


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor






@require_vision
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      str:
            _A				=							tempfile.mkdtemp()

            # fmt: off
            _A				=							['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
            # fmt: on
            _A				=							dict(zip(_UpperCamelCase			,     range(len(_UpperCamelCase   )   )   )   )
            _A				=							['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
            _A				=							{'unk_token': '<unk>'}

            _A				=							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']   )
            _A				=							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']   )
            with open(self.vocab_file			,     'w'			,     encoding='utf-8'   ) as fp:
                 fp.write(json.dumps(_UpperCamelCase   ) + '\n'   )
            with open(self.merges_file			,     'w'			,     encoding='utf-8'   ) as fp:
                 fp.write('\n'.join(_UpperCamelCase   )   )

            _A				=							{
                'do_resize': True,
                'size': 20,
                'do_center_crop': True,
                'crop_size': 18,
                'do_normalize': True,
                'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
                'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
            }
            _A				=							os.path.join(self.tmpdirname			,     _UpperCamelCase   )
            with open(self.image_processor_file			,     'w'			,     encoding='utf-8'   ) as fp:
                 json.dump(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self			,     **_UpperCamelCase   )->      Dict:
            return CLIPTokenizer.from_pretrained(self.tmpdirname			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     **_UpperCamelCase   )->      Tuple:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     **_UpperCamelCase   )->      Union[str, Any]:
            return CLIPImageProcessor.from_pretrained(self.tmpdirname			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[str]:
            shutil.rmtree(self.tmpdirname   )






       def   UpperCamelCase						( self   )->      str:
            _A				=							[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta   )]

            _A				=							[Image.fromarray(np.moveaxis(_UpperCamelCase			,     0			,     -1   )   ) for x in image_inputs]

            return image_inputs






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							self.get_tokenizer()
            _A				=							self.get_rust_tokenizer()
            _A				=							self.get_image_processor()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )
            processor_slow.save_pretrained(self.tmpdirname   )
            _A				=							CLIPProcessor.from_pretrained(self.tmpdirname			,     use_fast=_UpperCamelCase   )

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )
            processor_fast.save_pretrained(self.tmpdirname   )
            _A				=							CLIPProcessor.from_pretrained(self.tmpdirname   )

            self.assertEqual(processor_slow.tokenizer.get_vocab()			,     tokenizer_slow.get_vocab()   )
            self.assertEqual(processor_fast.tokenizer.get_vocab()			,     tokenizer_fast.get_vocab()   )
            self.assertEqual(tokenizer_slow.get_vocab()			,     tokenizer_fast.get_vocab()   )
            self.assertIsInstance(processor_slow.tokenizer			,     _UpperCamelCase   )
            self.assertIsInstance(processor_fast.tokenizer			,     _UpperCamelCase   )

            self.assertEqual(processor_slow.image_processor.to_json_string()			,     image_processor.to_json_string()   )
            self.assertEqual(processor_fast.image_processor.to_json_string()			,     image_processor.to_json_string()   )
            self.assertIsInstance(processor_slow.image_processor			,     _UpperCamelCase   )
            self.assertIsInstance(processor_fast.image_processor			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							CLIPProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

            _A				=							self.get_tokenizer(bos_token='(BOS)'			,     eos_token='(EOS)'   )
            _A				=							self.get_image_processor(do_normalize=_UpperCamelCase			,     padding_value=1.0   )

            _A				=							CLIPProcessor.from_pretrained(
                self.tmpdirname			,     bos_token='(BOS)'			,     eos_token='(EOS)'			,     do_normalize=_UpperCamelCase			,     padding_value=1.0   )

            self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer_add_kwargs.get_vocab()   )
            self.assertIsInstance(processor.tokenizer			,     _UpperCamelCase   )

            self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()   )
            self.assertIsInstance(processor.image_processor			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							self.get_image_processor()
            _A				=							self.get_tokenizer()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )

            _A				=							self.prepare_image_inputs()

            _A				=							image_processor(_UpperCamelCase			,     return_tensors='np'   )
            _A				=							processor(images=_UpperCamelCase			,     return_tensors='np'   )

            for key in input_image_proc.keys():
                 self.assertAlmostEqual(input_image_proc[key].sum()			,     input_processor[key].sum()			,     delta=1e-2   )






       def   UpperCamelCase						( self   )->      int:
            _A				=							self.get_image_processor()
            _A				=							self.get_tokenizer()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )

            _A				=							'lower newer'

            _A				=							processor(text=_UpperCamelCase   )

            _A				=							tokenizer(_UpperCamelCase   )

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]   )






       def   UpperCamelCase						( self   )->      int:
            _A				=							self.get_image_processor()
            _A				=							self.get_tokenizer()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )

            _A				=							'lower newer'
            _A				=							self.prepare_image_inputs()

            _A				=							processor(text=_UpperCamelCase			,     images=_UpperCamelCase   )

            self.assertListEqual(list(inputs.keys()   )			,     ['input_ids', 'attention_mask', 'pixel_values']   )

            # test if it raises when no input is passed
            with pytest.raises(_UpperCamelCase   ):
                 processor()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.get_image_processor()
            _A				=							self.get_tokenizer()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )

            _A				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _A				=							processor.batch_decode(_UpperCamelCase   )
            _A				=							tokenizer.batch_decode(_UpperCamelCase   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							self.get_image_processor()
            _A				=							self.get_tokenizer()

            _A				=							CLIPProcessor(tokenizer=_UpperCamelCase			,     image_processor=_UpperCamelCase   )

            _A				=							'lower newer'
            _A				=							self.prepare_image_inputs()

            _A				=							processor(text=_UpperCamelCase			,     images=_UpperCamelCase   )

            self.assertListEqual(list(inputs.keys()   )			,     processor.model_input_names   )









'''simple docstring'''
# Copyright 2021 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							Path(__UpperCamelCase		)
     path.parent.mkdir(parents=__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
     if path.exists():
          print(
              F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
          return False
     _A				=							mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
     _A				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							parser.add_parser('default'							,    parents=__UpperCamelCase							,    help=__UpperCamelCase							,    formatter_class=__UpperCamelCase		)
     parser.add_argument(
         '--config_file'							,    default=__UpperCamelCase							,    help=(
             'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
             'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
             'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
             'with \'huggingface\'.'
         )							,    dest='save_location'							,    )

     parser.add_argument(
         '--mixed_precision'							,    choices=['no', 'fp16', 'bf16']							,    type=__UpperCamelCase							,    help='Whether or not to use mixed precision training. '
         'Choose between FP16 and BF16 (bfloat16) training. '
         'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'							,    default='no'							,    )
     parser.set_defaults(func=__UpperCamelCase		)
     return parser

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





