






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   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    str       						=  ViTImageProcessor if is_vision_available() else None



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      str            =						(3, 3_2, 1_2_8)
							UpperCAmelCase_    :      int            =						tempfile.mkdtemp()

							# fmt: off
							UpperCAmelCase_    :      int            =						['[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[int]            =						dict(zip(_UpperCamelCase				,		range(len(_UpperCamelCase   )   )   )   )

							UpperCAmelCase_    :      List[str]            =						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(_UpperCamelCase   ) + '\n'   )

							UpperCAmelCase_    :      Dict            =						{
							    'do_normalize': False,
							    'do_resize': True,
							    'image_processor_type': 'ViTImageProcessor',
							    'resample': 3,
							    'size': {'height': 3_2, 'width': 1_2_8},
							}
							UpperCAmelCase_    :      Dict            =						os.path.join(self.tmpdirname				,		_UpperCamelCase   )
							with open(self.image_processor_file				,		'w'				,		encoding='utf-8'   ) as fp:
												json.dump(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							shutil.rmtree(self.tmpdirname   )



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      List[str]            =						np.random.randint(2_5_5				,		size=(3, 3_0, 4_0_0)				,		dtype=np.uinta   )

							UpperCAmelCase_    :      Any            =						Image.fromarray(np.moveaxis(_UpperCamelCase				,		0				,		-1   )   )

							return image_input



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      int            =						self.get_tokenizer()
							UpperCAmelCase_    :      int            =						self.get_image_processor()

							UpperCAmelCase_    :      Optional[Any]            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )
							processor.save_pretrained(self.tmpdirname   )
							UpperCAmelCase_    :      int            =						MgpstrProcessor.from_pretrained(self.tmpdirname				,		use_fast=_UpperCamelCase   )

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

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Optional[int]            =						self.get_tokenizer()
							UpperCAmelCase_    :      List[str]            =						self.get_image_processor()

							UpperCAmelCase_    :      Dict            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )
							processor.save_pretrained(self.tmpdirname   )

							UpperCAmelCase_    :      str            =						self.get_tokenizer(bos_token='(BOS)'				,		eos_token='(EOS)'   )
							UpperCAmelCase_    :      Optional[int]            =						self.get_image_processor(do_normalize=_UpperCamelCase				,		padding_value=1.0   )

							UpperCAmelCase_    :      List[str]            =						MgpstrProcessor.from_pretrained(
							    self.tmpdirname				,		bos_token='(BOS)'				,		eos_token='(EOS)'				,		do_normalize=_UpperCamelCase				,		padding_value=1.0   )

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

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



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Union[str, Any]            =						self.get_image_processor()
							UpperCAmelCase_    :      Tuple            =						self.get_tokenizer()

							UpperCAmelCase_    :      str            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						self.prepare_image_inputs()

							UpperCAmelCase_    :      List[Any]            =						image_processor(_UpperCamelCase				,		return_tensors='np'   )
							UpperCAmelCase_    :      Tuple            =						processor(images=_UpperCamelCase				,		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   )	->							Optional[Any]:
							UpperCAmelCase_    :      Dict            =						self.get_image_processor()
							UpperCAmelCase_    :      Union[str, Any]            =						self.get_tokenizer()

							UpperCAmelCase_    :      str            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						'test'

							UpperCAmelCase_    :      Union[str, Any]            =						processor(text=_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						tokenizer(_UpperCamelCase   )
							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]				,		encoded_processor[key]   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Tuple            =						self.get_image_processor()
							UpperCAmelCase_    :      Any            =						self.get_tokenizer()

							UpperCAmelCase_    :      Dict            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						'test'
							UpperCAmelCase_    :      str            =						self.prepare_image_inputs()

							UpperCAmelCase_    :      str            =						processor(text=_UpperCamelCase				,		images=_UpperCamelCase   )

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

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



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      List[Any]            =						self.get_image_processor()
							UpperCAmelCase_    :      str            =						self.get_tokenizer()

							UpperCAmelCase_    :      Optional[int]            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

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

							UpperCAmelCase_    :      Tuple            =						processor.char_decode(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						tokenizer.batch_decode(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						[seq.replace(' '				,		''   ) for seq in decoded_tok]

							self.assertListEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Dict            =						self.get_image_processor()
							UpperCAmelCase_    :      str            =						self.get_tokenizer()

							UpperCAmelCase_    :      Tuple            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

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

							UpperCAmelCase_    :      Optional[int]            =						processor(text=_UpperCamelCase				,		images=_UpperCamelCase   )

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



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Any            =						self.get_image_processor()
							UpperCAmelCase_    :      Optional[Any]            =						self.get_tokenizer()

							UpperCAmelCase_    :      Optional[int]            =						MgpstrProcessor(tokenizer=_UpperCamelCase				,		image_processor=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.randn(1				,		2_7				,		3_8   )
							UpperCAmelCase_    :      List[Any]            =						torch.randn(1				,		2_7				,		5_0_2_5_7   )
							UpperCAmelCase_    :      List[Any]            =						torch.randn(1				,		2_7				,		3_0_5_2_2   )

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

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










from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




