



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  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					ViTImageProcessor if is_vision_available() else None


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


							def _a      (							self		):
											a__				      =		(3, 32, 128)
											a__				      =		tempfile.mkdtemp()

											# fmt: off
											a__				      =		["""[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
											a__				      =		dict(zip(a_  ,  range(len(a_		)		)		)		)

											a__				      =		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"""		)

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


							def _a      (							self  ,  **a_		):
											return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return ViTImageProcessor.from_pretrained(self.tmpdirname  ,  **a_		)


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


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

											a__				      =		Image.fromarray(np.moveaxis(a_  ,  0  ,  -1		)		)

											return image_input


							def _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_image_processor()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor.save_pretrained(self.tmpdirname		)
											a__				      =		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 _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_image_processor()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor.save_pretrained(self.tmpdirname		)

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

											a__				      =		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 _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()

											a__				      =		image_processor(a_  ,  return_tensors="""np"""		)
											a__				      =		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 _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""test"""

											a__				      =		processor(text=a_		)

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


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""test"""
											a__				      =		self.prepare_image_inputs()

											a__				      =		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 _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

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

											a__				      =		processor.char_decode(a_		)
											a__				      =		tokenizer.batch_decode(a_		)
											a__				      =		[seq.replace(""" """  ,  """"""		) for seq in decoded_tok]

											self.assertListEqual(a_  ,  a_		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		None
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(text=a_  ,  images=a_		)

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







							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		MgpstrProcessor(tokenizer=a_  ,  image_processor=a_		)

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

											a__				      =		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 2020 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 sys

import transformers


UpperCAmelCase							 =					"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

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())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)

