"""simple docstring"""







import collections
import importlib.util
import os
import re
from pathlib import Path


_a     :     List[Any]= '''src/transformers'''


# Matches is_xxx_available()
_a     :     Union[str, Any]= re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_a     :     Optional[Any]= re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_a     :     int= re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
_a     :     Optional[int]= re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_a     :     str= re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_a     :     Optional[int]= re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
_a     :     Tuple= re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
_a     :     Optional[int]= re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_a     :     str= re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_a     :     Tuple= re.compile(R"^\s*try:")
# Catches a line with else:
_a     :     Tuple= re.compile(R"^\s*else:")
def   __UpperCAmelCase				(    UpperCAmelCase_    :				Tuple  )							->			Union[str, Any]:

	'''simple docstring'''





	if _re_test_backend.search(A_  ) is None:
		return None
	__snake_case			:							Optional[Any]      		= [b[0] for b in _re_backend.findall(A_  )]
	backends.sort()
	return "_and_".join(A_  )
def   __UpperCAmelCase				(    UpperCAmelCase_    :				str  )							->			str:

	'''simple docstring'''





	with open(A_			,							'r'			,							encoding='utf-8'			,							newline='\n'  ) as f:
		__snake_case			:							Union[str, Any]      		= f.readlines()

	__snake_case			:							Optional[int]      		= 0
	while line_index < len(A_  ) and not lines[line_index].startswith('_import_structure = {'  ):
		line_index += 1

	# If this is a traditional init, just return.
	if line_index >= len(A_  ):
		return None

	# First grab the objects without a specific backend in _import_structure
	__snake_case			:							Optional[int]      		= []
	while not lines[line_index].startswith('if TYPE_CHECKING'  ) and find_backend(lines[line_index]  ) is None:
		__snake_case			:							Any      		= lines[line_index]
		# If we have everything on a single line, let's deal with it.
		if _re_one_line_import_struct.search(A_  ):
			__snake_case			:							Tuple      		= _re_one_line_import_struct.search(A_  ).groups()[0]
			__snake_case			:							int      		= re.findall('\[([^\]]+)\]'			,							A_  )
			for imp in imports:
				objects.extend([obj[1:-1] for obj in imp.split(', '  )]  )
			line_index += 1
			continue
		__snake_case			:							Dict      		= _re_import_struct_key_value.search(A_  )
		if single_line_import_search is not None:
			__snake_case			:							Union[str, Any]      		= [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '  ) if len(A_  ) > 0]
			objects.extend(A_  )
		elif line.startswith(' ' * 8 + '"'  ):
			objects.append(line[9:-3]  )
		line_index += 1

	__snake_case			:							Dict      		= {'''none''': objects}
	# Let's continue with backend-specific objects in _import_structure
	while not lines[line_index].startswith('if TYPE_CHECKING'  ):
		# If the line is an if not is_backend_available, we grab all objects associated.
		__snake_case			:							Optional[Any]      		= find_backend(lines[line_index]  )
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]  ) is None:
			__snake_case			:							List[str]      		= None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]  ) is None:
				line_index += 1

			line_index += 1

			__snake_case			:							Optional[Any]      		= []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(' ' * 4  ):
				__snake_case			:							List[str]      		= lines[line_index]
				if _re_import_struct_add_one.search(A_  ) is not None:
					objects.append(_re_import_struct_add_one.search(A_  ).groups()[0]  )
				elif _re_import_struct_add_many.search(A_  ) is not None:
					__snake_case			:							Dict      		= _re_import_struct_add_many.search(A_  ).groups()[0].split(', '  )
					__snake_case			:							List[str]      		= [obj[1:-1] for obj in imports if len(A_  ) > 0]
					objects.extend(A_  )
				elif _re_between_brackets.search(A_  ) is not None:
					__snake_case			:							Optional[Any]      		= _re_between_brackets.search(A_  ).groups()[0].split(', '  )
					__snake_case			:							str      		= [obj[1:-1] for obj in imports if len(A_  ) > 0]
					objects.extend(A_  )
				elif _re_quote_object.search(A_  ) is not None:
					objects.append(_re_quote_object.search(A_  ).groups()[0]  )
				elif line.startswith(' ' * 8 + '"'  ):
					objects.append(line[9:-3]  )
				elif line.startswith(' ' * 12 + '"'  ):
					objects.append(line[13:-3]  )
				line_index += 1

			__snake_case			:							int      		= objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	__snake_case			:							Optional[Any]      		= []
	while (
	    line_index < len(A_  )
	    and find_backend(lines[line_index]  ) is None
	    and not lines[line_index].startswith('else'  )
	):
		__snake_case			:							str      		= lines[line_index]
		__snake_case			:							List[Any]      		= _re_import.search(A_  )
		if single_line_import_search is not None:
			objects.extend(single_line_import_search.groups()[0].split(', '  )  )
		elif line.startswith(' ' * 8  ):
			objects.append(line[8:-2]  )
		line_index += 1

	__snake_case			:							Tuple      		= {'''none''': objects}
	# Let's continue with backend-specific objects
	while line_index < len(A_  ):
		# If the line is an if is_backend_available, we grab all objects associated.
		__snake_case			:							List[Any]      		= find_backend(lines[line_index]  )
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]  ) is None:
			__snake_case			:							Any      		= None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]  ) is None:
				line_index += 1

			line_index += 1

			__snake_case			:							Optional[Any]      		= []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(' ' * 8  ):
				__snake_case			:							str      		= lines[line_index]
				__snake_case			:							str      		= _re_import.search(A_  )
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(', '  )  )
				elif line.startswith(' ' * 12  ):
					objects.append(line[12:-2]  )
				line_index += 1

			__snake_case			:							List[Any]      		= objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects
def   __UpperCAmelCase				(    UpperCAmelCase_    :				Optional[int]			,							UpperCAmelCase_    :				Optional[Any]  )							->			Optional[int]:

	'''simple docstring'''





	def find_duplicates(UpperCAmelCase_    :				Union[str, Any]  ):
		return [k for k, v in collections.Counter(A_  ).items() if v > 1]

	if list(import_dict_objects.keys()  ) != list(type_hint_objects.keys()  ):
		return ["Both sides of the init do not have the same backends!"]

	__snake_case			:							str      		= []
	for key in import_dict_objects.keys():
		__snake_case			:							Any      		= find_duplicates(import_dict_objects[key]  )
		if duplicate_imports:
			errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"  )
		__snake_case			:							List[Any]      		= find_duplicates(type_hint_objects[key]  )
		if duplicate_type_hints:
			errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"  )

		if sorted(set(import_dict_objects[key]  )  ) != sorted(set(type_hint_objects[key]  )  ):
			__snake_case			:							Tuple      		= '''base imports''' if key == '''none''' else F"{key} backend"
			errors.append(F"Differences for {name}:"  )
			for a in type_hint_objects[key]:
				if a not in import_dict_objects[key]:
					errors.append(F"  {a} in TYPE_HINT but not in _import_structure."  )
			for a in import_dict_objects[key]:
				if a not in type_hint_objects[key]:
					errors.append(F"  {a} in _import_structure but not in TYPE_HINT."  )
	return errors
def   __UpperCAmelCase				(    )							->			List[str]:

	'''simple docstring'''





	__snake_case			:							Optional[int]      		= []
	for root, _, files in os.walk(A_  ):
		if "__init__.py" in files:
			__snake_case			:							str      		= os.path.join(A_			,							'__init__.py'  )
			__snake_case			:							Optional[int]      		= parse_init(A_  )
			if objects is not None:
				__snake_case			:							Optional[int]      		= analyze_results(*A_  )
				if len(A_  ) > 0:
					__snake_case			:							Dict      		= F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
					failures.append('\n'.join(A_  )  )
	if len(A_  ) > 0:
		raise ValueError('\n\n'.join(A_  )  )
def   __UpperCAmelCase				(    )							->			Union[str, Any]:

	'''simple docstring'''





	__snake_case			:							Optional[Any]      		= []
	for path, directories, files in os.walk(A_  ):
		for folder in directories:
			# Ignore private modules
			if folder.startswith('_'  ):
				directories.remove(A_  )
				continue
			# Ignore leftovers from branches (empty folders apart from pycache)
			if len(list((Path(A_  ) / folder).glob('*.py'  )  )  ) == 0:
				continue
			__snake_case			:							Any      		= str((Path(A_  ) / folder).relative_to(A_  )  )
			__snake_case			:							Optional[int]      		= short_path.replace(os.path.sep			,							'.'  )
			submodules.append(A_  )
		for fname in files:
			if fname == "__init__.py":
				continue
			__snake_case			:							Any      		= str((Path(A_  ) / fname).relative_to(A_  )  )
			__snake_case			:							str      		= short_path.replace('.py'			,							''  ).replace(os.path.sep			,							'.'  )
			if len(submodule.split('.'  )  ) == 1:
				submodules.append(A_  )
	return submodules


_a     :     str= [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]
def   __UpperCAmelCase				(    )							->			str:

	'''simple docstring'''





	__snake_case			:							Union[str, Any]      		= importlib.util.spec_from_file_location(
	    'transformers'			,							os.path.join(A_			,							'__init__.py'  )			,							submodule_search_locations=[PATH_TO_TRANSFORMERS]			,							)
	__snake_case			:							Any      		= spec.loader.load_module()

	__snake_case			:							List[str]      		= [
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
	]
	if len(A_  ) > 0:
		__snake_case			:							List[str]      		= '''\n'''.join(F"- {module}" for module in module_not_registered  )
		raise ValueError(
		    'The following submodules are not properly registered in the main init of Transformers:\n'
		    F"{list_of_modules}\n"
		    'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'  )


if __name__ == "__main__":
	check_all_inits()
	check_submodules()








"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__UpperCamelCase  :      int							      =		logging.getLogger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# save results
	if os.path.exists(A_ ):
		if os.path.exists(os.path.join(A_						,     '''config.json''' ) ) and os.path.isfile(
		    os.path.join(A_						,     '''config.json''' ) ):
			os.remove(os.path.join(A_						,     '''config.json''' ) )
		if os.path.exists(os.path.join(A_						,     '''pytorch_model.bin''' ) ) and os.path.isfile(
		    os.path.join(A_						,     '''pytorch_model.bin''' ) ):
			os.remove(os.path.join(A_						,     '''pytorch_model.bin''' ) )
	else:
		os.makedirs(A_ )
	model.save_pretrained(A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						Optional[Any]  				=	2
	if unlogit:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.pow(A_						,     A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	p * torch.log(A_ )
	lowerCAmelCase__	:						List[Any]  				=	0
	return -plogp.sum(dim=-1 )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(A_ ) ) ) )
	for row in range(len(A_ ) ):
		if tensor.dtype != torch.long:
			logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
		else:
			logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data ) )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_=True						,     A_=True						,     A_=None						,     A_=False ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	model.config.num_hidden_layers, model.config.num_attention_heads
	lowerCAmelCase__	:						Dict  				=	torch.zeros(A_						,     A_ ).to(args.device )
	lowerCAmelCase__	:						int  				=	torch.zeros(A_						,     A_ ).to(args.device )

	if head_mask is None:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.ones(A_						,     A_ ).to(args.device )

	head_mask.requires_grad_(requires_grad=A_ )
	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
	if actually_pruned:
		lowerCAmelCase__	:						Union[str, Any]  				=	None

	lowerCAmelCase__	:						Optional[int]  				=	0.0
	lowerCAmelCase__	:						Optional[int]  				=	0.0
	for step, inputs in enumerate(tqdm(A_						,     desc='''Iteration'''						,     disable=args.local_rank not in [-1, 0] ) ):
		lowerCAmelCase__	:						Any  				=	tuple(t.to(args.device ) for t in inputs )
		((lowerCAmelCase__)				,)	:						List[Any]  				=	inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		lowerCAmelCase__	:						Any  				=	model(A_						,     labels=A_						,     head_mask=A_ )
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	(
		    outputs[0],
		    outputs[1],
		    outputs[-1],
		)  # Loss and logits are the first, attention the last
		loss.backward()  # Backpropagate to populate the gradients in the head mask
		total_loss += loss.detach().cpu().numpy()
		if compute_entropy:
			for layer, attn in enumerate(A_ ):
				lowerCAmelCase__	:						Dict  				=	entropy(attn.detach()						,     A_ )
				attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()

		if compute_importance:
			head_importance += head_mask.grad.abs().detach()
		tot_tokens += torch.ones_like(A_ ).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		lowerCAmelCase__	:						Any  				=	2
		lowerCAmelCase__	:						Dict  				=	torch.pow(torch.pow(A_						,     A_ ).sum(-1 )						,     1 / exponent )
		head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0

	if not args.dont_normalize_global_importance:
		lowerCAmelCase__	:						List[Any]  				=	(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

	# Print matrices
	if compute_entropy:
		logger.info('''Attention entropies''' )
		print_ad_tensor(A_ )
	if compute_importance:
		logger.info('''Head importance scores''' )
		print_ad_tensor(A_ )
	logger.info('''Head ranked by importance scores''' )
	lowerCAmelCase__	:						str  				=	torch.zeros(head_importance.numel()						,     dtype=torch.long						,     device=args.device )
	lowerCAmelCase__	:						Optional[int]  				=	torch.arange(
	    head_importance.numel()						,     device=args.device )
	lowerCAmelCase__	:						int  				=	head_ranks.view_as(A_ )
	print_ad_tensor(A_ )
	return attn_entropy, head_importance, total_loss

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	compute_heads_importance(A_						,     A_						,     A_						,     compute_entropy=A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	1 / loss  # instead of downsteam score use the LM loss
	logger.info('''Pruning: original score: %f, threshold: %f'''						,     A_						,     original_score * args.masking_threshold )

	lowerCAmelCase__	:						Union[str, Any]  				=	torch.ones_like(A_ )
	lowerCAmelCase__	:						List[str]  				=	max(1						,     int(new_head_mask.numel() * args.masking_amount ) )

	lowerCAmelCase__	:						int  				=	original_score
	while current_score >= original_score * args.masking_threshold:
		lowerCAmelCase__	:						Union[str, Any]  				=	new_head_mask.clone().detach()  # save current head mask
		# heads from least important to most - keep only not-masked heads
		lowerCAmelCase__	:						str  				=	float('''Inf''' )
		lowerCAmelCase__	:						List[Any]  				=	head_importance.view(-1 ).sort()[1]

		if len(A_ ) <= num_to_mask:
			print('''BREAK BY num_to_mask''' )
			break

		# mask heads
		lowerCAmelCase__	:						List[Any]  				=	current_heads_to_mask[:num_to_mask]
		logger.info('''Heads to mask: %s'''						,     str(current_heads_to_mask.tolist() ) )
		lowerCAmelCase__	:						int  				=	new_head_mask.view(-1 )
		lowerCAmelCase__	:						Optional[int]  				=	0.0
		lowerCAmelCase__	:						Union[str, Any]  				=	new_head_mask.view_as(A_ )
		lowerCAmelCase__	:						Tuple  				=	new_head_mask.clone().detach()
		print_ad_tensor(A_ )

		# Compute metric and head importance again
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	compute_heads_importance(
		    A_						,     A_						,     A_						,     compute_entropy=A_						,     head_mask=A_ )
		lowerCAmelCase__	:						Tuple  				=	1 / loss
		logger.info(
		    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''						,     A_						,     new_head_mask.sum()						,     new_head_mask.sum() / new_head_mask.numel() * 1_00						,     )

	logger.info('''Final head mask''' )
	print_ad_tensor(A_ )
	np.save(os.path.join(args.output_dir						,     '''head_mask.npy''' )						,     head_mask.detach().cpu().numpy() )

	return head_mask

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	1 / loss
	lowerCAmelCase__	:						Tuple  				=	datetime.now() - before_time

	lowerCAmelCase__	:						int  				=	sum(p.numel() for p in model.parameters() )
	lowerCAmelCase__	:						List[Any]  				=	{
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
	}

	for k, v in heads_to_prune.items():
		if isinstance(A_						,     A_ ):
			lowerCAmelCase__	:						int  				=	[
			    v,
			]

	assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
	model.prune_heads(A_ )
	lowerCAmelCase__	:						List[Any]  				=	sum(p.numel() for p in model.parameters() )

	lowerCAmelCase__	:						Any  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_						,     actually_pruned=A_						,     )

	lowerCAmelCase__	:						int  				=	1 / loss
	lowerCAmelCase__	:						Dict  				=	datetime.now() - before_time

	logger.info(
	    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''						,     A_						,     A_						,     pruned_num_params / original_num_params * 1_00						,     )
	logger.info('''Pruning: score with masking: %f score with pruning: %f'''						,     A_						,     A_ )
	logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''						,     original_time / new_time * 1_00 )
	save_model(A_						,     args.output_dir )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--data_dir'''						,     default=A_						,     type=A_						,     required=A_						,     help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''						,     )
	parser.add_argument(
	    '''--model_name_or_path'''						,     default=A_						,     type=A_						,     required=A_						,     help='''Path to pretrained model or model identifier from huggingface.co/models'''						,     )
	parser.add_argument(
	    '''--output_dir'''						,     default=A_						,     type=A_						,     required=A_						,     help='''The output directory where the model predictions and checkpoints will be written.'''						,     )

	# Other parameters
	parser.add_argument(
	    '''--config_name'''						,     default=''''''						,     type=A_						,     help='''Pretrained config name or path if not the same as model_name_or_path'''						,     )
	parser.add_argument(
	    '''--tokenizer_name'''						,     default=''''''						,     type=A_						,     help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''						,     )
	parser.add_argument(
	    '''--cache_dir'''						,     default=A_						,     type=A_						,     help='''Where do you want to store the pre-trained models downloaded from s3'''						,     )
	parser.add_argument(
	    '''--data_subset'''						,     type=A_						,     default=-1						,     help='''If > 0: limit the data to a subset of data_subset instances.''' )
	parser.add_argument(
	    '''--overwrite_output_dir'''						,     action='''store_true'''						,     help='''Whether to overwrite data in output directory''' )
	parser.add_argument(
	    '''--overwrite_cache'''						,     action='''store_true'''						,     help='''Overwrite the cached training and evaluation sets''' )

	parser.add_argument(
	    '''--dont_normalize_importance_by_layer'''						,     action='''store_true'''						,     help='''Don\'t normalize importance score by layers''' )
	parser.add_argument(
	    '''--dont_normalize_global_importance'''						,     action='''store_true'''						,     help='''Don\'t normalize all importance scores between 0 and 1'''						,     )

	parser.add_argument(
	    '''--try_masking'''						,     action='''store_true'''						,     help='''Whether to try to mask head until a threshold of accuracy.''' )
	parser.add_argument(
	    '''--masking_threshold'''						,     default=0.9						,     type=A_						,     help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''						,     )
	parser.add_argument(
	    '''--masking_amount'''						,     default=0.1						,     type=A_						,     help='''Amount to heads to masking at each masking step.''' )
	parser.add_argument('''--metric_name'''						,     default='''acc'''						,     type=A_						,     help='''Metric to use for head masking.''' )

	parser.add_argument(
	    '''--max_seq_length'''						,     default=1_28						,     type=A_						,     help=(
	        '''The maximum total input sequence length after WordPiece tokenization. \n'''
	        '''Sequences longer than this will be truncated, sequences shorter padded.'''
	    )						,     )
	parser.add_argument('''--batch_size'''						,     default=1						,     type=A_						,     help='''Batch size.''' )

	parser.add_argument('''--seed'''						,     type=A_						,     default=42 )
	parser.add_argument('''--local_rank'''						,     type=A_						,     default=-1						,     help='''local_rank for distributed training on gpus''' )
	parser.add_argument('''--no_cuda'''						,     action='''store_true'''						,     help='''Whether not to use CUDA when available''' )
	parser.add_argument('''--server_ip'''						,     type=A_						,     default=''''''						,     help='''Can be used for distant debugging.''' )
	parser.add_argument('''--server_port'''						,     type=A_						,     default=''''''						,     help='''Can be used for distant debugging.''' )
	lowerCAmelCase__	:						Optional[Any]  				=	parser.parse_args()

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print('''Waiting for debugger attach''' )
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,     redirect_output=A_ )
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
		lowerCAmelCase__	:						str  				=	0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank )
		lowerCAmelCase__	:						Dict  				=	torch.device('''cuda'''						,     args.local_rank )
		lowerCAmelCase__	:						Union[str, Any]  				=	1
		torch.distributed.init_process_group(backend='''nccl''' )  # Initializes the distributed backend

	# Setup logging
	logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
	logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device						,     args.n_gpu						,     bool(args.local_rank != -1 ) ) )

	lowerCAmelCase__	:						List[str]  				=	GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

	# Distributed and parallel training
	model.to(args.device )
	if args.local_rank != -1:
		lowerCAmelCase__	:						Dict  				=	nn.parallel.DistributedDataParallel(
		    A_						,     device_ids=[args.local_rank]						,     output_device=args.local_rank						,     find_unused_parameters=A_ )
	elif args.n_gpu > 1:
		lowerCAmelCase__	:						List[Any]  				=	nn.DataParallel(A_ )

	# Print/save training arguments
	os.makedirs(args.output_dir						,     exist_ok=A_ )
	torch.save(A_						,     os.path.join(args.output_dir						,     '''run_args.bin''' ) )
	logger.info('''Training/evaluation parameters %s'''						,     A_ )

	# Prepare dataset
	lowerCAmelCase__	:						str  				=	np.concatenate(
	    [
	        np.loadtxt(args.data_dir						,     dtype=np.intaa ),
	    ] )
	lowerCAmelCase__	:						Union[str, Any]  				=	(torch.from_numpy(A_ ),)
	lowerCAmelCase__	:						Tuple  				=	TensorDataset(*A_ )
	lowerCAmelCase__	:						Optional[int]  				=	RandomSampler(A_ )
	lowerCAmelCase__	:						Dict  				=	DataLoader(A_						,     sampler=A_						,     batch_size=args.batch_size )

	# Compute head entropy and importance score
	compute_heads_importance(A_						,     A_						,     A_ )

	# Try head masking (set heads to zero until the score goes under a threshole)
	# and head pruning (remove masked heads and see the effect on the network)
	if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
		lowerCAmelCase__	:						Tuple  				=	mask_heads(A_						,     A_						,     A_ )
		prune_heads(A_						,     A_						,     A_						,     A_ )


if __name__ == "__main__":
							main()




