





"""simple docstring"""






import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu



class 					__UpperCAmelCase							(						nn.Module  ):
				def __init__(		self    :			Any    )	->	int:



							'''simple docstring'''



							super().__init__()
							a__  :	Union[str, Any]		 =			nn.Linear(3	, 4    )
							a__  :	Dict		 =			nn.BatchNormad(4    )
							a__  :	Union[str, Any]		 =			nn.Linear(4	, 5    )






				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[Any]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.lineara(self.batchnorm(self.lineara(a_    )    )    )



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			Dict	, *a_    :			int	, **a_    :			int    )	->	List[str]:



							'''simple docstring'''



							return (args[0] + 1,) + args[1:], kwargs



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			Any	, a_    :			str    )	->	Dict:



							'''simple docstring'''



							return output + 1



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Any    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			ModelForTest()
							a__  :	Optional[int]		 =			ModelHook()

							add_hook_to_module(a_	, a_    )
							self.assertEqual(test_model._hf_hook	, a_    )
							self.assertTrue(hasattr(a_	, "_old_forward"    )    )

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__	, "forward"    )
							self.assertListEqual(list(inspect.signature(test_model.forward    ).parameters    )	, ["x"]    )

							remove_hook_from_module(a_    )
							self.assertFalse(hasattr(a_	, "_hf_hook"    )    )
							self.assertFalse(hasattr(a_	, "_old_forward"    )    )
				def       UpperCAmelCase      (		self    :			str    )	->	str:



							'''simple docstring'''



							a__  :	Any		 =			ModelForTest()
							a__  :	Optional[int]		 =			ModelHook()

							add_hook_to_module(a_	, a_    )
							add_hook_to_module(a_	, a_	, append=a_    )

							self.assertEqual(isinstance(test_model._hf_hook	, a_    )	, a_    )
							self.assertEqual(len(test_model._hf_hook.hooks    )	, 2    )
							self.assertTrue(hasattr(a_	, "_old_forward"    )    )

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__	, "forward"    )
							self.assertListEqual(list(inspect.signature(test_model.forward    ).parameters    )	, ["x"]    )

							remove_hook_from_module(a_    )
							self.assertFalse(hasattr(a_	, "_hf_hook"    )    )
							self.assertFalse(hasattr(a_	, "_old_forward"    )    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			ModelForTest()
							a__  :	Union[str, Any]		 =			torch.randn(2	, 3    )
							a__  :	Optional[int]		 =			test_model(x + 1    )
							a__  :	List[str]		 =			test_model(x + 2    )

							a__  :	Tuple		 =			PreForwardHook()
							add_hook_to_module(a_	, a_    )
							a__  :	Dict		 =			test_model(a_    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )

							# Attaching a hook to a model when it already has one replaces, does not chain
							a__  :	Any		 =			PreForwardHook()
							add_hook_to_module(a_	, a_    )
							a__  :	List[Any]		 =			test_model(a_    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )

							# You need to use the sequential hook to chain two or more hooks
							a__  :	List[str]		 =			SequentialHook(PreForwardHook()	, PreForwardHook()    )
							add_hook_to_module(a_	, a_    )

							a__  :	Dict		 =			test_model(a_    )
							assert torch.allclose(a_	, a_	, atol=1E-5    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			ModelForTest()
							a__  :	List[Any]		 =			torch.randn(2	, 3    )
							a__  :	Union[str, Any]		 =			test_model(a_    )

							a__  :	Any		 =			PostForwardHook()
							add_hook_to_module(a_	, a_    )
							a__  :	int		 =			test_model(a_    )
							self.assertTrue(torch.allclose(a_	, output + 1	, atol=1E-5    )    )

							# Attaching a hook to a model when it already has one replaces, does not chain
							a__  :	List[str]		 =			PostForwardHook()
							add_hook_to_module(a_	, a_    )
							a__  :	Optional[Any]		 =			test_model(a_    )
							self.assertTrue(torch.allclose(a_	, output + 1	, atol=1E-5    )    )

							# You need to use the sequential hook to chain two or more hooks
							a__  :	Union[str, Any]		 =			SequentialHook(PostForwardHook()	, PostForwardHook()    )
							add_hook_to_module(a_	, a_    )

							a__  :	Dict		 =			test_model(a_    )
							assert torch.allclose(a_	, output + 2	, atol=1E-5    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			ModelForTest()
							a__  :	Dict		 =			torch.randn(2	, 3    )
							a__  :	Any		 =			test_model(a_    )

							a__  :	Optional[int]		 =			PostForwardHook()
							add_hook_to_module(a_	, a_    )
							a__  :	List[str]		 =			test_model(a_    )
							self.assertTrue(torch.allclose(a_	, output + 1    )    )
							self.assertTrue(outputa.requires_grad    )

							a__  :	Any		 =			True
							a__  :	Dict		 =			test_model(a_    )
							self.assertFalse(outputa.requires_grad    )
				@require_multi_gpu
				def       UpperCAmelCase      (		self    :			int    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			ModelForTest()
							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# This will move each submodule on different devices
							add_hook_to_module(model.lineara	, AlignDevicesHook(execution_device=0    )    )
							add_hook_to_module(model.batchnorm	, AlignDevicesHook(execution_device=0    )    )
							add_hook_to_module(model.lineara	, AlignDevicesHook(execution_device=1    )    )

							self.assertEqual(model.lineara.weight.device	, torch.device(0    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device(0    )    )
							self.assertEqual(model.batchnorm.running_mean.device	, torch.device(0    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device(1    )    )

							# We can still make a forward pass. The input does not need to be on any particular device
							a__  :	Optional[Any]		 =			torch.randn(2	, 3    )
							a__  :	Optional[int]		 =			model(a_    )
							self.assertEqual(output.device	, torch.device(1    )    )

							# We can add a general hook to put back output on same device as input.
							add_hook_to_module(a_	, AlignDevicesHook(io_same_device=a_    )    )
							a__  :	Dict		 =			torch.randn(2	, 3    ).to(0    )
							a__  :	Any		 =			model(a_    )
							self.assertEqual(output.device	, torch.device(0    )    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# This will move each submodule on different devices
							a__  :	int		 =			{"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

							add_hook_to_module(model.lineara	, AlignDevicesHook(**a_    )    )
							add_hook_to_module(model.batchnorm	, AlignDevicesHook(**a_    )    )
							add_hook_to_module(model.lineara	, AlignDevicesHook(**a_    )    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							a__  :	Dict		 =			torch.device(hook_kwargs["execution_device"]    )
							self.assertEqual(model.batchnorm.running_mean.device	, a_    )

							a__  :	Optional[Any]		 =			torch.randn(2	, 3    )
							a__  :	int		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara    )
							remove_hook_from_module(model.batchnorm    )
							remove_hook_from_module(model.lineara    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# Now test with buffers included in the offload
							a__  :	str		 =			{
							    "execution_device": 0 if torch.cuda.is_available() else "cpu",
							    "offload": True,
							    "offload_buffers": True,
							}

							add_hook_to_module(model.lineara	, AlignDevicesHook(**a_    )    )
							add_hook_to_module(model.batchnorm	, AlignDevicesHook(**a_    )    )
							add_hook_to_module(model.lineara	, AlignDevicesHook(**a_    )    )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.running_mean.device	, torch.device("meta"    )    )

							a__  :	Dict		 =			torch.randn(2	, 3    )
							a__  :	Optional[int]		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara    )
							remove_hook_from_module(model.batchnorm    )
							remove_hook_from_module(model.lineara    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
				def       UpperCAmelCase      (		self    :			int    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# This will move each submodule on different devices
							a__  :	Any		 =			0 if torch.cuda.is_available() else "cpu"
							attach_align_device_hook(a_	, execution_device=a_	, offload=a_    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							a__  :	Union[str, Any]		 =			torch.device(a_    )
							self.assertEqual(model.batchnorm.running_mean.device	, a_    )

							a__  :	Any		 =			torch.randn(2	, 3    )
							a__  :	int		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(a_    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# Now test with buffers included in the offload
							attach_align_device_hook(a_	, execution_device=a_	, offload=a_	, offload_buffers=a_    )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.running_mean.device	, torch.device("meta"    )    )

							a__  :	List[Any]		 =			torch.randn(2	, 3    )
							a__  :	List[Any]		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(a_    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )






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



							'''simple docstring'''



							a__  :	Optional[Any]		 =			ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# This will move each submodule on different devices
							a__  :	List[str]		 =			0 if torch.cuda.is_available() else "cpu"
							attach_align_device_hook(
							    a_	, execution_device=a_	, offload=a_	, weights_map=model.state_dict()    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							a__  :	int		 =			torch.device(a_    )
							self.assertEqual(model.batchnorm.running_mean.device	, a_    )

							a__  :	Union[str, Any]		 =			torch.randn(2	, 3    )
							a__  :	Tuple		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(a_    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )

							# Now test with buffers included in the offload
							attach_align_device_hook(
							    a_	, execution_device=a_	, offload=a_	, weights_map=model.state_dict()	, offload_buffers=a_	, )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("meta"    )    )
							self.assertEqual(model.batchnorm.running_mean.device	, torch.device("meta"    )    )

							a__  :	Dict		 =			torch.randn(2	, 3    )
							a__  :	Dict		 =			model(a_    )
							self.assertEqual(output.device	, a_    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(a_    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.batchnorm.weight.device	, torch.device("cpu"    )    )
							self.assertEqual(model.lineara.weight.device	, torch.device("cpu"    )    )






"""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  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	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
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			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__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

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

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			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       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			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       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



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

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

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

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

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



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

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

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

							a__  :	Any		 =			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       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

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

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

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

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



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

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

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

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






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



							'''simple docstring'''



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

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

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