





'''simple docstring'''







import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
_a      :				Optional[int]											=					logging.getLogger(__name__)

_a      :				int											=					int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

_a      :				List[str]											=					{
    """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
    """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
    """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
    """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
    """gptj""": (GPTJForCausalLM, AutoTokenizer),
    """bloom""": (BloomForCausalLM, BloomTokenizerFast),
    """llama""": (LlamaForCausalLM, LlamaTokenizer),
    """opt""": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_a      :				List[Any]											=					"""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>"""





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			np.random.seed(args.seed			)
			torch.manual_seed(args.seed			)
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed			)





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Tuple:
			if args.temperature > 0.7:
						logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""			)

			__lowerCAmelCase      		=       tokenizer.encode(lowercase      ,			add_special_tokens=lowercase			)
			if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()			):
						logger.info("""WARNING! You are not starting your generation from a control code so you won't get good results"""			)
			return prompt_text





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
			# kwargs = {"language": None, "mask_token_id": None}

			# Set the language
			__lowerCAmelCase      		=       hasattr(model.config      ,			"""use_lang_emb"""			) and model.config.use_lang_emb
			if hasattr(model.config      ,			"""lang2id"""			) and use_lang_emb:
						__lowerCAmelCase      		=       model.config.langaid.keys()
						if args.xlm_language in available_languages:
									__lowerCAmelCase      		=       args.xlm_language
						else:
									__lowerCAmelCase      		=       None
									while language not in available_languages:
												__lowerCAmelCase      		=       input("""Using XLM. Select language in """ + str(list(lowercase			)			) + """ >>> """			)

						__lowerCAmelCase      		=       model.config.langaid[language]
						# kwargs["language"] = tokenizer.lang2id[language]

			# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
			# XLM masked-language modeling (MLM) models need masked token
			# is_xlm_mlm = "mlm" in args.model_name_or_path
			# if is_xlm_mlm:
			#     kwargs["mask_token_id"] = tokenizer.mask_token_id

			return prompt_text





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Dict:
			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			__lowerCAmelCase      		=       prefix + prompt_text
			return prompt_text





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			__lowerCAmelCase      		=       prefix + prompt_text
			return prompt_text


_a      :				List[Any]											=					{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Union[str, Any]:
			if length < 0 and max_sequence_length > 0:
						__lowerCAmelCase      		=       max_sequence_length
			elif 0 < max_sequence_length < length:
						__lowerCAmelCase      		=       max_sequence_length  # No generation bigger than model size
			elif length < 0:
						__lowerCAmelCase      		=       MAX_LENGTH  # avoid infinite loop
			return length





def      _lowerCAmelCase		(		lowercase			)      ->      Dict:
			__lowerCAmelCase      		=       None
			if hasattr(lowercase      ,			"""hidden_size"""			):
						__lowerCAmelCase      		=       model_config.hidden_size
			elif hasattr(lowercase      ,			"""n_embed"""			):
						__lowerCAmelCase      		=       model_config.n_embed
			elif hasattr(lowercase      ,			"""n_embd"""			):
						__lowerCAmelCase      		=       model_config.n_embd

			__lowerCAmelCase      		=       None
			if hasattr(lowercase      ,			"""num_attention_heads"""			):
						__lowerCAmelCase      		=       model_config.num_attention_heads
			elif hasattr(lowercase      ,			"""n_head"""			):
						__lowerCAmelCase      		=       model_config.n_head

			if embedding_size is None or num_head is None or num_head == 0:
						raise ValueError("""Check the model config"""			)

			__lowerCAmelCase      		=       int(embedding_size / num_head			)
			if hasattr(lowercase      ,			"""n_layer"""			):
						__lowerCAmelCase      		=       model_config.n_layer
			elif hasattr(lowercase      ,			"""num_hidden_layers"""			):
						__lowerCAmelCase      		=       model_config.num_hidden_layers
			else:
						raise ValueError("""Number of hidden layers couldn't be determined from the model config"""			)

			return num_layer, num_head, num_embedding_size_per_head





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Optional[Any]:
			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       sparse_model_config(model.config			)
			if model.config.model_type == "bloom":
						__lowerCAmelCase      		=       tuple(
						    (
						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						    )
						    for _ in range(lowercase			)			)
			else:
						__lowerCAmelCase      		=       tuple(
						    (
						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						    )
						    for _ in range(lowercase			)			)
			return past_key_values





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       len(lowercase			)
			__lowerCAmelCase      		=       tokenizer.batch_encode_plus(lowercase      ,			return_tensors="""pt"""			)
			__lowerCAmelCase      		=       dummy_input.to(model.device			)
			if model.config.use_cache:
						__lowerCAmelCase      		=       generate_past_key_values(lowercase      ,			lowercase      ,			1			)
			__lowerCAmelCase      		=       torch.cat(
			    [
			        torch.zeros(dummy_input["""attention_mask"""].shape[0]      ,			1			)
			        .to(dummy_input["""attention_mask"""].dtype			)
			        .to(model.device			),
			        dummy_input["""attention_mask"""],
			    ]      ,			-1      ,			)
			return dummy_input





class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		a     :     Optional[Any]     						=("""_optimized""", """_default""")
		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       optimized
					__lowerCAmelCase      		=       default
		def __call__(						self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					if kwargs["past_key_values"] is None and self._default.config.use_cache:
								__lowerCAmelCase      		=       generate_past_key_values(self._default,kwargs["""input_ids"""].shape[0],0    )
					kwargs.pop("""position_ids""",__SCREAMING_SNAKE_CASE    )
					for k in list(kwargs.keys()    ):
								if kwargs[k] is None or isinstance(kwargs[k],__SCREAMING_SNAKE_CASE    ):
											kwargs.pop(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self._optimized(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       outputs[0]
					__lowerCAmelCase      		=       outputs[1]
					__lowerCAmelCase      		=       CausalLMOutputWithPast(
					    loss=__SCREAMING_SNAKE_CASE,logits=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,hidden_states=__SCREAMING_SNAKE_CASE,attentions=__SCREAMING_SNAKE_CASE,)
					return fixed_output
		def __getattr__(						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return getattr(self._default,__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return self._default.prepare_inputs_for_generation(
					    __SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,inputs_embeds=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return self._default._reorder_cache(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )





def      _lowerCAmelCase		(		)      ->      int:
			__lowerCAmelCase      		=       argparse.ArgumentParser()
			parser.add_argument(
			    """--model_type"""      ,			default=lowercase      ,			type=lowercase      ,			required=lowercase      ,			help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()			)      ,			)
			parser.add_argument(
			    """--model_name_or_path"""      ,			default=lowercase      ,			type=lowercase      ,			required=lowercase      ,			help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()			)      ,			)

			parser.add_argument("""--prompt"""      ,			type=lowercase      ,			default=""""""			)
			parser.add_argument("""--length"""      ,			type=lowercase      ,			default=20			)
			parser.add_argument("""--stop_token"""      ,			type=lowercase      ,			default=lowercase      ,			help="""Token at which text generation is stopped"""			)

			parser.add_argument(
			    """--temperature"""      ,			type=lowercase      ,			default=1.0      ,			help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""      ,			)
			parser.add_argument(
			    """--repetition_penalty"""      ,			type=lowercase      ,			default=1.0      ,			help="""primarily useful for CTRL model; in that case, use 1.2"""			)
			parser.add_argument("""--k"""      ,			type=lowercase      ,			default=0			)
			parser.add_argument("""--p"""      ,			type=lowercase      ,			default=0.9			)

			parser.add_argument("""--prefix"""      ,			type=lowercase      ,			default=""""""      ,			help="""Text added prior to input."""			)
			parser.add_argument("""--padding_text"""      ,			type=lowercase      ,			default=""""""      ,			help="""Deprecated, the use of `--prefix` is preferred."""			)
			parser.add_argument("""--xlm_language"""      ,			type=lowercase      ,			default=""""""      ,			help="""Optional language when used with the XLM model."""			)

			parser.add_argument("""--seed"""      ,			type=lowercase      ,			default=42      ,			help="""random seed for initialization"""			)
			parser.add_argument("""--no_cuda"""      ,			action="""store_true"""      ,			help="""Avoid using CUDA when available"""			)
			parser.add_argument("""--num_return_sequences"""      ,			type=lowercase      ,			default=1      ,			help="""The number of samples to generate."""			)
			parser.add_argument(
			    """--fp16"""      ,			action="""store_true"""      ,			help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,			)
			parser.add_argument("""--jit"""      ,			action="""store_true"""      ,			help="""Whether or not to use jit trace to accelerate inference"""			)
			__lowerCAmelCase      		=       parser.parse_args()

			__lowerCAmelCase      		=       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
			__lowerCAmelCase      		=       0 if args.no_cuda else torch.cuda.device_count()

			logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'			)

			set_seed(lowercase			)

			# Initialize the model and tokenizer
			try:
						__lowerCAmelCase      		=       args.model_type.lower()
						__lowerCAmelCase		, __lowerCAmelCase      		=       MODEL_CLASSES[args.model_type]
			except KeyError:
						raise KeyError("""the model {} you specified is not supported. You are welcome to add it and open a PR :)"""			)

			__lowerCAmelCase      		=       tokenizer_class.from_pretrained(args.model_name_or_path			)
			if tokenizer.pad_token is None:
						__lowerCAmelCase      		=       tokenizer.eos_token
			__lowerCAmelCase      		=       model_class.from_pretrained(args.model_name_or_path			)
			model.to(args.device			)

			if args.fpaa:
						model.half()
			__lowerCAmelCase      		=       getattr(model.config      ,			"""max_position_embeddings"""      ,			0			)
			__lowerCAmelCase      		=       adjust_length_to_model(args.length      ,			max_sequence_length=lowercase			)
			logger.info(lowercase			)

			__lowerCAmelCase      		=       args.prompt if args.prompt else input("""Model prompt >>> """			)

			# Different models need different input formatting and/or extra arguments
			__lowerCAmelCase      		=       args.model_type in PREPROCESSING_FUNCTIONS.keys()
			if requires_preprocessing:
						__lowerCAmelCase      		=       PREPROCESSING_FUNCTIONS.get(args.model_type			)
						__lowerCAmelCase      		=       prepare_input(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)

						if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
									__lowerCAmelCase      		=       {"""add_space_before_punct_symbol""": True}
						else:
									__lowerCAmelCase      		=       {}

						__lowerCAmelCase      		=       tokenizer.encode(
						    lowercase      ,			add_special_tokens=lowercase      ,			return_tensors="""pt"""      ,			**lowercase			)
			else:
						__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text
						__lowerCAmelCase      		=       tokenizer.encode(prefix + prompt_text      ,			add_special_tokens=lowercase      ,			return_tensors="""pt"""			)
			__lowerCAmelCase      		=       encoded_prompt.to(args.device			)

			if encoded_prompt.size()[-1] == 0:
						__lowerCAmelCase      		=       None
			else:
						__lowerCAmelCase      		=       encoded_prompt

			if args.jit:
						__lowerCAmelCase      		=       ["""enable jit"""]
						__lowerCAmelCase      		=       prepare_jit_inputs(lowercase      ,			lowercase      ,			lowercase			)
						torch._C._jit_set_texpr_fuser_enabled(lowercase			)
						__lowerCAmelCase      		=       False
						if hasattr(lowercase      ,			"""forward"""			):
									__lowerCAmelCase      		=       inspect.signature(model.forward			)
						else:
									__lowerCAmelCase      		=       inspect.signature(model.__call__			)
						__lowerCAmelCase      		=       tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowercase      ,			lowercase			) is not None			)
						__lowerCAmelCase      		=       torch.jit.trace(lowercase      ,			lowercase      ,			strict=lowercase			)
						__lowerCAmelCase      		=       torch.jit.freeze(traced_model.eval()			)
						traced_model(*lowercase			)
						traced_model(*lowercase			)

						__lowerCAmelCase      		=       _ModelFallbackWrapper(lowercase      ,			lowercase			)

			__lowerCAmelCase      		=       model.generate(
			    input_ids=lowercase      ,			max_length=args.length + len(encoded_prompt[0]			)      ,			temperature=args.temperature      ,			top_k=args.k      ,			top_p=args.p      ,			repetition_penalty=args.repetition_penalty      ,			do_sample=lowercase      ,			num_return_sequences=args.num_return_sequences      ,			)

			# Remove the batch dimension when returning multiple sequences
			if len(output_sequences.shape			) > 2:
						output_sequences.squeeze_()

			__lowerCAmelCase      		=       []

			for generated_sequence_idx, generated_sequence in enumerate(lowercase			):
						print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='			)
						__lowerCAmelCase      		=       generated_sequence.tolist()

						# Decode text
						__lowerCAmelCase      		=       tokenizer.decode(lowercase      ,			clean_up_tokenization_spaces=lowercase			)

						# Remove all text after the stop token
						__lowerCAmelCase      		=       text[: text.find(args.stop_token			) if args.stop_token else None]

						# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
						__lowerCAmelCase      		=       (
						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      ,			clean_up_tokenization_spaces=lowercase			)			) :]
						)

						generated_sequences.append(lowercase			)
						print(lowercase			)

			return generated_sequences


if __name__ == "__main__":
	main()








'''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


_a      :				Optional[Any]											=					torch.cuda.is_available()
_a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a      :				Optional[Any]											=					{
    """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""": 4_2,
    """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""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """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      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       """\n""".join(lowercase			)
			Path(lowercase			).open("""w"""			).writelines(lowercase			)





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
			return tmp_dir







class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		@classmethod
		def 						lowerCamelCase__    (						cls    ):



					'''simple docstring'''

					return cls
		@require_torch_multi_gpu
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """no_teacher""": True,
					    """freeze_encoder""": True,
					    """gpus""": 2,
					    """overwrite_output_dir""": True,
					    """sortish_sampler""": True,
					}
					self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE,check_contents=__SCREAMING_SNAKE_CASE    )







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """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(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       CHEAP_ARGS.copy()
					__lowerCAmelCase      		=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					__lowerCAmelCase      		=       self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=__SCREAMING_SNAKE_CASE,output_dir=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					def convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
								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}'

					__lowerCAmelCase      		=       [x for x in (convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE    )]
					__lowerCAmelCase      		=       [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
					execute_subprocess_async(__SCREAMING_SNAKE_CASE,env=self.get_env()    )

					__lowerCAmelCase      		=       os.listdir(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       {os.path.basename(__SCREAMING_SNAKE_CASE    ) for p in contents}
					__lowerCAmelCase      		=       [p for p in contents if p.endswith("""ckpt"""    )]
					assert len(__SCREAMING_SNAKE_CASE    ) > 0

					self.assertIn("""test_generations.txt""",__SCREAMING_SNAKE_CASE    )
					self.assertIn("""test_results.txt""",__SCREAMING_SNAKE_CASE    )

					# get the following from the module, (we don't have access to `model` here)
					__lowerCAmelCase      		=       os.path.join(__SCREAMING_SNAKE_CASE,"""metrics.json"""    )
					__lowerCAmelCase      		=       """rouge2"""

					__lowerCAmelCase      		=       load_json(__SCREAMING_SNAKE_CASE    )
					# {'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(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       metrics["""val"""][-1]
					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""],0.01    )
					self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],__SCREAMING_SNAKE_CASE    )
					self.assertEqual(len(metrics["""test"""]    ),1    )
					__lowerCAmelCase      		=       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1    )
					self.assertEqual(len(metrics["""val"""]    ),__SCREAMING_SNAKE_CASE    )

