





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class UpperCAmelCase							(		unittest.TestCase							):
    _A :			List[str]						   =       ViTImageProcessor if is_vision_available() else None



    @property
    def 					__lowerCamelCase						(  self					):
     return self.image_processor_tester.prepare_image_processor_dict()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   (3, 32, 128)
     __UpperCAmelCase					=   tempfile.mkdtemp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

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

     __UpperCAmelCase					=   {
         'do_normalize': False,
         'do_resize': True,
         'image_processor_type': 'ViTImageProcessor',
         'resample': 3,
         'size': {'height': 32, 'width': 128},
     }
     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			__A					)
     with open(self.image_processor_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      json.dump(__A     ,			__A					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self					):
     shutil.rmtree(self.tmpdirname					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta					)

     __UpperCAmelCase					=   Image.fromarray(np.moveaxis(__A     ,			0     ,			-1					)					)

     return image_input



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizer()
     __UpperCAmelCase					=   self.get_image_processor()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)
     processor.save_pretrained(self.tmpdirname					)
     __UpperCAmelCase					=   MgpstrProcessor.from_pretrained(self.tmpdirname     ,			use_fast=__A					)

     self.assertEqual(processor.char_tokenizer.get_vocab()     ,			tokenizer.get_vocab()					)
     self.assertIsInstance(processor.char_tokenizer     ,			__A					)

     self.assertEqual(processor.image_processor.to_json_string()     ,			image_processor.to_json_string()					)
     self.assertIsInstance(processor.image_processor     ,			__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizer()
     __UpperCAmelCase					=   self.get_image_processor()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)
     processor.save_pretrained(self.tmpdirname					)

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

     __UpperCAmelCase					=   MgpstrProcessor.from_pretrained(
         self.tmpdirname     ,			bos_token='(BOS)'     ,			eos_token='(EOS)'     ,			do_normalize=__A     ,			padding_value=1.0					)

     self.assertEqual(processor.char_tokenizer.get_vocab()     ,			tokenizer_add_kwargs.get_vocab()					)
     self.assertIsInstance(processor.char_tokenizer     ,			__A					)

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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

     __UpperCAmelCase					=   self.prepare_image_inputs()

     __UpperCAmelCase					=   image_processor(__A     ,			return_tensors='np'					)
     __UpperCAmelCase					=   processor(images=__A     ,			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 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

     __UpperCAmelCase					=   'test'

     __UpperCAmelCase					=   processor(text=__A					)

     __UpperCAmelCase					=   tokenizer(__A					)
     for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]     ,			encoded_processor[key]					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

     __UpperCAmelCase					=   'test'
     __UpperCAmelCase					=   self.prepare_image_inputs()

     __UpperCAmelCase					=   processor(text=__A     ,			images=__A					)

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

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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

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

     __UpperCAmelCase					=   processor.char_decode(__A					)
     __UpperCAmelCase					=   tokenizer.batch_decode(__A					)
     __UpperCAmelCase					=   [seq.replace(' '     ,			''					) for seq in decoded_tok]

     self.assertListEqual(__A     ,			__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   self.prepare_image_inputs()

     __UpperCAmelCase					=   processor(text=__A     ,			images=__A					)

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




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_image_processor()
     __UpperCAmelCase					=   self.get_tokenizer()

     __UpperCAmelCase					=   MgpstrProcessor(tokenizer=__A     ,			image_processor=__A					)

     __UpperCAmelCase					=   torch.randn(1     ,			27     ,			38					)
     __UpperCAmelCase					=   torch.randn(1     ,			27     ,			50_257					)
     __UpperCAmelCase					=   torch.randn(1     ,			27     ,			30_522					)

     __UpperCAmelCase					=   processor.batch_decode([char_input, bpe_input, wp_input]					)

     self.assertListEqual(list(results.keys()					)     ,			['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']					)












'''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


_A:					str							=				"""Create a default config file for Accelerate with only a few flags set."""







def _lowerCAmelCase     (      _lowerCAmelCase="no"       ,    _lowerCAmelCase = default_json_config_file       ,    _lowerCAmelCase = False    )->     List[Any]:
 __UpperCAmelCase					=   Path(_lowerCAmelCase    )
 path.parent.mkdir(parents=_lowerCAmelCase       ,    exist_ok=_lowerCAmelCase    )
 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
 __UpperCAmelCase					=   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}'    )
 __UpperCAmelCase					=   {
     'compute_environment': 'LOCAL_MACHINE',
     'mixed_precision': mixed_precision,
 }
 if torch.cuda.is_available():
  __UpperCAmelCase					=   torch.cuda.device_count()
  __UpperCAmelCase					=   num_gpus
  __UpperCAmelCase					=   False
  if num_gpus > 1:
   __UpperCAmelCase					=   'MULTI_GPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_xpu_available() and use_xpu:
  __UpperCAmelCase					=   torch.xpu.device_count()
  __UpperCAmelCase					=   num_xpus
  __UpperCAmelCase					=   False
  if num_xpus > 1:
   __UpperCAmelCase					=   'MULTI_XPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_npu_available():
  __UpperCAmelCase					=   torch.npu.device_count()
  __UpperCAmelCase					=   num_npus
  __UpperCAmelCase					=   False
  if num_npus > 1:
   __UpperCAmelCase					=   'MULTI_NPU'
  else:
   __UpperCAmelCase					=   'NO'
 else:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   True
  __UpperCAmelCase					=   1
  __UpperCAmelCase					=   'NO'
 __UpperCAmelCase					=   ClusterConfig(**_lowerCAmelCase    )
 config.to_json_file(_lowerCAmelCase    )
 return path







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   parser.add_parser('default'       ,    parents=_lowerCAmelCase       ,    help=_lowerCAmelCase       ,    formatter_class=_lowerCAmelCase    )
 parser.add_argument(
     '--config_file'       ,    default=_lowerCAmelCase       ,    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=_lowerCAmelCase       ,    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=_lowerCAmelCase    )
 return parser







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Union[str, Any]:
 __UpperCAmelCase					=   write_basic_config(args.mixed_precision       ,    args.save_location    )
 if config_file:
  print(F'accelerate configuration saved at {config_file}'    )





