
'''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  							= torch.cuda.is_available()
__lowerCAmelCase  							= [' Sam ate lunch today.', 'Sams lunch ingredients.']
__lowerCAmelCase  							= ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__lowerCAmelCase  							= {
    '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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						"""\n""".join(_SCREAMING_SNAKE_CASE   )
							Path(_SCREAMING_SNAKE_CASE   ).open("""w"""   ).writelines(_SCREAMING_SNAKE_CASE   )






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


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							@classmethod
							def  lowercase							(cls						)  ->     str:
														return cls

							@require_torch_multi_gpu
							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						{
														    """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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=True						)  ->     Dict:
														_snake_case     =						{
														    """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						)
														_snake_case     =						CHEAP_ARGS.copy()
														_snake_case     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
														_snake_case     =						self.get_auto_remove_tmp_dir()
														args_d.update(data_dir=UpperCAmelCase						,						output_dir=UpperCAmelCase						,						**UpperCAmelCase						)

														def convert(UpperCAmelCase						,						UpperCAmelCase						):
																					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}"""

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

														_snake_case     =						os.listdir(UpperCAmelCase						)
														_snake_case     =						{os.path.basename(UpperCAmelCase						) for p in contents}
														_snake_case     =						[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)
														_snake_case     =						os.path.join(UpperCAmelCase						,						"""metrics.json"""						)
														_snake_case     =						"""rouge2"""

														_snake_case     =						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						)
														_snake_case     =						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						)
														_snake_case     =						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1						)
														self.assertEqual(len(metrics["""val"""]						)						,						UpperCAmelCase						)

'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		"m2m_100"
							lowerCAmelCase_      		=		["past_key_values"]
							lowerCAmelCase_      		=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(self						,						UpperCAmelCase=128112						,						UpperCAmelCase=1024						,						UpperCAmelCase=12						,						UpperCAmelCase=4096						,						UpperCAmelCase=16						,						UpperCAmelCase=12						,						UpperCAmelCase=4096						,						UpperCAmelCase=16						,						UpperCAmelCase=0.05						,						UpperCAmelCase=0.05						,						UpperCAmelCase=True						,						UpperCAmelCase=True						,						UpperCAmelCase="relu"						,						UpperCAmelCase=1024						,						UpperCAmelCase=0.1						,						UpperCAmelCase=0.1						,						UpperCAmelCase=0.0						,						UpperCAmelCase=0.02						,						UpperCAmelCase=2						,						UpperCAmelCase=True						,						UpperCAmelCase=1						,						UpperCAmelCase=0						,						UpperCAmelCase=2						,						**UpperCAmelCase						,						)  ->     int:
														_snake_case     =						vocab_size
														_snake_case     =						max_position_embeddings
														_snake_case     =						d_model
														_snake_case     =						encoder_ffn_dim
														_snake_case     =						encoder_layers
														_snake_case     =						encoder_attention_heads
														_snake_case     =						decoder_ffn_dim
														_snake_case     =						decoder_layers
														_snake_case     =						decoder_attention_heads
														_snake_case     =						dropout
														_snake_case     =						attention_dropout
														_snake_case     =						activation_dropout
														_snake_case     =						activation_function
														_snake_case     =						init_std
														_snake_case     =						encoder_layerdrop
														_snake_case     =						decoder_layerdrop
														_snake_case     =						use_cache
														_snake_case     =						encoder_layers
														_snake_case     =						scale_embedding  # scale factor will be sqrt(d_model) if True

														super().__init__(
														    pad_token_id=UpperCAmelCase						,						bos_token_id=UpperCAmelCase						,						eos_token_id=UpperCAmelCase						,						is_encoder_decoder=UpperCAmelCase						,						decoder_start_token_id=UpperCAmelCase						,						**UpperCAmelCase						,						)


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							@property
							def  lowercase							(self						)  ->     Mapping[str, Mapping[int, str]]:
														_snake_case     =						OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]						)

														if self.use_past:
																					_snake_case     =						{0: """batch"""}
																					_snake_case     =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																					_snake_case     =						{0: """batch""", 1: """decoder_sequence"""}
																					_snake_case     =						{0: """batch""", 1: """decoder_sequence"""}

														if self.use_past:
																					self.fill_with_past_key_values_(UpperCAmelCase						,						direction="""inputs"""						)
														return common_inputs

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = -1						,						UpperCAmelCase = -1						,						UpperCAmelCase = False						,						UpperCAmelCase = None						,						)  ->     Mapping[str, Any]:
														# Copied from OnnxConfig.generate_dummy_inputs
														# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														_snake_case     =						compute_effective_axis_dimension(
														    UpperCAmelCase						,						fixed_dimension=OnnxConfig.default_fixed_batch						,						num_token_to_add=0						)

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														_snake_case     =						tokenizer.num_special_tokens_to_add(UpperCAmelCase						)
														_snake_case     =						compute_effective_axis_dimension(
														    UpperCAmelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=UpperCAmelCase						)

														# Generate dummy inputs according to compute batch and sequence
														_snake_case     =						[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
														_snake_case     =						dict(tokenizer(UpperCAmelCase						,						return_tensors=UpperCAmelCase						)						)
														return common_inputs

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = -1						,						UpperCAmelCase = -1						,						UpperCAmelCase = False						,						UpperCAmelCase = None						,						)  ->     Mapping[str, Any]:
														_snake_case     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)

														# Generate decoder inputs
														_snake_case     =						seq_length if not self.use_past else 1
														_snake_case     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)
														_snake_case     =						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														_snake_case     =						dict(**UpperCAmelCase						,						**UpperCAmelCase						)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																					else:
																												import torch
																					_snake_case,							_snake_case     =						common_inputs["""input_ids"""].shape
																					_snake_case     =						common_inputs["""decoder_input_ids"""].shape[1]
																					_snake_case,							_snake_case     =						self.num_attention_heads
																					_snake_case     =						(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					_snake_case     =						decoder_seq_length + 3
																					_snake_case     =						(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					_snake_case     =						torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase						,						UpperCAmelCase						)]						,						dim=1						)

																					_snake_case     =						[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					_snake_case,							_snake_case     =						self.num_layers
																					_snake_case     =						min(UpperCAmelCase						,						UpperCAmelCase						)
																					_snake_case     =						max(UpperCAmelCase						,						UpperCAmelCase						) - min_num_layers
																					_snake_case     =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																					for _ in range(UpperCAmelCase						):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												    )						)
																					# TODO: test this.
																					_snake_case     =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																					for _ in range(UpperCAmelCase						,						UpperCAmelCase						):
																												common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase						), torch.zeros(UpperCAmelCase						))						)
														return common_inputs





							lowerCAmelCase_      		=		_generate_dummy_inputs_for_default_and_seqaseq_lm