
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:
	__a							  =						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        UpperCamelCase__(    unittest.TestCase	):

	"""simple docstring"""

	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			A				=tempfile.mkdtemp()

			A				=PixaStructImageProcessor()
			A				=TaTokenizer.from_pretrained("t5-small" )

			A				=PixaStructProcessor(UpperCAmelCase_							,			UpperCAmelCase_ )

			processor.save_pretrained(self.tmpdirname )







	def        _a					(      self				:			Any							,			**snake_case__				:			Any ):
			"""simple docstring"""





			return AutoProcessor.from_pretrained(self.tmpdirname							,			**UpperCAmelCase_ ).tokenizer







	def        _a					(      self				:			Optional[Any]							,			**snake_case__				:			Any ):
			"""simple docstring"""





			return AutoProcessor.from_pretrained(self.tmpdirname							,			**UpperCAmelCase_ ).image_processor







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			shutil.rmtree(self.tmpdirname )







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			A				=[np.random.randint(2_55							,			size=(3, 30, 4_00)							,			dtype=np.uinta )]

			A				=[Image.fromarray(np.moveaxis(UpperCAmelCase_							,			0							,			-1 ) ) for x in image_inputs]

			return image_inputs







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=PixaStructProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor() )
			processor.save_pretrained(self.tmpdirname )

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

			A				=PixaStructProcessor.from_pretrained(
			    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=UpperCAmelCase_							,			padding_value=1.0 )

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

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







	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

			A				=self.prepare_image_inputs()

			A				=image_processor(UpperCAmelCase_							,			return_tensors="np" )
			A				=processor(images=UpperCAmelCase_							,			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        _a					(      self				:			Dict ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

			A				="lower newer"

			A				=processor(text=UpperCAmelCase_ )

			A				=tokenizer(UpperCAmelCase_							,			return_token_type_ids=UpperCAmelCase_							,			add_special_tokens=UpperCAmelCase_ )

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







	def        _a					(      self				:			str ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

			A				="lower newer"
			A				=self.prepare_image_inputs()

			A				=processor(text=UpperCAmelCase_							,			images=UpperCAmelCase_ )

			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(UpperCAmelCase_ ):
					processor()







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

			A				="lower newer"
			A				=self.prepare_image_inputs()

			A				=processor(text=UpperCAmelCase_							,			images=UpperCAmelCase_ )

			A				=[5_12, 10_24, 20_48, 40_96]
			A				=[7_70, 7_70, 7_70, 7_70]
			# with text
			for i, max_patch in enumerate(UpperCAmelCase_ ):
					A				=processor(text=UpperCAmelCase_							,			images=UpperCAmelCase_							,			max_patches=UpperCAmelCase_ )
					self.assertEqual(inputs["flattened_patches"][0].shape[0]							,			UpperCAmelCase_ )
					self.assertEqual(inputs["flattened_patches"][0].shape[1]							,			expected_hidden_size[i] )

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







	def        _a					(      self				:			str ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

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

			A				=processor.batch_decode(UpperCAmelCase_ )
			A				=tokenizer.batch_decode(UpperCAmelCase_ )

			self.assertListEqual(UpperCAmelCase_							,			UpperCAmelCase_ )







	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			A				=self.get_image_processor()
			A				=self.get_tokenizer()

			A				=PixaStructProcessor(tokenizer=UpperCAmelCase_							,			image_processor=UpperCAmelCase_ )

			A				="lower newer"
			A				=self.prepare_image_inputs()

			A				=processor(text=UpperCAmelCase_							,			images=UpperCAmelCase_ )

			# 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				=processor(text=UpperCAmelCase_ )

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











'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




