


'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Optional[int]    				=				tempfile.mkdtemp()

										lowerCAmelCase_ :      List[str]    				=				[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										lowerCAmelCase_ :      Dict    				=				os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''vocab_file''']							)
										with open(self.vocab_file   ,						'''w'''   ,						encoding='''utf-8'''							) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

										lowerCAmelCase_ :      Tuple    				=				{
										    'do_resize': True,
										    'size': 2_0,
										    'do_center_crop': True,
										    'crop_size': 1_8,
										    'do_normalize': True,
										    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
										    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
										}
										lowerCAmelCase_ :      str    				=				os.path.join(self.tmpdirname   ,						UpperCamelCase__							)
										with open(self.image_processor_file   ,						'''w'''   ,						encoding='''utf-8'''							) as fp:
														json.dump(UpperCamelCase__   ,						UpperCamelCase__							)




						def 						lowercase_ (				self   ,						**__lowercase							)					->    Any:
										return BertTokenizer.from_pretrained(self.tmpdirname   ,						**UpperCamelCase__							)




						def 						lowercase_ (				self   ,						**__lowercase							)					->    List[Any]:
										return BertTokenizerFast.from_pretrained(self.tmpdirname   ,						**UpperCamelCase__							)




						def 						lowercase_ (				self   ,						**__lowercase							)					->    Optional[int]:
										return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,						**UpperCamelCase__							)




						def 						lowercase_ (				self							)					->    Dict:
										shutil.rmtree(self.tmpdirname							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      List[Any]    				=				[np.random.randint(2_5_5   ,						size=(3, 3_0, 4_0_0)   ,						dtype=np.uinta							)]
										lowerCAmelCase_ :      Optional[Any]    				=				[Image.fromarray(np.moveaxis(UpperCamelCase__   ,						0   ,						-1							)							) for x in image_inputs]
										return image_inputs




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Tuple    				=				self.get_tokenizer()
										lowerCAmelCase_ :      Dict    				=				self.get_rust_tokenizer()
										lowerCAmelCase_ :      Dict    				=				self.get_image_processor()

										lowerCAmelCase_ :      Optional[int]    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										lowerCAmelCase_ :      Union[str, Any]    				=				AlignProcessor.from_pretrained(self.tmpdirname   ,						use_fast=UpperCamelCase__							)

										lowerCAmelCase_ :      List[Any]    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										lowerCAmelCase_ :      Union[str, Any]    				=				AlignProcessor.from_pretrained(self.tmpdirname							)

										self.assertEqual(processor_slow.tokenizer.get_vocab()   ,						tokenizer_slow.get_vocab()							)
										self.assertEqual(processor_fast.tokenizer.get_vocab()   ,						tokenizer_fast.get_vocab()							)
										self.assertEqual(tokenizer_slow.get_vocab()   ,						tokenizer_fast.get_vocab()							)
										self.assertIsInstance(processor_slow.tokenizer   ,						UpperCamelCase__							)
										self.assertIsInstance(processor_fast.tokenizer   ,						UpperCamelCase__							)

										self.assertEqual(processor_slow.image_processor.to_json_string()   ,						image_processor.to_json_string()							)
										self.assertEqual(processor_fast.image_processor.to_json_string()   ,						image_processor.to_json_string()							)
										self.assertIsInstance(processor_slow.image_processor   ,						UpperCamelCase__							)
										self.assertIsInstance(processor_fast.image_processor   ,						UpperCamelCase__							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Union[str, Any]    				=				AlignProcessor(tokenizer=self.get_tokenizer()   ,						image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.get_tokenizer(bos_token='''(BOS)'''   ,						eos_token='''(EOS)'''							)
										lowerCAmelCase_ :      int    				=				self.get_image_processor(do_normalize=UpperCamelCase__   ,						padding_value=1.0							)

										lowerCAmelCase_ :      List[Any]    				=				AlignProcessor.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 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      int    				=				self.get_image_processor()
										lowerCAmelCase_ :      str    				=				self.get_tokenizer()

										lowerCAmelCase_ :      str    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)

										lowerCAmelCase_ :      Union[str, Any]    				=				self.prepare_image_inputs()

										lowerCAmelCase_ :      int    				=				image_processor(UpperCamelCase__   ,						return_tensors='''np'''							)
										lowerCAmelCase_ :      Dict    				=				processor(images=UpperCamelCase__   ,						return_tensors='''np'''							)

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




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

										lowerCAmelCase_ :      List[str]    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)

										lowerCAmelCase_ :      Tuple    				=				'lower newer'

										lowerCAmelCase_ :      Tuple    				=				processor(text=UpperCamelCase__							)

										lowerCAmelCase_ :      int    				=				tokenizer(UpperCamelCase__   ,						padding='''max_length'''   ,						max_length=6_4							)

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




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Dict    				=				self.get_image_processor()
										lowerCAmelCase_ :      List[Any]    				=				self.get_tokenizer()

										lowerCAmelCase_ :      str    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)

										lowerCAmelCase_ :      str    				=				'lower newer'
										lowerCAmelCase_ :      Optional[int]    				=				self.prepare_image_inputs()

										lowerCAmelCase_ :      Optional[int]    				=				processor(text=UpperCamelCase__   ,						images=UpperCamelCase__							)

										self.assertListEqual(list(inputs.keys()							)   ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']							)

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




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      List[str]    				=				self.get_image_processor()
										lowerCAmelCase_ :      List[str]    				=				self.get_tokenizer()

										lowerCAmelCase_ :      Union[str, Any]    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)

										lowerCAmelCase_ :      Tuple    				=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCAmelCase_ :      List[str]    				=				processor.batch_decode(UpperCamelCase__							)
										lowerCAmelCase_ :      Any    				=				tokenizer.batch_decode(UpperCamelCase__							)

										self.assertListEqual(UpperCamelCase__   ,						UpperCamelCase__							)




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

										lowerCAmelCase_ :      Union[str, Any]    				=				AlignProcessor(tokenizer=UpperCamelCase__   ,						image_processor=UpperCamelCase__							)

										lowerCAmelCase_ :      str    				=				'lower newer'
										lowerCAmelCase_ :      List[str]    				=				self.prepare_image_inputs()

										lowerCAmelCase_ :      List[Any]    				=				processor(text=UpperCamelCase__   ,						images=UpperCamelCase__							)

										self.assertListEqual(list(inputs.keys()							)   ,						processor.model_input_names							)



_UpperCAmelCase			:  int     =frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

_UpperCAmelCase			:  List[Any]     =frozenset(["""prompt""", """negative_prompt"""])

_UpperCAmelCase			:  Dict     =frozenset([])

_UpperCAmelCase			:  int     =frozenset(["""image"""])

_UpperCAmelCase			:  Tuple     =frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  int     =frozenset(["""image"""])

_UpperCAmelCase			:  str     =frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

_UpperCAmelCase			:  int     =frozenset(["""prompt""", """image""", """negative_prompt"""])

_UpperCAmelCase			:  Optional[int]     =frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

_UpperCAmelCase			:  Optional[int]     =frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  Optional[Any]     =frozenset(["""image""", """mask_image"""])

_UpperCAmelCase			:  Union[str, Any]     =frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  Tuple     =frozenset(["""example_image""", """image""", """mask_image"""])

_UpperCAmelCase			:  Any     =frozenset(["""class_labels"""])

_UpperCAmelCase			:  List[Any]     =frozenset(["""class_labels"""])

_UpperCAmelCase			:  int     =frozenset(["""batch_size"""])

_UpperCAmelCase			:  str     =frozenset([])

_UpperCAmelCase			:  str     =frozenset(["""batch_size"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset([])

_UpperCAmelCase			:  Tuple     =frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

_UpperCAmelCase			:  Tuple     =frozenset(["""prompt""", """negative_prompt"""])
_UpperCAmelCase			:  List[str]     =frozenset(["""input_tokens"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset(["""input_tokens"""])