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  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   Any							):
   __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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   processor.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				**__SCREAMING_SNAKE_CASE			:   Dict							):
   return AutoProcessor.from_pretrained(self.tmpdirname  ,				**__SCREAMING_SNAKE_CASE							).tokenizer

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   return AutoProcessor.from_pretrained(self.tmpdirname  ,				**__SCREAMING_SNAKE_CASE							).image_processor

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   return AutoProcessor.from_pretrained(self.tmpdirname  ,				**__SCREAMING_SNAKE_CASE							).qformer_tokenizer

  def 		_UpperCAmelCase (		self			:   Any							):
   shutil.rmtree(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	[np.random.randint(2_55  ,				size=(3, 30, 4_00)  ,				dtype=np.uinta							)]

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

   return image_inputs

  def 		_UpperCAmelCase (		self			:   Dict							):
   __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=__SCREAMING_SNAKE_CASE  ,				padding_value=1.0							)

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

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

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

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	self.get_image_processor()
   __a							=	self.get_tokenizer()
   __a							=	self.get_qformer_tokenizer()

   __a							=	InstructBlipProcessor(
       tokenizer=__SCREAMING_SNAKE_CASE  ,				image_processor=__SCREAMING_SNAKE_CASE  ,				qformer_tokenizer=__SCREAMING_SNAKE_CASE							)

   __a							=	self.prepare_image_inputs()

   __a							=	image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							)
   __a							=	processor(images=__SCREAMING_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 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.get_image_processor()
   __a							=	self.get_tokenizer()
   __a							=	self.get_qformer_tokenizer()

   __a							=	InstructBlipProcessor(
       tokenizer=__SCREAMING_SNAKE_CASE  ,				image_processor=__SCREAMING_SNAKE_CASE  ,				qformer_tokenizer=__SCREAMING_SNAKE_CASE							)

   __a							=	"lower newer"

   __a							=	processor(text=__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer(__SCREAMING_SNAKE_CASE  ,				return_token_type_ids=__SCREAMING_SNAKE_CASE							)
   __a							=	qformer_tokenizer(__SCREAMING_SNAKE_CASE  ,				return_token_type_ids=__SCREAMING_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 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	self.get_image_processor()
   __a							=	self.get_tokenizer()
   __a							=	self.get_qformer_tokenizer()

   __a							=	InstructBlipProcessor(
       tokenizer=__SCREAMING_SNAKE_CASE  ,				image_processor=__SCREAMING_SNAKE_CASE  ,				qformer_tokenizer=__SCREAMING_SNAKE_CASE							)

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

   __a							=	processor(text=__SCREAMING_SNAKE_CASE  ,				images=__SCREAMING_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(__SCREAMING_SNAKE_CASE							):
    processor()

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.get_image_processor()
   __a							=	self.get_tokenizer()
   __a							=	self.get_qformer_tokenizer()

   __a							=	InstructBlipProcessor(
       tokenizer=__SCREAMING_SNAKE_CASE  ,				image_processor=__SCREAMING_SNAKE_CASE  ,				qformer_tokenizer=__SCREAMING_SNAKE_CASE							)

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

   __a							=	processor.batch_decode(__SCREAMING_SNAKE_CASE							)
   __a							=	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE							)

   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.get_image_processor()
   __a							=	self.get_tokenizer()
   __a							=	self.get_qformer_tokenizer()

   __a							=	InstructBlipProcessor(
       tokenizer=__SCREAMING_SNAKE_CASE  ,				image_processor=__SCREAMING_SNAKE_CASE  ,				qformer_tokenizer=__SCREAMING_SNAKE_CASE							)

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

   __a							=	processor(text=__SCREAMING_SNAKE_CASE  ,				images=__SCREAMING_SNAKE_CASE							)

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



# 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 argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def 	__A		(      ):



 """simple docstring"""


 __a							=	ArgumentParser("Accelerate CLI tool"				,					usage="accelerate <command> [<args>]"				,					allow_abbrev=_A       )
 __a							=	parser.add_subparsers(help="accelerate command helpers"       )

 # Register commands
 get_config_parser(subparsers=_A       )
 env_command_parser(subparsers=_A       )
 launch_command_parser(subparsers=_A       )
 tpu_command_parser(subparsers=_A       )
 test_command_parser(subparsers=_A       )

 # Let's go
 __a							=	parser.parse_args()

 if not hasattr(_A				,					"func"       ):
  parser.print_help()
  exit(1       )

 # Run
 args.func(_A       )


if __name__ == "__main__":
  main()


