


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase			=				'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def      _a					( SCREAMING_SNAKE_CASE=None	):


		"""simple docstring"""




		if subparsers is not None:
				lowercase__				= subparsers.add_parser('''tpu-config'''		,							description=_description	)
		else:
				lowercase__				= argparse.ArgumentParser('''Accelerate tpu-config command'''		,							description=_description	)
		# Core arguments
		lowercase__				= parser.add_argument_group(
		    '''Config Arguments'''		,							'''Arguments that can be configured through `accelerate config`.'''	)
		config_args.add_argument(
		    '''--config_file'''		,							type=SCREAMING_SNAKE_CASE		,							default=SCREAMING_SNAKE_CASE		,							help='''Path to the config file to use for accelerate.'''		,							)
		config_args.add_argument(
		    '''--tpu_name'''		,							default=SCREAMING_SNAKE_CASE		,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''		,							)
		config_args.add_argument(
		    '''--tpu_zone'''		,							default=SCREAMING_SNAKE_CASE		,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''		,							)
		lowercase__				= parser.add_argument_group('''TPU Arguments'''		,							'''Arguments for options ran inside the TPU.'''	)
		pod_args.add_argument(
		    '''--use_alpha'''		,							action='''store_true'''		,							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''		,							)
		pod_args.add_argument(
		    '''--command_file'''		,							default=SCREAMING_SNAKE_CASE		,							help='''The path to the file containing the commands to run on the pod on startup.'''		,							)
		pod_args.add_argument(
		    '''--command'''		,							action='''append'''		,							nargs='''+'''		,							help='''A command to run on the pod. Can be passed multiple times.'''		,							)
		pod_args.add_argument(
		    '''--install_accelerate'''		,							action='''store_true'''		,							help='''Whether to install accelerate on the pod. Defaults to False.'''		,							)
		pod_args.add_argument(
		    '''--accelerate_version'''		,							default='''latest'''		,							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''		,							)
		pod_args.add_argument(
		    '''--debug'''		,							action='''store_true'''		,							help='''If set, will print the command that would be run instead of running it.'''	)

		if subparsers is not None:
				parser.set_defaults(func=SCREAMING_SNAKE_CASE	)
		return parser
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE	):
				lowercase__				= load_config_from_file(args.config_file	)
				if not args.command_file and defaults.command_file is not None and not args.command:
						lowercase__				= defaults.command_file
				if not args.command and defaults.commands is not None:
						lowercase__				= defaults.commands
				if not args.tpu_name:
						lowercase__				= defaults.tpu_name
				if not args.tpu_zone:
						lowercase__				= defaults.tpu_zone
		if args.accelerate_version == "dev":
				lowercase__				= '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				lowercase__				= '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version	)		,							SCREAMING_SNAKE_CASE	):
				lowercase__				= f'accelerate=={args.accelerate_version}'

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''	)

		if args.command_file:
				with open(args.command_file		,							'''r'''	) as f:
						lowercase__				= [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0]		,							SCREAMING_SNAKE_CASE	):
				lowercase__				= [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		lowercase__				= ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [f'pip install {args.accelerate_version}']
		new_cmd += args.command
		lowercase__				= '''; '''.join(SCREAMING_SNAKE_CASE	)

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		lowercase__				= ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(f'Running {" ".join(SCREAMING_SNAKE_CASE	)}'	)
				return
		subprocess.run(SCREAMING_SNAKE_CASE	)
		print('''Successfully setup pod.'''	)
def      _a					( ):


		"""simple docstring"""




		lowercase__				= tpu_command_parser()
		lowercase__				= parser.parse_args()

		tpu_command_launcher(SCREAMING_SNAKE_CASE	)





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Optional[int] ,  A__      :  Union[str, Any]	)       -> int:





      """simple docstring"""



      _lowercase      			=nn.functional.normalize(A__	)
      _lowercase      			=nn.functional.normalize(A__	)
      return torch.mm(A__ ,  normalized_text_embeds.t()	)







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    CLIPConfig

  _a		     =    ["""CLIPEncoderLayer"""]




  def __init__(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )

        _lowercase      			=CLIPVisionModel(config.vision_config   )
        _lowercase      			=nn.Linear(config.vision_config.hidden_size				,  config.projection_dim				,  bias=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3   )				,  requires_grad=lowerCAmelCase   )




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   ).cpu().float().numpy()
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.concept_embeds   ).cpu().float().numpy()

        _lowercase      			=[]
        _lowercase      			=image_embeds.shape[0]
        for i in range(lowerCAmelCase   ):
              _lowercase      			={'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}

              # increase this value to create a stronger `nfsw` filter
              # at the cost of increasing the possibility of filtering benign images
              _lowercase      			=0.0

              for concept_idx in range(len(special_cos_dist[0]   )   ):
                    _lowercase      			=special_cos_dist[i][concept_idx]
                    _lowercase      			=self.special_care_embeds_weights[concept_idx].item()
                    _lowercase      			=round(concept_cos - concept_threshold + adjustment				,  3   )
                    if result_img["special_scores"][concept_idx] > 0:
                          result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]}   )
                          _lowercase      			=0.01

              for concept_idx in range(len(cos_dist[0]   )   ):
                    _lowercase      			=cos_dist[i][concept_idx]
                    _lowercase      			=self.concept_embeds_weights[concept_idx].item()
                    _lowercase      			=round(concept_cos - concept_threshold + adjustment				,  3   )
                    if result_img["concept_scores"][concept_idx] > 0:
                          result_img["bad_concepts"].append(lowerCAmelCase   )

              result.append(lowerCAmelCase   )

        _lowercase      			=[len(res['bad_concepts']   ) > 0 for res in result]

        return images, has_nsfw_concepts




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   )
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.concept_embeds   )

        # increase this value to create a stronger `nsfw` filter
        # at the cost of increasing the possibility of filtering benign images
        _lowercase      			=0.0

        _lowercase      			=special_cos_dist - self.special_care_embeds_weights + adjustment
        # special_scores = special_scores.round(decimals=3)
        _lowercase      			=torch.any(special_scores > 0				,  dim=1   )
        _lowercase      			=special_care * 0.01
        _lowercase      			=special_adjustment.unsqueeze(1   ).expand(-1				,  cos_dist.shape[1]   )

        _lowercase      			=(cos_dist - self.concept_embeds_weights) + special_adjustment
        # concept_scores = concept_scores.round(decimals=3)
        _lowercase      			=torch.any(concept_scores > 0				,  dim=1   )

        return images, has_nsfw_concepts


