mask=A_			)
											UpperCamelCase					       =			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_sentencepiece
@require_flax
class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    snake_case_				,    unittest.TestCase):
				lowerCAmelCase_			       =				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				lowerCAmelCase_			       =				(FlaxXGLMForCausalLM,) if is_flax_available() else ()







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			FlaxXGLMModelTester(self			)







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(A_		,							A_		,							A_		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    A_		,							A_		,							A_		,							A_			)







				@slow
				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			XGLMTokenizer.from_pretrained('XGLM'		,							padding_side='left'			)
											UpperCamelCase					       =			tokenizer(['Hello this is a long string', 'Hey']		,							return_tensors='np'		,							padding=A_		,							truncation=A_			)

											UpperCamelCase					       =			FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'			)
											UpperCamelCase					       =			1
											UpperCamelCase					       =			False

											UpperCamelCase					       =			jax.jit(model.generate			)

											UpperCamelCase					       =			jit_generate(inputs['input_ids']		,							attention_mask=inputs['attention_mask']			).sequences

											UpperCamelCase					       =			tokenizer.batch_decode(A_		,							skip_special_tokens=A_			)

											UpperCamelCase					       =			[
											    'Hello this is a long string of questions, but I\'m not sure if I\'m',
											    'Hey, I\'m a newbie to the forum and I\'',
											]

											self.assertListEqual(A_		,							A_			)







				@is_pt_flax_cross_test
				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			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
																									UpperCamelCase					       =			self._prepare_for_class(A_		,							A_			)
																									UpperCamelCase					       =			{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									UpperCamelCase					       =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									UpperCamelCase					       =			getattr(A_		,							A_			)

																									UpperCamelCase       ,     UpperCamelCase					       =			pt_inputs['input_ids'].shape
																									UpperCamelCase					       =			np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)			)
																									for batch_idx, start_index in enumerate(A_			):
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1
																									UpperCamelCase					       =			pt_model_class(A_			).eval()
																									# Flax models don't use the `use_cache` option and cache is not returned as a default.
																									# So we disable `use_cache` here for PyTorch model.
																									UpperCamelCase					       =			False
																									UpperCamelCase					       =			model_class(A_		,							dtype=jnp.floataa			)
																									UpperCamelCase					       =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,							A_			)
																									UpperCamelCase					       =			fx_state

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model(**A_			).to_tuple()

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

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																pt_model.save_pretrained(A_			)
																																UpperCamelCase					       =			model_class.from_pretrained(A_		,							from_pt=A_			)

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







				@is_pt_flax_cross_test
				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			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
																									UpperCamelCase					       =			self._prepare_for_class(A_		,							A_			)
																									UpperCamelCase					       =			{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									UpperCamelCase					       =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									UpperCamelCase					       =			getattr(A_		,							A_			)

																									UpperCamelCase					       =			pt_model_class(A_			).eval()
																									UpperCamelCase					       =			False
																									UpperCamelCase					       =			model_class(A_		,							dtype=jnp.floataa			)

																									UpperCamelCase					       =			load_flax_weights_in_pytorch_model(A_		,							fx_model.params			)
																									UpperCamelCase       ,     UpperCamelCase					       =			pt_inputs['input_ids'].shape
																									UpperCamelCase					       =			np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)			)
																									for batch_idx, start_index in enumerate(A_			):
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1

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

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model(**A_			).to_tuple()

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

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																fx_model.save_pretrained(A_			)
																																UpperCamelCase					       =			pt_model_class.from_pretrained(A_		,							from_flax=A_			)

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model_loaded(**A_			).to_tuple()

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







				@slow
				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase					       =			model_class_name.from_pretrained('facebook/xglm-564M'			)
																		UpperCamelCase					       =			model(np.ones((1, 1)			)			)
																		self.assertIsNotNone(A_			)






'''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()




