'''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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						ViTImageProcessor if is_vision_available() else None




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Any							      =    (3, 32, 128)
             _UpperCAmelCase     :  Any							      =    tempfile.mkdtemp()

             # fmt: off
             _UpperCAmelCase     :  Dict							      =    ["[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     :  List[str]							      =    dict(zip(A						,					range(len(A       )       )       )       )

             _UpperCAmelCase     :  Optional[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"       )

             _UpperCAmelCase     :  str							      =    {
                 "do_normalize": False,
                 "do_resize": True,
                 "image_processor_type": "ViTImageProcessor",
                 "resample": 3,
                 "size": {"height": 32, "width": 128},
             }
             _UpperCAmelCase     :  List[str]							      =    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						:				Optional[Any]						,					**A						:				List[Any]       ):
             return MgpstrTokenizer.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Dict						,					**A						:				List[str]       ):
             return ViTImageProcessor.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				List[Any]       ):
             shutil.rmtree(self.tmpdirname       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    np.random.randint(255						,					size=(3, 30, 400)						,					dtype=np.uinta       )

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

             return image_input




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Tuple							      =    self.get_tokenizer()
             _UpperCAmelCase     :  List[Any]							      =    self.get_image_processor()

             _UpperCAmelCase     :  List[Any]							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )
             processor.save_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  Union[str, 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      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    self.get_tokenizer()
             _UpperCAmelCase     :  Optional[Any]							      =    self.get_image_processor()

             _UpperCAmelCase     :  str							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )
             processor.save_pretrained(self.tmpdirname       )

             _UpperCAmelCase     :  Dict							      =    self.get_tokenizer(bos_token="(BOS)"						,					eos_token="(EOS)"       )
             _UpperCAmelCase     :  Dict							      =    self.get_image_processor(do_normalize=A						,					padding_value=1.0       )

             _UpperCAmelCase     :  Any							      =    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						:				int       ):
             _UpperCAmelCase     :  int							      =    self.get_image_processor()
             _UpperCAmelCase     :  str							      =    self.get_tokenizer()

             _UpperCAmelCase     :  Optional[int]							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  int							      =    self.prepare_image_inputs()

             _UpperCAmelCase     :  str							      =    image_processor(A						,					return_tensors="np"       )
             _UpperCAmelCase     :  List[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      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    self.get_image_processor()
             _UpperCAmelCase     :  Any							      =    self.get_tokenizer()

             _UpperCAmelCase     :  str							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Any							      =    "test"

             _UpperCAmelCase     :  Tuple							      =    processor(text=A       )

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




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Dict							      =    self.get_image_processor()
             _UpperCAmelCase     :  Tuple							      =    self.get_tokenizer()

             _UpperCAmelCase     :  Optional[Any]							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Any							      =    "test"
             _UpperCAmelCase     :  Union[str, Any]							      =    self.prepare_image_inputs()

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

             _UpperCAmelCase     :  str							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  List[Any]							      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase     :  Optional[int]							      =    processor.char_decode(A       )
             _UpperCAmelCase     :  Tuple							      =    tokenizer.batch_decode(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    [seq.replace(" "						,					""       ) for seq in decoded_tok]

             self.assertListEqual(A						,					A       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    self.get_image_processor()
             _UpperCAmelCase     :  str							      =    self.get_tokenizer()

             _UpperCAmelCase     :  List[str]							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Any							      =    None
             _UpperCAmelCase     :  int							      =    self.prepare_image_inputs()

             _UpperCAmelCase     :  Dict							      =    processor(text=A						,					images=A       )

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





      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.get_image_processor()
             _UpperCAmelCase     :  List[str]							      =    self.get_tokenizer()

             _UpperCAmelCase     :  str							      =    MgpstrProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  List[Any]							      =    torch.randn(1						,					27						,					38       )
             _UpperCAmelCase     :  List[Any]							      =    torch.randn(1						,					27						,					50257       )
             _UpperCAmelCase     :  List[str]							      =    torch.randn(1						,					27						,					30522       )

             _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"]       )


'''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()

