






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 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[int]			 =ViTImageProcessor if is_vision_available() else None


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return self.image_processor_tester.prepare_image_processor_dict()


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[int]       		=(3, 32, 128)
									__UpperCamelCase      :					Optional[Any]       		=tempfile.mkdtemp()

									# fmt: off
									__UpperCamelCase      :					str       		=['[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      :					Optional[Any]       		=dict(zip(lowerCamelCase__							,  range(len(lowerCamelCase__    )    )    )    )

									__UpperCamelCase      :					Dict       		=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(lowerCamelCase__    ) + '\n'    )

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


				def       __lowercase							( self							,  **lowerCamelCase__    ):




									"""simple docstring"""

									return MgpstrTokenizer.from_pretrained(self.tmpdirname							,  **lowerCamelCase__    )


				def       __lowercase							( self							,  **lowerCamelCase__    ):




									"""simple docstring"""

									return ViTImageProcessor.from_pretrained(self.tmpdirname							,  **lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									shutil.rmtree(self.tmpdirname    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=np.random.randint(255							,  size=(3, 30, 400)							,  dtype=np.uinta    )

									__UpperCamelCase      :					List[str]       		=Image.fromarray(np.moveaxis(lowerCamelCase__							,  0							,  -1    )    )

									return image_input


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=self.get_tokenizer()
									__UpperCamelCase      :					Union[str, Any]       		=self.get_image_processor()

									__UpperCamelCase      :					Union[str, Any]       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )
									processor.save_pretrained(self.tmpdirname    )
									__UpperCamelCase      :					Dict       		=MgpstrProcessor.from_pretrained(self.tmpdirname							,  use_fast=lowerCamelCase__    )

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

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[int]       		=self.get_tokenizer()
									__UpperCamelCase      :					List[str]       		=self.get_image_processor()

									__UpperCamelCase      :					int       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )
									processor.save_pretrained(self.tmpdirname    )

									__UpperCamelCase      :					Union[str, Any]       		=self.get_tokenizer(bos_token='(BOS)'							,  eos_token='(EOS)'    )
									__UpperCamelCase      :					str       		=self.get_image_processor(do_normalize=lowerCamelCase__							,  padding_value=1.0    )

									__UpperCamelCase      :					Dict       		=MgpstrProcessor.from_pretrained(
									    self.tmpdirname							,  bos_token='(BOS)'							,  eos_token='(EOS)'							,  do_normalize=lowerCamelCase__							,  padding_value=1.0    )

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

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=self.get_image_processor()
									__UpperCamelCase      :					Optional[int]       		=self.get_tokenizer()

									__UpperCamelCase      :					Dict       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					Optional[int]       		=self.prepare_image_inputs()

									__UpperCamelCase      :					List[Any]       		=image_processor(lowerCamelCase__							,  return_tensors='np'    )
									__UpperCamelCase      :					Dict       		=processor(images=lowerCamelCase__							,  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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.get_image_processor()
									__UpperCamelCase      :					int       		=self.get_tokenizer()

									__UpperCamelCase      :					str       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					List[Any]       		='test'

									__UpperCamelCase      :					Optional[Any]       		=processor(text=lowerCamelCase__    )

									__UpperCamelCase      :					str       		=tokenizer(lowerCamelCase__    )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]							,  encoded_processor[key]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=self.get_image_processor()
									__UpperCamelCase      :					int       		=self.get_tokenizer()

									__UpperCamelCase      :					Optional[int]       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					Dict       		='test'
									__UpperCamelCase      :					Any       		=self.prepare_image_inputs()

									__UpperCamelCase      :					List[Any]       		=processor(text=lowerCamelCase__							,  images=lowerCamelCase__    )

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

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.get_image_processor()
									__UpperCamelCase      :					Union[str, Any]       		=self.get_tokenizer()

									__UpperCamelCase      :					List[str]       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					Union[str, Any]       		=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									__UpperCamelCase      :					str       		=processor.char_decode(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		=tokenizer.batch_decode(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		=[seq.replace(' '							,  ''    ) for seq in decoded_tok]

									self.assertListEqual(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=self.get_image_processor()
									__UpperCamelCase      :					str       		=self.get_tokenizer()

									__UpperCamelCase      :					str       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					List[str]       		=None
									__UpperCamelCase      :					int       		=self.prepare_image_inputs()

									__UpperCamelCase      :					List[Any]       		=processor(text=lowerCamelCase__							,  images=lowerCamelCase__    )

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







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.get_image_processor()
									__UpperCamelCase      :					List[str]       		=self.get_tokenizer()

									__UpperCamelCase      :					Tuple       		=MgpstrProcessor(tokenizer=lowerCamelCase__							,  image_processor=lowerCamelCase__    )

									__UpperCamelCase      :					Optional[int]       		=torch.randn(1							,  27							,  38    )
									__UpperCamelCase      :					Any       		=torch.randn(1							,  27							,  50257    )
									__UpperCamelCase      :					Any       		=torch.randn(1							,  27							,  30522    )

									__UpperCamelCase      :					List[str]       		=processor.batch_decode([char_input, bpe_input, wp_input]    )

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


# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


__lowerCamelCase			: Union[str, Any]							   =		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
   print('''Torch version:''', None)

try:
   import transformers

   print('''transformers version:''', transformers.__version__)
except ImportError:
   print('''transformers version:''', None)
