


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:
  __lowerCAmelCase			=							False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor





class   __a      (   unittest.TestCase				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=7    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=18    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=400    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=None    ,			) ->    Dict:




        '''simple docstring'''
        lowercase__:       int        =       size if size is not None else {'height': 20, 'width': 20}
        lowercase__:       Any        =       parent
        lowercase__:       Any        =       batch_size
        lowercase__:       Optional[int]        =       num_channels
        lowercase__:       Tuple        =       image_size
        lowercase__:       List[str]        =       min_resolution
        lowercase__:       Union[str, Any]        =       max_resolution
        lowercase__:       Dict        =       size
        lowercase__:       Optional[Any]        =       do_normalize
        lowercase__:       str        =       do_convert_rgb
        lowercase__:       Any        =       [512, 1_024, 2_048, 4_096]
        lowercase__:       Dict        =       patch_size if patch_size is not None else {'height': 16, 'width': 16}
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       str        =       'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
        lowercase__:       Any        =       Image.open(requests.get(lowerCAmelCase__    ,			stream=lowerCAmelCase__			).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      (   __snake_case	,	unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			PixaStructImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       str        =       PixaStructImageProcessingTester(self			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_normalize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_convert_rgb'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.image_processor_tester.prepare_dummy_image()

        lowercase__:       Any        =       self.image_processing_class(**self.image_processor_dict			)
        lowercase__:       List[str]        =       2_048

        lowercase__:       Tuple        =       image_processor(lowerCAmelCase__    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()    ,			torch.tensor(0.0_6_0_6			)    ,			atol=1E-3    ,			rtol=1E-3			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # Initialize image_processor
        lowercase__:       Tuple        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       Tuple        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

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

               # Test batched
               lowercase__:       Union[str, Any]        =       image_processor(
                   lowerCAmelCase__    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        # Initialize image_processor
        lowercase__:       Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       List[Any]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

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

        lowercase__:       Optional[Any]        =       True

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

               lowercase__:       int        =       'Hello'

               lowercase__:       Union[str, Any]        =       image_processor(
                   image_inputs[0]    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__    ,			header_text=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(1, max_patch, expected_hidden_dim)    ,			)

               # Test batched
               lowercase__:       Any        =       image_processor(
                   lowerCAmelCase__    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__    ,			header_text=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




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

        lowercase__:       List[Any]        =       (
            (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
               lowercase__:       str        =       image_processor(
                   image_inputs[0]    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(1, max_patch, expected_hidden_dim)    ,			)

               # Test batched
               lowercase__:       Tuple        =       image_processor(
                   lowerCAmelCase__    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        # Initialize image_processor
        lowercase__:       str        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__:       List[Any]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			torchify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			torch.Tensor			)

        # Test not batched input
        lowercase__:       Optional[Any]        =       (
            (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
               lowercase__:       str        =       image_processor(
                   image_inputs[0]    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(1, max_patch, expected_hidden_dim)    ,			)

               # Test batched
               lowercase__:       str        =       image_processor(
                   lowerCAmelCase__    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).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      (   __snake_case	,	unittest.TestCase				):
 __lowercase			:							int       					=			PixaStructImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       PixaStructImageProcessingTester(self    ,			num_channels=4			)
        lowercase__:       Union[str, Any]        =       3
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       int        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_normalize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_convert_rgb'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        # Initialize image_processor
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       Union[str, Any]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

        # Test not batched input
        lowercase__:       Optional[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
               lowercase__:       Union[str, Any]        =       image_processor(
                   image_inputs[0]    ,			return_tensors='pt'    ,			max_patches=lowerCAmelCase__			).flattened_patches
               self.assertEqual(
                   encoded_images.shape    ,			(1, max_patch, expected_hidden_dim)    ,			)

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






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[int]       					=			CLIPTokenizer
 __lowercase			:							str       					=			CLIPTokenizerFast
 __lowercase			:							Tuple       					=			True
 __lowercase			:							str       					=			{}
 __lowercase			:							Dict       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().setUp()

        # fmt: off
        lowercase__:       str        =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        lowercase__:       List[str]        =       dict(zip(lowerCAmelCase__    ,			range(len(lowerCAmelCase__			)			)			)			)
        lowercase__:       int        =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
        lowercase__:       Optional[int]        =       {'unk_token': '<unk>'}

        lowercase__:       int        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       Union[str, Any]        =       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(lowerCAmelCase__			) + '\n'			)
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write('\n'.join(lowerCAmelCase__			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Dict        =       'lower newer'
        return input_text, output_text
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       CLIPTokenizer(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map			)
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Union[str, Any]        =       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
        lowercase__:       Any        =       tokenizer.tokenize(lowerCAmelCase__			)
        self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       Tuple        =       tokens + [tokenizer.unk_token]
        lowercase__:       Tuple        =       [10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)    ,			lowerCAmelCase__			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       List[str]        =       self.tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)
                      lowercase__:       Optional[int]        =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)

                      lowercase__:       Optional[int]        =       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                      lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       Dict        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                      # with Tilde) encoded in 2 different ways
                      lowercase__:       Dict        =       'xa\u0303y' + ' ' + 'x\xe3y'
                      lowercase__:       Tuple        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       int        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of space type
                      lowercase__:       str        =       [
                          '\u0009',  # (horizontal tab, '\t')
                          '\u000B',  # (vertical tab)
                          '\u000C',  # (form feed)
                          '\u0020',  # (space, ' ')
                          '\u200E',  # (left-to-right mark):w
                          '\u200F',  # (right-to-left mark)
                      ]
                      for unicode_seq in spaces_unicodes:
                             lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Tuple        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of line break type
                      lowercase__:       str        =       [
                          '\u000A',  # (line feed, '\n')
                          '\r\n',  # (carriage return and line feed, '\r\n')
                          '\u000D',  # (carriage return, '\r')
                          '\r',  # (carriage return, '\r')
                          '\u000D',  # (carriage return, '\r')
                          '\u2028',  # (line separator)
                          '\u2029',  # (paragraph separator)
                          # "\u0085", # (next line)
                      ]

                      # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                      # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                      # space (and thus into an empty list).

                      for unicode_seq in line_break_unicodes:
                             lowercase__:       Optional[int]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Optional[int]        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       Optional[int]        =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                      lowercase__:       Optional[int]        =       F'{text_of_1_token} {text_of_1_token}'

                      lowercase__:       int        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       Dict        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(len(lowerCAmelCase__			) + 1, len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)

                      lowercase__:       Any        =       F' {text}'

                      lowercase__:       Union[str, Any]        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       int        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase__			) + 1, 1 + len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # Test related to the breaking change introduced in transformers v4.17.0
        # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
        with self.assertRaises(lowerCAmelCase__			) as context:
               self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'			)

        self.assertTrue(
            context.exception.args[0].startswith(
                'The `backend_tokenizer` provided does not match the expected format.'			)			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        super().test_tokenization_python_rust_equals()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # CLIP always lower cases letters
        pass


