




"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import (
          AutoProcessor,
          BertTokenizerFast,
          BlipImageProcessor,
          GPTaTokenizer,
          InstructBlipProcessor,
          PreTrainedTokenizerFast,
      )




@require_vision
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	List[Any]				)				->							str:




     '''simple docstring'''

     A__	=    tempfile.mkdtemp()

     A__	=    BlipImageProcessor()
     A__	=    GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"				)
     A__	=    BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"				)

     A__	=    InstructBlipProcessor(snake_case_     ,			snake_case_     ,			snake_case_				)

     processor.save_pretrained(self.tmpdirname				)




    def 							__magic_name__							(   self						:	List[str]     ,			**snake_case_						:	Any				)				->							Any:




     '''simple docstring'''

     return AutoProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				).tokenizer




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			**snake_case_						:	Dict				)				->							List[str]:




     '''simple docstring'''

     return AutoProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				).image_processor




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			**snake_case_						:	Tuple				)				->							Dict:




     '''simple docstring'''

     return AutoProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				).qformer_tokenizer




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    [np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta				)]

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

     return image_inputs




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    InstructBlipProcessor(
         tokenizer=self.get_tokenizer()     ,			image_processor=self.get_image_processor()     ,			qformer_tokenizer=self.get_qformer_tokenizer()     ,			)
     processor.save_pretrained(self.tmpdirname				)

     A__	=    self.get_tokenizer(bos_token="(BOS)"     ,			eos_token="(EOS)"				)
     A__	=    self.get_image_processor(do_normalize=snake_case_     ,			padding_value=1.0				)

     A__	=    InstructBlipProcessor.from_pretrained(
         self.tmpdirname     ,			bos_token="(BOS)"     ,			eos_token="(EOS)"     ,			do_normalize=snake_case_     ,			padding_value=1.0				)

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

     self.assertEqual(processor.image_processor.to_json_string()     ,			image_processor_add_kwargs.to_json_string()				)
     self.assertIsInstance(processor.image_processor     ,			snake_case_				)
     self.assertIsInstance(processor.qformer_tokenizer     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()
     A__	=    self.get_qformer_tokenizer()

     A__	=    InstructBlipProcessor(
         tokenizer=snake_case_     ,			image_processor=snake_case_     ,			qformer_tokenizer=snake_case_				)

     A__	=    self.prepare_image_inputs()

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=snake_case_     ,			return_tensors="np"				)

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




    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()
     A__	=    self.get_qformer_tokenizer()

     A__	=    InstructBlipProcessor(
         tokenizer=snake_case_     ,			image_processor=snake_case_     ,			qformer_tokenizer=snake_case_				)

     A__	=    "lower newer"

     A__	=    processor(text=snake_case_				)

     A__	=    tokenizer(snake_case_     ,			return_token_type_ids=snake_case_				)
     A__	=    qformer_tokenizer(snake_case_     ,			return_token_type_ids=snake_case_				)

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

     for key in encoded_tokens_qformer.keys():
      self.assertListEqual(encoded_tokens_qformer[key]     ,			encoded_processor["qformer_" + key]				)




    def 							__magic_name__							(   self						:	Any				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()
     A__	=    self.get_qformer_tokenizer()

     A__	=    InstructBlipProcessor(
         tokenizer=snake_case_     ,			image_processor=snake_case_     ,			qformer_tokenizer=snake_case_				)

     A__	=    "lower newer"
     A__	=    self.prepare_image_inputs()

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

     self.assertListEqual(
         list(inputs.keys()				)     ,			["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]     ,			)

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




    def 							__magic_name__							(   self						:	List[str]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()
     A__	=    self.get_qformer_tokenizer()

     A__	=    InstructBlipProcessor(
         tokenizer=snake_case_     ,			image_processor=snake_case_     ,			qformer_tokenizer=snake_case_				)

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

     A__	=    processor.batch_decode(snake_case_				)
     A__	=    tokenizer.batch_decode(snake_case_				)

     self.assertListEqual(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							int:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()
     A__	=    self.get_qformer_tokenizer()

     A__	=    InstructBlipProcessor(
         tokenizer=snake_case_     ,			image_processor=snake_case_     ,			qformer_tokenizer=snake_case_				)

     A__	=    "lower newer"
     A__	=    self.prepare_image_inputs()

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

     self.assertListEqual(
         list(inputs.keys()				)     ,			["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]     ,			)






"""simple docstring"""



# 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 platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

 parser.add_argument(
     "--config_file"      ,			default=lowercase_      ,			help="The config file to use for the default values in the launching script."						)

 if subparsers is not None:
  parser.set_defaults(func=lowercase_						)
 return parser







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`Accelerate` version": version,
     "Platform": platform.platform(),
     "Python version": platform.python_version(),
     "Numpy version": np.__version__,
     "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
     "PyTorch XPU available": str(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    torch.cuda.get_device_name()

 print("\nCopy-and-paste the text below in your GitHub issue\n"						)
 print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

 print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


if __name__ == "__main__":
      raise SystemExit(main())





