

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			UpperCAmelCase_       :				str		   =      False

if is_vision_available():
			from PIL import Image

			from transformers import (
			    AutoProcessor,
			    PixaStructImageProcessor,
			    PixaStructProcessor,
			    PreTrainedTokenizerFast,
			    TaTokenizer,
			)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,			reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	,			)
@require_vision
@require_torch
class     lowerCamelCase_			(  unittest.TestCase  ):




	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
		__A   : Any								= tempfile.mkdtemp()

		__A   : Tuple								= PixaStructImageProcessor()
		__A   : Optional[Any]								= TaTokenizer.from_pretrained("""t5-small"""       )

		__A   : Any								= PixaStructProcessor(_lowerCamelCase				,			_lowerCamelCase       )

		processor.save_pretrained(self.tmpdirname       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    Dict       ):
		return AutoProcessor.from_pretrained(self.tmpdirname				,			**_lowerCamelCase       ).tokenizer




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    List[Any]       ):
		return AutoProcessor.from_pretrained(self.tmpdirname				,			**_lowerCamelCase       ).image_processor




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




	def 					lowerCAmelCase_					(    self	:    Any       ):
		__A   : Optional[Any]								= [np.random.randint(255				,			size=(3, 30, 400)				,			dtype=np.uinta       )]

		__A   : Dict								= [Image.fromarray(np.moveaxis(_lowerCamelCase				,			0				,			-1       )       ) for x in image_inputs]

		return image_inputs




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : int								= PixaStructProcessor(tokenizer=self.get_tokenizer()				,			image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

		__A   : str								= self.get_tokenizer(bos_token="""(BOS)"""				,			eos_token="""(EOS)"""       )
		__A   : int								= self.get_image_processor(do_normalize=_lowerCamelCase				,			padding_value=1.0       )

		__A   : Any								= PixaStructProcessor.from_pretrained(
		    self.tmpdirname				,			bos_token="""(BOS)"""				,			eos_token="""(EOS)"""				,			do_normalize=_lowerCamelCase				,			padding_value=1.0       )

		self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()       )
		self.assertIsInstance(processor.tokenizer				,			_lowerCamelCase       )

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




	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
		__A   : str								= self.get_image_processor()
		__A   : List[str]								= self.get_tokenizer()

		__A   : str								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : List[Any]								= self.prepare_image_inputs()

		__A   : List[str]								= image_processor(_lowerCamelCase				,			return_tensors="""np"""       )
		__A   : List[str]								= processor(images=_lowerCamelCase				,			return_tensors="""np"""       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1e-2       )




	def 					lowerCAmelCase_					(    self	:    Any       ):
		__A   : str								= self.get_image_processor()
		__A   : Optional[Any]								= self.get_tokenizer()

		__A   : Union[str, Any]								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : Optional[Any]								= """lower newer"""

		__A   : List[Any]								= processor(text=_lowerCamelCase       )

		__A   : int								= tokenizer(_lowerCamelCase				,			return_token_type_ids=_lowerCamelCase				,			add_special_tokens=_lowerCamelCase       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : Any								= self.get_image_processor()
		__A   : Optional[Any]								= self.get_tokenizer()

		__A   : int								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : Optional[int]								= """lower newer"""
		__A   : Union[str, Any]								= self.prepare_image_inputs()

		__A   : str								= processor(text=_lowerCamelCase				,			images=_lowerCamelCase       )

		self.assertListEqual(
		    list(inputs.keys()       )				,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]       )

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




	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
		__A   : Any								= self.get_image_processor()
		__A   : Dict								= self.get_tokenizer()

		__A   : Dict								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : Optional[Any]								= """lower newer"""
		__A   : Optional[int]								= self.prepare_image_inputs()

		__A   : Tuple								= processor(text=_lowerCamelCase				,			images=_lowerCamelCase       )

		__A   : Optional[Any]								= [512, 1024, 2048, 4096]
		__A   : List[str]								= [770, 770, 770, 770]
		# with text
		for i, max_patch in enumerate(_lowerCamelCase       ):
			__A   : List[Any]								= processor(text=_lowerCamelCase				,			images=_lowerCamelCase				,			max_patches=_lowerCamelCase       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[0]				,			_lowerCamelCase       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[1]				,			expected_hidden_size[i]       )

		# without text input
		for i, max_patch in enumerate(_lowerCamelCase       ):
			__A   : Dict								= processor(images=_lowerCamelCase				,			max_patches=_lowerCamelCase       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[0]				,			_lowerCamelCase       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[1]				,			expected_hidden_size[i]       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : int								= self.get_image_processor()
		__A   : List[Any]								= self.get_tokenizer()

		__A   : Tuple								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : Optional[int]								= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		__A   : int								= processor.batch_decode(_lowerCamelCase       )
		__A   : Dict								= tokenizer.batch_decode(_lowerCamelCase       )

		self.assertListEqual(_lowerCamelCase				,			_lowerCamelCase       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : List[str]								= self.get_image_processor()
		__A   : Any								= self.get_tokenizer()

		__A   : Optional[Any]								= PixaStructProcessor(tokenizer=_lowerCamelCase				,			image_processor=_lowerCamelCase       )

		__A   : Dict								= """lower newer"""
		__A   : Dict								= self.prepare_image_inputs()

		__A   : Dict								= processor(text=_lowerCamelCase				,			images=_lowerCamelCase       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(
		    list(inputs.keys()       )				,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]       )

		__A   : Optional[int]								= processor(text=_lowerCamelCase       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(list(inputs.keys()       )				,			["""input_ids""", """attention_mask"""]       )








import math






class     lowerCamelCase_			:




	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
		__A   : Union[str, Any]								= x
		__A   : Union[str, Any]								= y
		__A   : Optional[int]								= step_size
		__A   : Dict								= function_to_optimize




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		return self.function(self.x				,			self.y       )




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Dict								= self.step_size
		return [
		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
		    for x, y in (
		        (self.x - step_size, self.y - step_size),
		        (self.x - step_size, self.y),
		        (self.x - step_size, self.y + step_size),
		        (self.x, self.y - step_size),
		        (self.x, self.y + step_size),
		        (self.x + step_size, self.y - step_size),
		        (self.x + step_size, self.y),
		        (self.x + step_size, self.y + step_size),
		    )
		]




	def __hash__(    self	:    str       ):
		return hash(str(self       )       )




	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
		if isinstance(__A				,			__A       ):
			return hash(str(self       )       ) == hash(str(__A       )       )
		return False




	def __str__(    self	:    List[Any]       ):
		return F"""x: {self.x} y: {self.y}"""






def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
	__A   : int								= search_prob
	__A   : List[str]								= []  # list to store the current score at each iteration
	__A   : Optional[Any]								= 0
	__A   : Any								= False
	__A   : Dict								= set()
	while not solution_found and iterations < max_iter:
		visited.add(a__					)
		iterations += 1
		__A   : Union[str, Any]								= current_state.score()
		scores.append(a__					)
		__A   : List[Any]								= current_state.get_neighbors()
		__A   : Tuple								= -math.inf
		__A   : str								= math.inf
		__A   : Optional[int]								= None  # to hold the next best neighbor
		for neighbor in neighbors:
			if neighbor in visited:
				continue  # do not want to visit the same state again
			if (
			    neighbor.x > max_x
			    or neighbor.x < min_x
			    or neighbor.y > max_y
			    or neighbor.y < min_y
			):
				continue  # neighbor outside our bounds
			__A   : int								= neighbor.score() - current_score
			if find_max:  # finding max
				# going to direction with greatest ascent
				if change > max_change and change > 0:
					__A   : str								= change
					__A   : Optional[int]								= neighbor
			else:  # finding min
				# to direction with greatest descent
				if change < min_change and change < 0:
					__A   : List[Any]								= change
					__A   : List[str]								= neighbor
		if next_state is not None:
			# we found at least one neighbor which improved the current state
			__A   : Tuple								= next_state
		else:
			# since we have no neighbor that improves the solution we stop the search
			__A   : str								= True

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(a__					) ,a__					)
		plt.xlabel("""Iterations"""					)
		plt.ylabel("""Function values"""					)
		plt.show()

	return current_state


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
				return (x**2) + (y**2)

			# starting the problem with initial coordinates (3, 4)
			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)

			# starting the problem with initial coordinates (12, 47)
			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(
			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
			)
			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
				return (3 * x**2) - (6 * y)

			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)







			print(
			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)





