

'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Union[str, Any]     	= ViTImageProcessor if is_vision_available() else None
  @property
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						(3, 3_2, 1_2_8)
    UpperCAmelCase    				=						tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase    				=						['[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    				=						dict(zip(a_ ,			range(len(a_  )  )  )  )

    UpperCAmelCase    				=						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    				=						{
        'do_normalize': False,
        'do_resize': True,
        'image_processor_type': 'ViTImageProcessor',
        'resample': 3,
        'size': {'height': 3_2, 'width': 1_2_8},
    }
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			a_  )
    with open(self.image_processor_file ,			'w' ,			encoding='utf-8'  ) as fp:
      json.dump(a_ ,			a_  )
  def     snake_case_      (  self ,			**a_  )				->					Optional[Any]:


    """simple docstring"""
    return MgpstrTokenizer.from_pretrained(self.tmpdirname ,			**a_  )
  def     snake_case_      (  self ,			**a_  )				->					int:


    """simple docstring"""
    return ViTImageProcessor.from_pretrained(self.tmpdirname ,			**a_  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						np.random.randint(2_5_5 ,			size=(3, 3_0, 4_0_0) ,			dtype=np.uinta  )

    UpperCAmelCase    				=						Image.fromarray(np.moveaxis(a_ ,			0 ,			-1  )  )

    return image_input
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()
    UpperCAmelCase    				=						self.get_image_processor()

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


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()
    UpperCAmelCase    				=						self.get_image_processor()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )
    processor.save_pretrained(self.tmpdirname  )

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )
    UpperCAmelCase    				=						self.get_image_processor(do_normalize=a_ ,			padding_value=1.0  )

    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						self.prepare_image_inputs()

    UpperCAmelCase    				=						image_processor(a_ ,			return_tensors='np'  )
    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						'test'

    UpperCAmelCase    				=						processor(text=a_  )

    UpperCAmelCase    				=						tokenizer(a_  )
    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key]  )
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						'test'
    UpperCAmelCase    				=						self.prepare_image_inputs()

    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase    				=						processor.char_decode(a_  )
    UpperCAmelCase    				=						tokenizer.batch_decode(a_  )
    UpperCAmelCase    				=						[seq.replace(' ' ,			''  ) for seq in decoded_tok]

    self.assertListEqual(a_ ,			a_  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.prepare_image_inputs()

    UpperCAmelCase    				=						processor(text=a_ ,			images=a_  )

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





  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_image_processor()
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						MgpstrProcessor(tokenizer=a_ ,			image_processor=a_  )

    UpperCAmelCase    				=						torch.randn(1 ,			2_7 ,			3_8  )
    UpperCAmelCase    				=						torch.randn(1 ,			2_7 ,			5_0_2_5_7  )
    UpperCAmelCase    				=						torch.randn(1 ,			2_7 ,			3_0_5_2_2  )

    UpperCAmelCase    				=						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'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):
  return not any(
      neighbour == 1 and colored_vertices[i] == color
      for i, neighbour in enumerate(SCREAMING_SNAKE_CASE    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):

  # Base Case
  if index == len(SCREAMING_SNAKE_CASE    ):
    return True

  # Recursive Step
  for i in range(SCREAMING_SNAKE_CASE    ):
    if valid_coloring(graph[index]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ):
      # Color current vertex
      UpperCAmelCase    				=						i
      # Validate coloring
      if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, index + 1    ):
        return True
      # Backtrack
      UpperCAmelCase    				=						-1
  return False



def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						[-1] * len(SCREAMING_SNAKE_CASE    )

  if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, 0    ):
    return colored_vertices

  return []


