




"""simple docstring"""

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowerCamelCase	:      Optional[Any]   =							torch.cuda.is_available()
_lowerCamelCase	:      Tuple   =							[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_lowerCamelCase	:      Any   =							["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_lowerCamelCase	:      Optional[int]   =							{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		'\n'.join(_UpperCAmelCase						)
	Path(_UpperCAmelCase						).open('''w'''						).writelines(_UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_UpperCAmelCase	,   f"""{split}.source"""						)	,   _UpperCAmelCase						)
		_dump_articles(os.path.join(_UpperCAmelCase	,   f"""{split}.target"""						)	,   _UpperCAmelCase						)
	return tmp_dir

class 							lowercase   (     a_):







				@classmethod
				def        a_	(				cls :       str							):



					"""simple docstring"""

					return cls







				@require_torch_multi_gpu
				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					List[Any]						 =		{
					    'no_teacher': True,
					    'freeze_encoder': True,
					    'gpus': 2,
					    'overwrite_output_dir': True,
					    'sortish_sampler': True,
					}
					self._test_distiller_cli_fork(UpperCAmelCase_   ,  check_contents=UpperCAmelCase_							)







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       Any=True							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		{
					    'label_smoothing': 0.0,
					    'early_stopping_patience': -1,
					    'train_batch_size': 1,
					    'eval_batch_size': 2,
					    'max_epochs': 2,
					    'alpha_mlm': 0.2,
					    'alpha_ce': 0.8,
					    'do_predict': True,
					    'model_name_or_path': 'sshleifer/tinier_bart',
					    'teacher': CHEAP_ARGS['model_name_or_path'],
					    'val_check_interval': 0.5,
					}
					default_updates.update(UpperCAmelCase_							)
					A_	:					dict						 =		CHEAP_ARGS.copy()
					A_	:					List[str]						 =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
					A_	:					Any						 =		self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=UpperCAmelCase_   ,  output_dir=UpperCAmelCase_   ,  **UpperCAmelCase_							)

					def convert(_lowerCamelCase :       int   ,  _lowerCamelCase :       Any							):
						if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
							return ""
						if v is False or v is None:
							return ""
						if v is True:  # or len(str(v))==0:
							return F"""--{k}"""
						return F"""--{k}={v}"""

					A_	:					Any						 =		[x for x in (convert(UpperCAmelCase_   ,  UpperCAmelCase_							) for k, v in args_d.items()) if len(UpperCAmelCase_							)]
					A_	:					str						 =		[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
					execute_subprocess_async(UpperCAmelCase_   ,  env=self.get_env()							)

					A_	:					Optional[int]						 =		os.listdir(UpperCAmelCase_							)
					A_	:					Dict						 =		{os.path.basename(UpperCAmelCase_							) for p in contents}
					A_	:					Tuple						 =		[p for p in contents if p.endswith('''ckpt'''							)]
					assert len(UpperCAmelCase_							) > 0

					self.assertIn('''test_generations.txt'''   ,  UpperCAmelCase_							)
					self.assertIn('''test_results.txt'''   ,  UpperCAmelCase_							)

					# get the following from the module, (we don't have access to `model` here)
					A_	:					Dict						 =		os.path.join(UpperCAmelCase_   ,  '''metrics.json'''							)
					A_	:					List[Any]						 =		'rouge2'

					A_	:					Dict						 =		load_json(UpperCAmelCase_							)
					# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
					print(UpperCAmelCase_							)
					A_	:					List[Any]						 =		metrics['val'][-1]
					self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']   ,  0.01							)
					self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]   ,  UpperCAmelCase_							)
					self.assertEqual(len(metrics['''test''']							)   ,  1							)
					A_	:					List[str]						 =		int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1							)
					self.assertEqual(len(metrics['''val''']							)   ,  UpperCAmelCase_							)






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    def __init__(					self     :						Dict							,							UpperCAmelCase_     :						List[str]							,							):


         """simple docstring"""







         a					:							Any							= parent
         a					:							Tuple							= 1_3
         a					:							Any							= 7
         a					:							Any							= True
         a					:							Optional[Any]							= True
         a					:							Optional[Any]							= True
         a					:							Dict							= True
         a					:							List[str]							= True
         a					:							Dict							= 9_9
         a					:							Tuple							= 3_2
         a					:							Optional[int]							= 2
         a					:							str							= 4
         a					:							str							= 3_7
         a					:							Union[str, Any]							= 'gelu'
         a					:							Any							= 0.1
         a					:							Any							= 0.1
         a					:							List[Any]							= 5_1_2
         a					:							Any							= 1_6
         a					:							int							= 2
         a					:							Dict							= 0.02
         a					:							Optional[int]							= 3
         a					:							Union[str, Any]							= 4
         a					:							int							= None
         a					:							Tuple							= self.vocab_size - 1





    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

         a					:							List[str]							= None
         if self.use_input_mask:
              a					:							Optional[Any]							= random_attention_mask([self.batch_size, self.seq_length])

         a					:							List[Any]							= None
         if self.use_token_type_ids:
              a					:							Any							= ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

         a					:							List[str]							= None
         if self.use_mc_token_ids:
              a					:							List[str]							= ids_tensor([self.batch_size, self.num_choices]							,							self.seq_length)

         a					:							str							= None
         a					:							Optional[Any]							= None
         a					:							Optional[int]							= None
         if self.use_labels:
              a					:							Tuple							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
              a					:							Any							= ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels)
              a					:							List[Any]							= ids_tensor([self.batch_size]							,							self.num_choices)

         a					:							Dict							= CTRLConfig(
             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							,							pad_token_id=self.pad_token_id							,							)

         a					:							Tuple							= ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,							2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						str							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Union[str, Any]							= TFCTRLModel(config=UpperCAmelCase_)
         a					:							Dict							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							Tuple							= model(UpperCAmelCase_)

         a					:							str							= [input_ids, None, input_mask]  # None is the input for 'past'
         a					:							Any							= model(UpperCAmelCase_)

         a					:							List[str]							= model(UpperCAmelCase_)

         self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Dict							,							*UpperCAmelCase_     :						str):


         """simple docstring"""







         a					:							Optional[int]							= TFCTRLLMHeadModel(config=UpperCAmelCase_)
         a					:							Tuple							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							int							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Tuple							= self.num_labels
         a					:							List[str]							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
         a					:							int							= {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'labels': sequence_labels,
         }
         a					:							str							= TFCTRLForSequenceClassification(UpperCAmelCase_)
         a					:							Union[str, Any]							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))







    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= self.prepare_config_and_inputs()

         (
             (
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			
         )					:							int							= config_and_inputs

         a					:							Tuple							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_tf
class   UpperCamelCase					(       a_    ,     a_    ,     unittest.TestCase       ):







    """simple docstring"""





    A     :    str				=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
    A     :    Optional[int]				=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
    A     :    str				=			(
        {
            "feature-extraction": TFCTRLModel,
            "text-classification": TFCTRLForSequenceClassification,
            "text-generation": TFCTRLLMHeadModel,
            "zero-shot": TFCTRLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    A     :    int				=			False
    A     :    Optional[Any]				=			False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Any							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]):


         """simple docstring"""







         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
              # config could not be created.
              return True

         return False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Any							= TFCTRLModelTester(self)
         a					:							Dict							= ConfigTester(self							,							config_class=UpperCAmelCase_							,							n_embd=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         self.config_tester.run_common_tests()





    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							List[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							str							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a							,			a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs_for_common()
         a					:							int							= [TFCTRLLMHeadModel]
         a					:							int							= [TFCTRLForSequenceClassification]

         for model_class in self.all_model_classes:
              a					:							int							= model_class(UpperCAmelCase_)
              model.build()  # may be needed for the get_bias() call below
              assert isinstance(model.get_input_embeddings()							,							tf.keras.layers.Layer)

              if model_class in list_lm_models:
                   a					:							Optional[Any]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							int							= model.get_bias()
                   assert isinstance(UpperCAmelCase_							,							UpperCAmelCase_)
                   for k, v in name.items():
                        assert isinstance(UpperCAmelCase_							,							tf.Variable)
              elif model_class in list_other_models_with_output_ebd:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							List[Any]							= model.get_bias()
                   assert name is None
              else:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert x is None
                   a					:							Optional[Any]							= model.get_bias()
                   assert name is None







    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a					:							Dict							= TFCTRLModel.from_pretrained(UpperCAmelCase_)
              self.assertIsNotNone(UpperCAmelCase_)






@require_tf
class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							List[Any]							= TFCTRLLMHeadModel.from_pretrained('ctrl')
         a					:							Union[str, Any]							= tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]							,							dtype=tf.intaa)  # Legal the president is
         a					:							List[Any]							= [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         a					:							List[str]							= model.generate(UpperCAmelCase_							,							do_sample=UpperCAmelCase_)
         self.assertListEqual(output_ids[0].numpy().tolist()							,							UpperCAmelCase_)

