



import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


a			:     Optional[int]     = logging.get_logger(__name__)  # pylint: disable=invalid-name


a			:     int     = 'lfs-multipart-upload'







class 							_a							(      _lowerCAmelCase				):

			@staticmethod
			def     __snake_case					(SCREAMING_SNAKE_CASE_				)					->      Optional[int]:
						UpperCAmelCase_:		Any       			=    parser.add_parser(
						    """lfs-enable-largefiles""",      help=(
						        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
						    ),      )
						enable_parser.add_argument("""path""",      type=SCREAMING_SNAKE_CASE_,      help="""Local path to repository you want to configure."""				)
						enable_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE_				: LfsEnableCommand(SCREAMING_SNAKE_CASE_				)				)

						UpperCAmelCase_:		Union[str, Any]       			=    parser.add_parser(
						    SCREAMING_SNAKE_CASE_,      help=(
						        """Deprecated: use `huggingface-cli` instead. """
						        """Command will get called by git-lfs, do not call it directly."""
						    ),      )
						upload_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE_				: LfsUploadCommand(SCREAMING_SNAKE_CASE_				)				)







class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						UpperCAmelCase_:		Optional[Any]       			=    args

			def     __snake_case					(self				)					->      Any:
						warnings.warn(
						    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""				)
						UpperCAmelCase_:		Union[str, Any]       			=    os.path.abspath(self.args.path				)
						if not os.path.isdir(SCREAMING_SNAKE_CASE_				):
									print("""This does not look like a valid git repo."""				)
									exit(1				)
						subprocess.run(
						    """git config lfs.customtransfer.multipart.path transformers-cli""".split(),      check=SCREAMING_SNAKE_CASE_,      cwd=SCREAMING_SNAKE_CASE_				)
						subprocess.run(
						    f'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split(),      check=SCREAMING_SNAKE_CASE_,      cwd=SCREAMING_SNAKE_CASE_,      )
						print("""Local repo set up for largefiles"""				)







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]       			=    json.dumps(lowerCAmelCase__     ) + """\n"""
			sys.stdout.write(lowerCAmelCase__     )
			sys.stdout.flush()







def    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Dict       			=    json.loads(sys.stdin.readline().strip()     )

			if "terminate" in (msg.get("""type"""     ), msg.get("""event"""     )):
						# terminate message received
						return None

			if msg.get("""event"""     ) not in ("download", "upload"):
						logger.critical("""Received unexpected message"""     )
						sys.exit(1     )

			return msg







class 							_a							(      _lowerCAmelCase				):

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		str       			=    filepath
						UpperCAmelCase_:		Union[str, Any]       			=    seek_from
						UpperCAmelCase_:		str       			=    read_limit
						UpperCAmelCase_:		List[Any]       			=    0

			def __enter__(self				)					->      int:
						UpperCAmelCase_:		Tuple       			=    open(self.filepath,      """rb"""				)
						self.f.seek(self.seek_from				)
						return self

			def __len__(self				)					->      List[Any]:
						UpperCAmelCase_:		str       			=    os.fstat(self.f.fileno()				).st_size
						return min(self.read_limit,      total_length - self.seek_from				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_=-1				)					->      Optional[int]:
						if self.n_seen >= self.read_limit:
									return b""
						UpperCAmelCase_:		List[str]       			=    self.read_limit - self.n_seen
						UpperCAmelCase_:		int       			=    self.f.read(remaining_amount if n < 0 else min(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)				)
						self.n_seen += len(SCREAMING_SNAKE_CASE_				)
						return data

			def __iter__(self				)					->      Dict:
						yield self.read(n=4 * 1024 * 1024				)

			def __exit__(self,      *SCREAMING_SNAKE_CASE_				)					->      Dict:
						self.f.close()







class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      Optional[int]:
						UpperCAmelCase_:		Any       			=    args

			def     __snake_case					(self				)					->      Union[str, Any]:
						# Immediately after invoking a custom transfer process, git-lfs
						# sends initiation data to the process over stdin.
						# This tells the process useful information about the configuration.
						UpperCAmelCase_:		Union[str, Any]       			=    json.loads(sys.stdin.readline().strip()				)
						if not (init_msg.get("""event"""				) == "init" and init_msg.get("""operation"""				) == "upload"):
									write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}				)
									sys.exit(1				)

						# The transfer process should use the information it needs from the
						# initiation structure, and also perform any one-off setup tasks it
						# needs to do. It should then respond on stdout with a simple empty
						# confirmation structure, as follows:
						write_msg({}				)

						# After the initiation exchange, git-lfs will send any number of
						# transfer requests to the stdin of the transfer process, in a serial sequence.
						while True:
									UpperCAmelCase_:		Dict       			=    read_msg()
									if msg is None:
												# When all transfers have been processed, git-lfs will send
												# a terminate event to the stdin of the transfer process.
												# On receiving this message the transfer process should
												# clean up and terminate. No response is expected.
												sys.exit(0				)

									UpperCAmelCase_:		Optional[int]       			=    msg["""oid"""]
									UpperCAmelCase_:		Optional[Any]       			=    msg["""path"""]
									UpperCAmelCase_:		Dict       			=    msg["""action"""]["""href"""]
									UpperCAmelCase_:		Union[str, Any]       			=    msg["""action"""]["""header"""]
									UpperCAmelCase_:		Union[str, Any]       			=    int(header.pop("""chunk_size"""				)				)
									UpperCAmelCase_:		List[str]       			=    list(header.values()				)

									UpperCAmelCase_:		int       			=    []
									for i, presigned_url in enumerate(SCREAMING_SNAKE_CASE_				):
												with FileSlice(SCREAMING_SNAKE_CASE_,      seek_from=i * chunk_size,      read_limit=SCREAMING_SNAKE_CASE_				) as data:
															UpperCAmelCase_:		Union[str, Any]       			=    requests.put(SCREAMING_SNAKE_CASE_,      data=SCREAMING_SNAKE_CASE_				)
															r.raise_for_status()
															parts.append(
															    {
															        """etag""": r.headers.get("""etag"""				),
															        """partNumber""": i + 1,
															    }				)
															# In order to support progress reporting while data is uploading / downloading,
															# the transfer process should post messages to stdout
															write_msg(
															    {
															        """event""": """progress""",
															        """oid""": oid,
															        """bytesSoFar""": (i + 1) * chunk_size,
															        """bytesSinceLast""": chunk_size,
															    }				)
															# Not precise but that's ok.

									UpperCAmelCase_:		List[str]       			=    requests.post(
									    SCREAMING_SNAKE_CASE_,      json={
									        """oid""": oid,
									        """parts""": parts,
									    },      )
									r.raise_for_status()

									write_msg({"""event""": """complete""", """oid""": oid}				)











import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


a			:     List[str]     = logging.getLogger(__name__)







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			random.seed(lowerCAmelCase__     )
			np.random.seed(lowerCAmelCase__     )
			torch.manual_seed(lowerCAmelCase__     )
			torch.cuda.manual_seed_all(lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			model.eval()
			UpperCAmelCase_:		Optional[Any]       			=    next(model.parameters()     ).device
			UpperCAmelCase_:		Tuple       			=    1
			UpperCAmelCase_:		Any       			=    torch.zeros((eval_batch_size, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    torch.zeros(1		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    0
			for batch in eval_dataloader:
						batch.to(lowerCAmelCase__     )
						# pad
						context.zero_()
						for i in range(lowerCAmelCase__     ):
									UpperCAmelCase_:		Optional[int]       			=    batch[i]
						UpperCAmelCase_:		Union[str, Any]       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0     )
			UpperCAmelCase_:		Tuple       			=    eval_loss / nb_eval_examples
			UpperCAmelCase_:		List[Any]       			=    torch.exp(lowerCAmelCase__     )
			model.train()
			return perplexity







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]="gpt2"     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__		,	output_hidden_states=lowerCAmelCase__     )
			torch.save(model.state_dict()		,	model_name + """local.pt"""     )
			return model







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			model.to(lowerCAmelCase__     )

			UpperCAmelCase_:		Dict       			=    ["""bias""", """LayerNorm.weight"""]
			UpperCAmelCase_:		Optional[int]       			=    [
			    {
			        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
			        """weight_decay""": 0.0,
			    },
			    {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )], """weight_decay""": 0.0},
			]
			UpperCAmelCase_:		str       			=    AdamW(lowerCAmelCase__		,	lr=5e-5		,	eps=1e-8     )
			UpperCAmelCase_:		Tuple       			=    get_linear_schedule_with_warmup(lowerCAmelCase__		,	0		,	lowerCAmelCase__     )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		str       			=    np.array(lowerCAmelCase__     ).mean()
			UpperCAmelCase_:		Optional[Any]       			=    np.array(lowerCAmelCase__     ).std()
			UpperCAmelCase_:		Tuple       			=    (real_perps - avg) / std
			UpperCAmelCase_:		Tuple       			=    list(zip(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )     )
			joblib.dump(lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Optional[Any]="dev.jbl"		,	lowerCAmelCase__:		Tuple=recopy_gpta		,	):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []

			# Initialize the transformer model
			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			orig_model.to(device="""cpu"""     )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

			for step in tqdm(range(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Union[str, Any]       			=    torch.zeros((1, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
						UpperCAmelCase_:		str       			=    random.choice(lowerCAmelCase__     )
						UpperCAmelCase_:		int       			=    random.randint(0		,	len(story[0]     ) - context_len - 1     )
						UpperCAmelCase_:		Optional[int]       			=    story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCAmelCase_:		Dict       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						UpperCAmelCase_:		List[str]       			=    outputs[0]
						UpperCAmelCase_:		Optional[Any]       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()		,	3.0     )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						UpperCAmelCase_:		int       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_0_0_0 == 0 and step > 1:
									intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						past_perps.append(past_perp.item()     )
						real_perps.append(orig_perp - real_perp.item()     )
						contexts.append(np.array(context.cpu()     )     )

			intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Any="data/tokenized_stories_train_wikitext103.jbl"		,	lowerCAmelCase__:		Any=1_0_0		,	lowerCAmelCase__:		Optional[int]=1_0_2_6		,	lowerCAmelCase__:		Optional[int]=True     ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    joblib.load(lowerCAmelCase__     )
			print("""data loaded"""     )
			UpperCAmelCase_:		List[Any]       			=    []
			if trim:
						for i, example in enumerate(lowerCAmelCase__     ):
									if len(example[0]     ) > min_len:
												UpperCAmelCase_:		Optional[int]       			=    random.randint(0		,	len(example[0]     ) - context_len - 1     )
												objective_set.append(example[0, start : start + context_len]     )
									if len(lowerCAmelCase__     ) >= number:
												break
						UpperCAmelCase_:		Union[str, Any]       			=    []
						for j in range(i + 1		,	len(lowerCAmelCase__     )     ):
									if len(data[j][0]     ) > min_len:
												train_data.append(data[j]     )
			else:
						UpperCAmelCase_:		Optional[int]       			=    data[0:number]
						UpperCAmelCase_:		Tuple       			=    data[number:]

			joblib.dump(lowerCAmelCase__		,	"""objective_set.jbl"""     )
			print("""objective set saved"""     )
			return train_data, objective_set







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		int=5_0		,	lowerCAmelCase__:		List[Any]="secondary_learner.pt"     ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple       			=    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			UpperCAmelCase_:		int       			=    train_dataset[:5_1_2]
			UpperCAmelCase_:		Any       			=    train_dataset[5_1_2:]
			UpperCAmelCase_:		Union[str, Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )

			# secondary learner model set up
			UpperCAmelCase_:		Optional[int]       			=    nn.MSELoss()
			UpperCAmelCase_:		str       			=    nn.MSELoss(reduction="""sum"""     )
			secondary_learner.to(lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    torch.optim.Adam(secondary_learner.parameters()		,	lr=0.00001     )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			UpperCAmelCase_:		Any       			=    float("""inf"""     )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Optional[Any]       			=    0.0
						secondary_learner.train()
						for step, batch in enumerate(lowerCAmelCase__     ):
									UpperCAmelCase_:		Union[str, Any]       			=    batch[0].to(lowerCAmelCase__     )
									UpperCAmelCase_:		List[Any]       			=    batch[1].to(lowerCAmelCase__     )
									UpperCAmelCase_:		Tuple       			=    secondary_learner(lowerCAmelCase__     )
									q_optimizer.zero_grad()
									UpperCAmelCase_:		Any       			=    loss(lowerCAmelCase__		,	real_q.float()     )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCAmelCase__     )):
												UpperCAmelCase_:		int       			=    tr_q_loss / (step + 1)

												secondary_learner.eval()
												UpperCAmelCase_:		Optional[Any]       			=    0.0
												UpperCAmelCase_:		Union[str, Any]       			=    0.0
												UpperCAmelCase_:		Any       			=    []
												UpperCAmelCase_:		Any       			=    []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(lowerCAmelCase__     ):
															UpperCAmelCase_:		Optional[Any]       			=    batcha[0].to(lowerCAmelCase__     )
															UpperCAmelCase_:		int       			=    batcha[1].to(lowerCAmelCase__     )
															UpperCAmelCase_:		Dict       			=    secondary_learner(lowerCAmelCase__     )
															q_lossa += test_loss(lowerCAmelCase__		,	lowerCAmelCase__     ).item()
															sum_qa += torch.sum(lowerCAmelCase__     ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy()     ):
																		predicted.append(i.item()     )
															for ei, i in enumerate(real_qa.cpu().detach().numpy()     ):
																		actual.append(i.item()     )

												q_lossa /= len(lowerCAmelCase__     )
												print(
												    """Epoch: """		,	lowerCAmelCase__		,	"""step: """		,	lowerCAmelCase__		,	"""Avg. q:"""		,	sum_qa / len(lowerCAmelCase__     )		,	"""Train Loss: """		,	lowerCAmelCase__		,	"""Test Loss: """		,	lowerCAmelCase__		,	)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)		,	"""pred_vs_actual.jbl"""     )
															torch.save(secondary_learner.state_dict()		,	lowerCAmelCase__     )
															UpperCAmelCase_:		str       			=    q_lossa

									secondary_learner.train()
			return secondary_learner







class 							_a							(      nn.Module				):

			def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						super(SCREAMING_SNAKE_CASE_,      self				).__init__()
						UpperCAmelCase_:		List[str]       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						UpperCAmelCase_:		List[str]       			=    nn.Convad(self.embeddings.weight.size(1				),      256,      3,      padding=1				)
						UpperCAmelCase_:		Union[str, Any]       			=    nn.Sequential(nn.Linear(256,      32				),      nn.Dropout(p=0.1				),      nn.Linear(32,      32				),      nn.Linear(32,      1				)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		Dict       			=    torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_				).squeeze(1				).transpose(1,      2				)				),      2				)[0]
						UpperCAmelCase_:		Dict       			=    self.fc(SCREAMING_SNAKE_CASE_				)
						return qs.squeeze(1				)

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		Optional[int]       			=    cls(SCREAMING_SNAKE_CASE_				)  # this calls __init__
						UpperCAmelCase_:		Any       			=    torch.load(SCREAMING_SNAKE_CASE_				)
						secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						return secondary_learner






