


'''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


_snake_case  :	int			      =       logging.getLogger(__name__)







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







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







def        lowerCAmelCase_  (      __lowerCamelCase			):
							logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(__lowerCamelCase			)			)			)			)
							for row in range(len(__lowerCamelCase			)			):
														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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=True							,			__lowerCamelCase=True							,			__lowerCamelCase=None							,			__lowerCamelCase=False			):
							__snake_case	:    List[Any]											=     model.config.num_hidden_layers, model.config.num_attention_heads
							__snake_case	:    Optional[Any]											=     torch.zeros(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)
							__snake_case	:    Union[str, Any]											=     torch.zeros(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)

							if head_mask is None:
														__snake_case	:    List[str]											=     torch.ones(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)

							head_mask.requires_grad_(requires_grad=__lowerCamelCase			)
							# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
							if actually_pruned:
														__snake_case	:    Optional[int]											=     None

							__snake_case	:    Optional[Any]											=     0.0
							__snake_case	:    Tuple											=     0.0
							for step, inputs in enumerate(tqdm(__lowerCamelCase							,			desc="Iteration"							,			disable=args.local_rank not in [-1, 0]			)			):
														__snake_case	:    int											=     tuple(t.to(args.device			) for t in inputs			)
														(__snake_case    )	:    Optional[int]											=     inputs

														# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
														__snake_case	:    int											=     model(__lowerCamelCase							,			labels=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
														#  (loss), lm_logits, presents, (all hidden_states), (attentions)
														__snake_case	:    List[str]											=     (
														    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(__lowerCamelCase			):
																												__snake_case	:    str											=     entropy(attn.detach()							,			__lowerCamelCase			)
																												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(__lowerCamelCase			).float().detach().sum().data

							# Normalize
							attn_entropy /= tot_tokens
							head_importance /= tot_tokens
							# Layerwise importance normalization
							if not args.dont_normalize_importance_by_layer:
														__snake_case	:    List[Any]											=     2
														__snake_case	:    Tuple											=     torch.pow(torch.pow(__lowerCamelCase							,			__lowerCamelCase			).sum(-1			)							,			1 / exponent			)
														head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

							if not args.dont_normalize_global_importance:
														__snake_case	:    Tuple											=     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Optional[Any]											=     compute_heads_importance(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     1 / loss  # instead of downsteam score use the LM loss
							logger.info("Pruning: original score: %f, threshold: %f"							,			__lowerCamelCase							,			original_score * args.masking_threshold			)

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

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

														if len(__lowerCamelCase			) <= num_to_mask:
																					print("BREAK BY num_to_mask"			)
																					break

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

														# Compute metric and head importance again
														__snake_case	:    Union[str, Any]											=     compute_heads_importance(
														    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
														__snake_case	:    List[str]											=     1 / loss
														logger.info(
														    "Masking: current score: %f, remaining heads %d (%.1f percents)"							,			__lowerCamelCase							,			new_head_mask.sum()							,			new_head_mask.sum() / new_head_mask.numel() * 1_0_0							,			)

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

							return head_mask







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    int											=     datetime.now()
							__snake_case	:    Any											=     compute_heads_importance(
							    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			compute_importance=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
							__snake_case	:    Dict											=     1 / loss
							__snake_case	:    Optional[Any]											=     datetime.now() - before_time

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

							for k, v in heads_to_prune.items():
														if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Dict											=     [
																					    v,
																					]

							assert sum(len(__lowerCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
							model.prune_heads(__lowerCamelCase			)
							__snake_case	:    Dict											=     sum(p.numel() for p in model.parameters()			)

							__snake_case	:    int											=     datetime.now()
							__snake_case	:    Optional[Any]											=     compute_heads_importance(
							    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			compute_importance=__lowerCamelCase							,			head_mask=__lowerCamelCase							,			actually_pruned=__lowerCamelCase							,			)

							__snake_case	:    Optional[Any]											=     1 / loss
							__snake_case	:    Union[str, Any]											=     datetime.now() - before_time

							logger.info(
							    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"							,			__lowerCamelCase							,			__lowerCamelCase							,			pruned_num_params / original_num_params * 1_0_0							,			)
							logger.info("Pruning: score with masking: %f score with pruning: %f"							,			__lowerCamelCase							,			__lowerCamelCase			)
							logger.info("Pruning: speed ratio (original timing / new timing): %f percents"							,			original_time / new_time * 1_0_0			)
							save_model(__lowerCamelCase							,			args.output_dir			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--data_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			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=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			help="Path to pretrained model or model identifier from huggingface.co/models"							,			)
							parser.add_argument(
							    "--output_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			help="The output directory where the model predictions and checkpoints will be written."							,			)

							# Other parameters
							parser.add_argument(
							    "--config_name"							,			default=""							,			type=__lowerCamelCase							,			help="Pretrained config name or path if not the same as model_name_or_path"							,			)
							parser.add_argument(
							    "--tokenizer_name"							,			default=""							,			type=__lowerCamelCase							,			help="Pretrained tokenizer name or path if not the same as model_name_or_path"							,			)
							parser.add_argument(
							    "--cache_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			help="Where do you want to store the pre-trained models downloaded from s3"							,			)
							parser.add_argument(
							    "--data_subset"							,			type=__lowerCamelCase							,			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=__lowerCamelCase							,			help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."							,			)
							parser.add_argument(
							    "--masking_amount"							,			default=0.1							,			type=__lowerCamelCase							,			help="Amount to heads to masking at each masking step."			)
							parser.add_argument("--metric_name"							,			default="acc"							,			type=__lowerCamelCase							,			help="Metric to use for head masking."			)

							parser.add_argument(
							    "--max_seq_length"							,			default=1_2_8							,			type=__lowerCamelCase							,			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=__lowerCamelCase							,			help="Batch size."			)

							parser.add_argument("--seed"							,			type=__lowerCamelCase							,			default=4_2			)
							parser.add_argument("--local_rank"							,			type=__lowerCamelCase							,			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=__lowerCamelCase							,			default=""							,			help="Can be used for distant debugging."			)
							parser.add_argument("--server_port"							,			type=__lowerCamelCase							,			default=""							,			help="Can be used for distant debugging."			)
							__snake_case	:    Union[str, 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=__lowerCamelCase			)
														ptvsd.wait_for_attach()

							# Setup devices and distributed training
							if args.local_rank == -1 or args.no_cuda:
														__snake_case	:    str											=     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"			)
														__snake_case	:    Union[str, Any]											=     0 if args.no_cuda else torch.cuda.device_count()
							else:
														torch.cuda.set_device(args.local_rank			)
														__snake_case	:    Tuple											=     torch.device("cuda"							,			args.local_rank			)
														__snake_case	:    int											=     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			)			)			)

							__snake_case	:    Tuple											=     GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

							# Distributed and parallel training
							model.to(args.device			)
							if args.local_rank != -1:
														__snake_case	:    int											=     nn.parallel.DistributedDataParallel(
														    __lowerCamelCase							,			device_ids=[args.local_rank]							,			output_device=args.local_rank							,			find_unused_parameters=__lowerCamelCase			)
							elif args.n_gpu > 1:
														__snake_case	:    str											=     nn.DataParallel(__lowerCamelCase			)

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

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

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

							# 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:
														__snake_case	:    List[Any]											=     mask_heads(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)
														prune_heads(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)


if __name__ == "__main__":
			main()





import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_snake_case  :	List[Any]			      =       "src/diffusers"
_snake_case  :	str			      =       "."


# This is to make sure the diffusers module imported is the one in the repo.
_snake_case  :	List[str]			      =       importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_snake_case  :	Dict			      =       spec.loader.load_module()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							return line.startswith(__lowerCamelCase			) or len(__lowerCamelCase			) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"							,			__lowerCamelCase			) is not None







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    str											=     object_name.split("."			)
							__snake_case	:    Any											=     0

							# First let's find the module where our object lives.
							__snake_case	:    Optional[int]											=     parts[i]
							while i < len(__lowerCamelCase			) and not os.path.isfile(os.path.join(__lowerCamelCase							,			F'{module}.py'			)			):
														i += 1
														if i < len(__lowerCamelCase			):
																					__snake_case	:    Dict											=     os.path.join(__lowerCamelCase							,			parts[i]			)
							if i >= len(__lowerCamelCase			):
														raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'			)

							with open(os.path.join(__lowerCamelCase							,			F'{module}.py'			)							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    Optional[Any]											=     f.readlines()

							# Now let's find the class / func in the code!
							__snake_case	:    Any											=     ""
							__snake_case	:    Any											=     0
							for name in parts[i + 1 :]:
														while (
														    line_index < len(__lowerCamelCase			) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'							,			lines[line_index]			) is None
														):
																					line_index += 1
														indent += "    "
														line_index += 1

							if line_index >= len(__lowerCamelCase			):
														raise ValueError(F' {object_name} does not match any function or class in {module}.'			)

							# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
							__snake_case	:    Optional[Any]											=     line_index
							while line_index < len(__lowerCamelCase			) and _should_continue(lines[line_index]							,			__lowerCamelCase			):
														line_index += 1
							# Clean up empty lines at the end (if any).
							while len(lines[line_index - 1]			) <= 1:
														line_index -= 1

							__snake_case	:    Dict											=     lines[start_index:line_index]
							return "".join(__lowerCamelCase			)


_snake_case  :	Any			      =       re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
_snake_case  :	Union[str, Any]			      =       re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
_snake_case  :	Optional[int]			      =       re.compile(R"<FILL\s+[^>]*>")







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Dict											=     code.split("\n"			)
							__snake_case	:    List[str]											=     0
							while idx < len(__lowerCamelCase			) and len(lines[idx]			) == 0:
														idx += 1
							if idx < len(__lowerCamelCase			):
														return re.search(R"^(\s*)\S"							,			lines[idx]			).groups()[0]
							return ""







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    List[str]											=     len(get_indent(__lowerCamelCase			)			) > 0
							if has_indent:
														__snake_case	:    str											=     F'class Bla:\n{code}'
							__snake_case	:    List[Any]											=     black.Mode(target_versions={black.TargetVersion.PYaa}							,			line_length=1_1_9							,			preview=__lowerCamelCase			)
							__snake_case	:    int											=     black.format_str(__lowerCamelCase							,			mode=__lowerCamelCase			)
							__snake_case				,    __snake_case	:    List[str]											=     style_docstrings_in_code(__lowerCamelCase			)
							return result[len("class Bla:\n"			) :] if has_indent else result







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							with open(__lowerCamelCase							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    List[Any]											=     f.readlines()
							__snake_case	:    int											=     []
							__snake_case	:    Optional[Any]											=     0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(__lowerCamelCase			):
														__snake_case	:    List[Any]											=     _re_copy_warning.search(lines[line_index]			)
														if search is None:
																					line_index += 1
																					continue

														# There is some copied code here, let's retrieve the original.
														__snake_case				,    __snake_case				,    __snake_case	:    List[str]											=     search.groups()
														__snake_case	:    List[str]											=     find_code_in_diffusers(__lowerCamelCase			)
														__snake_case	:    str											=     get_indent(__lowerCamelCase			)

														__snake_case	:    Optional[Any]											=     line_index + 1 if indent == theoretical_indent else line_index + 2
														__snake_case	:    Tuple											=     theoretical_indent
														__snake_case	:    str											=     start_index

														# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
														__snake_case	:    str											=     True
														while line_index < len(__lowerCamelCase			) and should_continue:
																					line_index += 1
																					if line_index >= len(__lowerCamelCase			):
																												break
																					__snake_case	:    Union[str, Any]											=     lines[line_index]
																					__snake_case	:    Any											=     _should_continue(__lowerCamelCase							,			__lowerCamelCase			) and re.search(F'^{indent}# End copy'							,			__lowerCamelCase			) is None
														# Clean up empty lines at the end (if any).
														while len(lines[line_index - 1]			) <= 1:
																					line_index -= 1

														__snake_case	:    int											=     lines[start_index:line_index]
														__snake_case	:    int											=     "".join(__lowerCamelCase			)

														# Remove any nested `Copied from` comments to avoid circular copies
														__snake_case	:    Union[str, Any]											=     [line for line in theoretical_code.split("\n"			) if _re_copy_warning.search(__lowerCamelCase			) is None]
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)

														# Before comparing, use the `replace_pattern` on the original code.
														if len(__lowerCamelCase			) > 0:
																					__snake_case	:    Optional[int]											=     replace_pattern.replace("with"							,			""			).split(","			)
																					__snake_case	:    Tuple											=     [_re_replace_pattern.search(__lowerCamelCase			) for p in patterns]
																					for pattern in patterns:
																												if pattern is None:
																																			continue
																												__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     pattern.groups()
																												__snake_case	:    Tuple											=     re.sub(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)
																												if option.strip() == "all-casing":
																																			__snake_case	:    List[Any]											=     re.sub(obja.lower()							,			obja.lower()							,			__lowerCamelCase			)
																																			__snake_case	:    Union[str, Any]											=     re.sub(obja.upper()							,			obja.upper()							,			__lowerCamelCase			)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
																					__snake_case	:    str											=     blackify(lines[start_index - 1] + theoretical_code			)
																					__snake_case	:    Tuple											=     theoretical_code[len(lines[start_index - 1]			) :]

														# Test for a diff and act accordingly.
														if observed_code != theoretical_code:
																					diffs.append([object_name, start_index]			)
																					if overwrite:
																												__snake_case	:    str											=     lines[:start_index] + [theoretical_code] + lines[line_index:]
																												__snake_case	:    List[Any]											=     start_index + 1

							if overwrite and len(__lowerCamelCase			) > 0:
														# Warn the user a file has been modified.
														print(F'Detected changes, rewriting {filename}.'			)
														with open(__lowerCamelCase							,			"w"							,			encoding="utf-8"							,			newline="\n"			) as f:
																					f.writelines(__lowerCamelCase			)
							return diffs







def        lowerCAmelCase_  (      __lowerCamelCase = False			):
							__snake_case	:    Optional[Any]											=     glob.glob(os.path.join(__lowerCamelCase							,			"**/*.py"			)							,			recursive=__lowerCamelCase			)
							__snake_case	:    Dict											=     []
							for filename in all_files:
														__snake_case	:    Union[str, Any]											=     is_copy_consistent(__lowerCamelCase							,			__lowerCamelCase			)
														diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
							if not overwrite and len(__lowerCamelCase			) > 0:
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)
														raise Exception(
														    "Found the following copy inconsistencies:\n"
														    + diff
														    + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."			)


if __name__ == "__main__":
			_snake_case  :	Optional[int]			      =       argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_snake_case  :	Union[str, Any]			      =       parser.parse_args()

			check_copies(args.fix_and_overwrite)

