





'''simple docstring'''







import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					snake_case_     :       Dict						=  False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor


class 					__a      (unittest.TestCase     ):

							def __init__(							self							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     str=7       ,     __magic_name__							:     int=3       ,     __magic_name__							:     List[Any]=18       ,     __magic_name__							:     List[Any]=30       ,     __magic_name__							:     Union[str, Any]=4_00       ,     __magic_name__							:     Union[str, Any]=None       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     List[str]=True       ,     __magic_name__							:     str=None       ,     )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  size if size is not None else {'''height''': 20, '''width''': 20}
													UpperCAmelCase_			:      List[str]     						=  parent
													UpperCAmelCase_			:      List[Any]     						=  batch_size
													UpperCAmelCase_			:      Dict     						=  num_channels
													UpperCAmelCase_			:      List[str]     						=  image_size
													UpperCAmelCase_			:      int     						=  min_resolution
													UpperCAmelCase_			:      int     						=  max_resolution
													UpperCAmelCase_			:      Tuple     						=  size
													UpperCAmelCase_			:      Tuple     						=  do_normalize
													UpperCAmelCase_			:      Union[str, Any]     						=  do_convert_rgb
													UpperCAmelCase_			:      Dict     						=  [5_12, 10_24, 20_48, 40_96]
													UpperCAmelCase_			:      Any     						=  patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
													UpperCAmelCase_			:      int     						=  Image.open(requests.get(__magic_name__       ,     stream=__magic_name__   ).raw   ).convert('''RGB'''   )
													return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,		)
@require_torch
@require_vision
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Optional[Any]									=       PixaStructImageProcessor if is_vision_available() else None

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







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  PixaStructImageProcessingTester(self   )

							@property
							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       str:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_normalize'''   )   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_convert_rgb'''   )   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.image_processor_tester.prepare_dummy_image()

													UpperCAmelCase_			:      Dict     						=  self.image_processing_class(**self.image_processor_dict   )
													UpperCAmelCase_			:      Optional[Any]     						=  20_48

													UpperCAmelCase_			:      int     						=  image_processor(__magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   )
													self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,     torch.tensor(0.0_6_0_6   )       ,     atol=1E-3       ,     rtol=1E-3   )   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													# Initialize image_processor
													UpperCAmelCase_			:      Any     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      str     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      int     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      List[Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Union[str, Any]     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )

							def     UpperCAmelCase__	(							self							:     str   )					->       Dict:







													"""simple docstring"""
													# Initialize image_processor
													UpperCAmelCase_			:      Optional[int]     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      Dict     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      int     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													UpperCAmelCase_			:      Optional[Any]     						=  True

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			with self.assertRaises(__magic_name__   ):
																									UpperCAmelCase_			:      int     						=  image_processor(
																									    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches

																			UpperCAmelCase_			:      Tuple     						=  '''Hello'''

																			UpperCAmelCase_			:      Optional[int]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__       ,     header_text=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Tuple     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__       ,     header_text=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )

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







													"""simple docstring"""
													# Initialize image_processor
													UpperCAmelCase_			:      Optional[int]     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random numpy tensors
													UpperCAmelCase_			:      Optional[Any]     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__       ,     numpify=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     np.ndarray   )

													UpperCAmelCase_			:      List[str]     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      Optional[Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      int     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													# Initialize image_processor
													UpperCAmelCase_			:      Tuple     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PyTorch tensors
													UpperCAmelCase_			:      Optional[Any]     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__       ,     torchify=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     torch.Tensor   )

													# Test not batched input
													UpperCAmelCase_			:      str     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      Union[str, Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Optional[int]     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,		)
@require_torch
@require_vision
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Optional[int]									=       PixaStructImageProcessor if is_vision_available() else None

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







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  PixaStructImageProcessingTester(self       ,     num_channels=4   )
													UpperCAmelCase_			:      Optional[int]     						=  3

							@property
							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Dict:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_normalize'''   )   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_convert_rgb'''   )   )



							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													# Initialize image_processor
													UpperCAmelCase_			:      Dict     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      str     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      Union[str, Any]     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * (self.image_processor_tester.num_channels - 1)
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      List[str]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Tuple     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )












'''simple docstring'''







import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   List[str]									=       BlenderbotSmallTokenizer
							__a					:   List[Any]									=       False

							def     UpperCAmelCase__	(							self							:     str   )					->       str:







													"""simple docstring"""
													super().setUp()

													UpperCAmelCase_			:      Tuple     						=  ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
													UpperCAmelCase_			:      Optional[Any]     						=  dict(zip(__magic_name__       ,     range(len(__magic_name__   )   )   )   )

													UpperCAmelCase_			:      int     						=  ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
													UpperCAmelCase_			:      Optional[Any]     						=  {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

													UpperCAmelCase_			:      Tuple     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''vocab_file''']   )
													UpperCAmelCase_			:      Dict     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''merges_file''']   )
													with open(self.vocab_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			fp.write(json.dumps(__magic_name__   ) + '''\n'''   )
													with open(self.merges_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			fp.write('''\n'''.join(__magic_name__   )   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     **__magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													kwargs.update(self.special_tokens_map   )
													return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Tuple     						=  '''adapt act apte'''
													return input_text, output_text

							def     UpperCAmelCase__	(							self							:     str   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BlenderbotSmallTokenizer(self.vocab_file       ,     self.merges_file       ,     **self.special_tokens_map   )
													UpperCAmelCase_			:      List[Any]     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Dict     						=  ['''adapt''', '''act''', '''ap@@''', '''te''']
													UpperCAmelCase_			:      Dict     						=  tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

													UpperCAmelCase_			:      Dict     						=  [0, 1, 2, 3, 4, 5]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   )       ,     __magic_name__   )

							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
													assert tok('''sam'''   ).input_ids == [13_84]
													UpperCAmelCase_			:      Optional[int]     						=  '''I am a small frog.'''
													UpperCAmelCase_			:      List[str]     						=  tok([src_text]       ,     padding=__magic_name__       ,     truncation=__magic_name__   )['''input_ids''']
													UpperCAmelCase_			:      Dict     						=  tok.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__       ,     clean_up_tokenization_spaces=__magic_name__   )[0]
													assert src_text != decoded  # I wish it did!
													assert decoded == "i am a small frog ."



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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
													UpperCAmelCase_			:      List[Any]     						=  '''I am a small frog .'''
													UpperCAmelCase_			:      Any     						=  '''.'''
													UpperCAmelCase_			:      List[Any]     						=  tok(__magic_name__   )['''input_ids''']
													UpperCAmelCase_			:      Optional[int]     						=  tok(__magic_name__   )['''input_ids''']

													assert encoded[-1] == encoded_dot[0]





