






'''simple docstring'''





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 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def _lowerCAmelCase     (    self:    Union[str, Any])  ->List[Any]:






								'''simple docstring'''


								a_				     =   tempfile.mkdtemp()

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

								a_				     =   PixaStructProcessor(a				,   a)

								processor.save_pretrained(self.tmpdirname)



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   **a:    Optional[int])  ->Tuple:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    int				,   **a:    List[Any])  ->Any:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Union[str, Any])  ->List[Any]:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase     (    self:    Tuple)  ->Optional[int]:






								'''simple docstring'''


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

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

								return image_inputs



		def _lowerCAmelCase     (    self:    str)  ->int:






								'''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=a				,   padding_value=1.0)

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

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

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



		def _lowerCAmelCase     (    self:    int)  ->List[Any]:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

								a_				     =   self.prepare_image_inputs()

								a_				     =   image_processor(a				,   return_tensors="np")
								a_				     =   processor(images=a				,   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:    Optional[Any])  ->Any:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

								a_				     =   "lower newer"

								a_				     =   processor(text=a)

								a_				     =   tokenizer(a				,   return_token_type_ids=a				,   add_special_tokens=a)

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



		def _lowerCAmelCase     (    self:    List[str])  ->Dict:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

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

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

								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(a):
														processor()



		def _lowerCAmelCase     (    self:    str)  ->int:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

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

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

								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(a):
														a_				     =   processor(text=a				,   images=a				,   max_patches=a)
														self.assertEqual(inputs["flattened_patches"][0].shape[0]				,   a)
														self.assertEqual(inputs["flattened_patches"][0].shape[1]				,   expected_hidden_size[i])

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



		def _lowerCAmelCase     (    self:    List[Any])  ->Dict:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

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

								a_				     =   processor.batch_decode(a)
								a_				     =   tokenizer.batch_decode(a)

								self.assertListEqual(a				,   a)




		def _lowerCAmelCase     (    self:    Union[str, Any])  ->List[Any]:






								'''simple docstring'''


								a_				     =   self.get_image_processor()
								a_				     =   self.get_tokenizer()

								a_				     =   PixaStructProcessor(tokenizer=a				,   image_processor=a)

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

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

								# 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=a)

								# 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, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



