





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  A__       (	unittest.TestCase      ):
      _UpperCAmelCase     :int						   =       ViTImageProcessor if is_vision_available() else None





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       (3, 32, 128)
       UpperCamelCase     : Any	     =       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     : Tuple	     =       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     : Union[str, Any]	     =       {
           "do_normalize": False,
           "do_resize": True,
           "image_processor_type": "ViTImageProcessor",
           "resample": 3,
           "size": {"height": 32, "width": 128},
       }
       UpperCamelCase     : int	     =       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_       ):
       '''simple docstring'''




       return MgpstrTokenizer.from_pretrained(self.tmpdirname	,							**A_       )





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained(self.tmpdirname	,							**A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       np.random.randint(255	,							size=(3, 30, 400)	,							dtype=np.uinta       )

       UpperCamelCase     : int	     =       Image.fromarray(np.moveaxis(A_	,							0	,							-1       )       )

       return image_input





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       self.get_tokenizer()
       UpperCamelCase     : int	     =       self.get_image_processor()

       UpperCamelCase     : int	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )
       processor.save_pretrained(self.tmpdirname       )
       UpperCamelCase     : 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       ):
       '''simple docstring'''




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

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

       UpperCamelCase     : int	     =       self.get_tokenizer(bos_token="(BOS)"	,							eos_token="(EOS)"       )
       UpperCamelCase     : int	     =       self.get_image_processor(do_normalize=A_	,							padding_value=1.0       )

       UpperCamelCase     : 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       ):
       '''simple docstring'''




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

       UpperCamelCase     : List[str]	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : str	     =       self.prepare_image_inputs()

       UpperCamelCase     : Union[str, Any]	     =       image_processor(A_	,							return_tensors="np"       )
       UpperCamelCase     : 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       ):
       '''simple docstring'''




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

       UpperCamelCase     : str	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : int	     =       "test"

       UpperCamelCase     : Any	     =       processor(text=A_       )

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     : Optional[int]	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Optional[Any]	     =       "test"
       UpperCamelCase     : List[str]	     =       self.prepare_image_inputs()

       UpperCamelCase     : Optional[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       ):
       '''simple docstring'''




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

       UpperCamelCase     : Any	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : int	     =       [[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(A_       )
       UpperCamelCase     : Any	     =       tokenizer.batch_decode(A_       )
       UpperCamelCase     : int	     =       [seq.replace(" "	,							""       ) for seq in decoded_tok]

       self.assertListEqual(A_	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       self.get_image_processor()
       UpperCamelCase     : Any	     =       self.get_tokenizer()

       UpperCamelCase     : str	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Dict	     =       None
       UpperCamelCase     : int	     =       self.prepare_image_inputs()

       UpperCamelCase     : List[str]	     =       processor(text=A_	,							images=A_       )

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.get_image_processor()
       UpperCamelCase     : Optional[int]	     =       self.get_tokenizer()

       UpperCamelCase     : str	     =       MgpstrProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Union[str, Any]	     =       torch.randn(1	,							27	,							38       )
       UpperCamelCase     : Optional[int]	     =       torch.randn(1	,							27	,							5_0257       )
       UpperCamelCase     : Optional[Any]	     =       torch.randn(1	,							27	,							3_0522       )

       UpperCamelCase     : str	     =       processor.batch_decode([char_input, bpe_input, wp_input]       )

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







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			list[float]:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       coefficient_matrix.shape
 UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase     : List[Any]	     =       F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if colsa != 1:
  UpperCamelCase     : Any	     =       F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if rowsa != rowsa:
  UpperCamelCase     : Tuple	     =       (
      "Coefficient and constant matrices dimensions must be nxn and nx1 but "
      F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if len(_lowerCAmelCase					) != rowsa:
  UpperCamelCase     : Any	     =       (
      "Number of initial values must be equal to number of rows in coefficient "
      F"""matrix but received {len(_lowerCAmelCase					)} and {rowsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if iterations <= 0:
  raise ValueError("Iterations must be at least 1"					)

 UpperCamelCase     : NDArray[floataa]	     =       np.concatenate(
     (coefficient_matrix, constant_matrix)							,       axis=1					)

 UpperCamelCase     ,   UpperCamelCase     : str	     =       table.shape

 strictly_diagonally_dominant(_lowerCAmelCase					)

 # Iterates the whole matrix for given number of times
 for _ in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       []
  for row in range(_lowerCAmelCase					):
   UpperCamelCase     : Optional[int]	     =       0
   for col in range(_lowerCAmelCase					):
    if col == row:
     UpperCamelCase     : Union[str, Any]	     =       table[row][col]
    elif col == cols - 1:
     UpperCamelCase     : List[Any]	     =       table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase     : Dict	     =       (temp + val) / denom
   new_val.append(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       new_val

 return [float(_lowerCAmelCase					) for i in new_val]



def 		A_    (					_lowerCAmelCase					)						->			bool:
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       table.shape

 UpperCamelCase     : List[Any]	     =       True

 for i in range(0							,       _lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       0
  for j in range(0							,       cols - 1					):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()
