





'''simple docstring'''







from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFTransfoXLForSequenceClassification,
					    TFTransfoXLLMHeadModel,
					    TFTransfoXLModel,
					)
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					Union[str, Any]					,				):
								_a								= parent
								_a								= 13
								_a								= 7
								_a								= 30
								_a								= self.seq_length + self.mem_len
								_a								= 15
								_a								= True
								_a								= True
								_a								= 99
								_a								= [10, 50, 80]
								_a								= 32
								_a								= 32
								_a								= 4
								_a								= 8
								_a								= 1_28
								_a								= 2
								_a								= 2
								_a								= None
								_a								= 1
								_a								= 0
								_a								= 3
								_a								= self.vocab_size - 1
								_a								= 0.01




				def   UpperCamelCase__   (  self     :					int					):
								_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)
								_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)

								_a								= None
								if self.use_labels:
												_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)

								_a								= TransfoXLConfig(
								    vocab_size=self.vocab_size					,				mem_len=self.mem_len					,				clamp_len=self.clamp_len					,				cutoffs=self.cutoffs					,				d_model=self.hidden_size					,				d_embed=self.d_embed					,				n_head=self.num_attention_heads					,				d_head=self.d_head					,				d_inner=self.d_inner					,				div_val=self.div_val					,				n_layer=self.num_hidden_layers					,				eos_token_id=self.eos_token_id					,				pad_token_id=self.vocab_size - 1					,				init_range=self.init_range					,				num_labels=self.num_labels					,				)

								return (config, input_ids_a, input_ids_a, lm_labels)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								random.seed(self.seed					)
								tf.random.set_seed(self.seed					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					List[Any]					,				__a     :					Tuple					,				__a     :					str					,				__a     :					int					):
								_a								= TFTransfoXLModel(__a					)

								_a	,					_a								= model(__a					).to_tuple()

								_a								= {"input_ids": input_ids_a, "mems": mems_a}

								_a	,					_a								= model(__a					).to_tuple()

								self.parent.assertEqual(hidden_states_a.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)
								self.parent.assertEqual(hidden_states_a.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					List[str]					,				__a     :					Union[str, Any]					):
								_a								= TFTransfoXLLMHeadModel(__a					)

								_a	,					_a								= model(__a					).to_tuple()

								_a								= {"input_ids": input_ids_a, "labels": lm_labels}
								_a	,					_a								= model(__a					).to_tuple()

								_a	,					_a								= model([input_ids_a, mems_a]					).to_tuple()

								_a								= {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

								_a	,					_a								= model(__a					).to_tuple()

								self.parent.assertEqual(lm_logits_a.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)

								self.parent.assertEqual(lm_logits_a.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					List[Any]					,				__a     :					List[str]					,				__a     :					Any					,				__a     :					str					):
								_a								= TFTransfoXLForSequenceClassification(__a					)
								_a								= model(__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)					)




				def   UpperCamelCase__   (  self     :					str					):
								_a								= self.prepare_config_and_inputs()
								((_a)	,					(_a)	,					(_a)	,					(_a))								= config_and_inputs
								_a								= {"input_ids": input_ids_a}
								return config, inputs_dict


@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				__a            =() if is_tf_available() else ()
				__a            =(
				    {
				        'feature-extraction': TFTransfoXLModel,
				        'text-classification': TFTransfoXLForSequenceClassification,
				        'text-generation': TFTransfoXLLMHeadModel,
				        'zero-shot': TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				__a            =False
				__a            =False
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Union[str, Any]					,				__a     :					List[str]					,				__a     :					Tuple					,				__a     :					Optional[Any]					,				__a     :					Tuple					):
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
												# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
												# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
												# tokenizer.
												return True

								return False




				def   UpperCamelCase__   (  self     :					str					):
								_a								= TFTransfoXLModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				d_embed=37					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								self.config_tester.run_common_tests()




				def   UpperCamelCase__   (  self     :					Any					):
								self.model_tester.set_seed()
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*__a					)




				def   UpperCamelCase__   (  self     :					str					):
								self.model_tester.set_seed()
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()
								_a								= [TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												assert isinstance(model.get_input_embeddings()					,				tf.keras.layers.Layer					)
												if model_class in list_other_models_with_output_ebd:
																_a								= model.get_output_embeddings()
																assert isinstance(__a					,				tf.keras.layers.Layer					)
																_a								= model.get_bias()
																assert name is None
												else:
																_a								= model.get_output_embeddings()
																assert x is None
																_a								= model.get_bias()
																assert name is None




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								# TODO JP: Make TransfoXL XLA compliant
								pass




				@slow
				def   UpperCamelCase__   (  self     :					Tuple					):
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_a								= TFTransfoXLModel.from_pretrained(__a					)
												self.assertIsNotNone(__a					)




				@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."					)
				def   UpperCamelCase__   (  self     :					Tuple					):
								pass


@require_tf
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@unittest.skip("Skip test until #12651 is resolved."					)
				@slow
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"					)
								# fmt: off
								_a								= tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]					,				dtype=tf.intaa					)  # noqa: E231
								# fmt: on
								#  In 1991 , the remains of Russian Tsar Nicholas II and his family
								#  ( except for Alexei and Maria ) are discovered .
								#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
								#  remainder of the story . 1883 Western Siberia ,
								#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
								#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
								#  father initially slaps him for making such an accusation , Rasputin watches as the
								#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
								#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
								#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

								# fmt: off
								_a								= [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
								# fmt: on
								#  In 1991, the remains of Russian Tsar Nicholas II and his family (
								#  except for Alexei and Maria ) are discovered. The voice of young son,
								#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
								#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
								#  and a group of men to perform magic. Rasputin has a vision and
								#  denounces one of the men as a horse thief. Although his father initially
								#  slaps him for making such an accusation, Rasputin watches as the man
								#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
								#  of the Virgin Mary, prompting him to become a priest.
								#  Rasputin quickly becomes famous, with people, even a bishop, begging for
								#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
								# Nicholas II and his family were discovered. The voice of <unk> young son,
								# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

								_a								= model.generate(__a					,				max_length=2_00					,				do_sample=__a					)
								self.assertListEqual(output_ids[0].numpy().tolist()					,				__a					)







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
