"""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__		:    Union[str, Any]   =  ViTImageProcessor if is_vision_available() else None


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


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Union[str, Any]       =	(3, 3_2, 1_2_8)
             lowercase__			:		Dict       =	tempfile.mkdtemp()

             # fmt: off
             lowercase__			:		Tuple       =	['[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
             lowercase__			:		Union[str, Any]       =	dict(zip(a		,			range(len(a							)							)							)							)

             lowercase__			:		Optional[Any]       =	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'							)

             lowercase__			:		Dict       =	{
                 'do_normalize': False,
                 'do_resize': True,
                 'image_processor_type': 'ViTImageProcessor',
                 'resample': 3,
                 'size': {'height': 3_2, 'width': 1_2_8},
             }
             lowercase__			:		Dict       =	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		,			**a							)       ->					Union[str, Any]:
             return MgpstrTokenizer.from_pretrained(self.tmpdirname		,			**a							)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Any:
             return ViTImageProcessor.from_pretrained(self.tmpdirname		,			**a							)


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


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

             lowercase__			:		Union[str, Any]       =	Image.fromarray(np.moveaxis(a		,			0		,			-1							)							)

             return image_input


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Optional[int]       =	self.get_tokenizer()
             lowercase__			:		Optional[Any]       =	self.get_image_processor()

             lowercase__			:		Optional[Any]       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)
             processor.save_pretrained(self.tmpdirname							)
             lowercase__			:		Dict       =	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							)       ->					List[str]:
             lowercase__			:		Tuple       =	self.get_tokenizer()
             lowercase__			:		Dict       =	self.get_image_processor()

             lowercase__			:		Optional[Any]       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)
             processor.save_pretrained(self.tmpdirname							)

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

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

             lowercase__			:		Tuple       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

             lowercase__			:		str       =	self.prepare_image_inputs()

             lowercase__			:		List[str]       =	image_processor(a		,			return_tensors='np'							)
             lowercase__			:		str       =	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							)       ->					Any:
             lowercase__			:		Dict       =	self.get_image_processor()
             lowercase__			:		Any       =	self.get_tokenizer()

             lowercase__			:		Union[str, Any]       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

             lowercase__			:		List[str]       =	'test'

             lowercase__			:		str       =	processor(text=a							)

             lowercase__			:		List[Any]       =	tokenizer(a							)
             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]		,			encoded_processor[key]							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Any       =	self.get_image_processor()
             lowercase__			:		List[Any]       =	self.get_tokenizer()

             lowercase__			:		int       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

             lowercase__			:		Union[str, Any]       =	'test'
             lowercase__			:		List[Any]       =	self.prepare_image_inputs()

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

             lowercase__			:		List[str]       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

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

             lowercase__			:		List[str]       =	processor.char_decode(a							)
             lowercase__			:		Union[str, Any]       =	tokenizer.batch_decode(a							)
             lowercase__			:		Any       =	[seq.replace(' '		,			''							) for seq in decoded_tok]

             self.assertListEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		List[Any]       =	self.get_image_processor()
             lowercase__			:		Tuple       =	self.get_tokenizer()

             lowercase__			:		Dict       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

             lowercase__			:		Optional[Any]       =	None
             lowercase__			:		int       =	self.prepare_image_inputs()

             lowercase__			:		Optional[int]       =	processor(text=a		,			images=a							)

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


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		List[str]       =	self.get_image_processor()
             lowercase__			:		Any       =	self.get_tokenizer()

             lowercase__			:		str       =	MgpstrProcessor(tokenizer=a		,			image_processor=a							)

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

             lowercase__			:		str       =	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'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

