

import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor




@require_tokenizers
@require_vision
class      UpperCamelCase (     unittest.TestCase   ):

				def UpperCamelCase			(			self :      str    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE								=			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
					# fmt: on
					SCREAMING_SNAKE_CASE								=			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]    )    )

					SCREAMING_SNAKE_CASE								=			{
					    'do_resize': True,
					    'size': {'height': 1_8, 'width': 1_8},
					    'do_normalize': True,
					    'image_mean': [0.5, 0.5, 0.5],
					    'image_std': [0.5, 0.5, 0.5],
					}
					SCREAMING_SNAKE_CASE								=			os.path.join(self.tmpdirname							,    UpperCamelCase_    )
					with open(self.image_processor_file							,    'w'							,    encoding='utf-8'    ) as fp:
						json.dump(UpperCamelCase_							,    UpperCamelCase_    )

				def UpperCamelCase			(			self :      Any							,    **snake_case__ :      List[str]    ):

					"""simple docstring"""






					return BertTokenizer.from_pretrained(self.tmpdirname							,    **UpperCamelCase_    )

				def UpperCamelCase			(			self :      Optional[Any]							,    **snake_case__ :      Dict    ):

					"""simple docstring"""






					return ViTImageProcessor.from_pretrained(self.tmpdirname							,    **UpperCamelCase_    )

				def UpperCamelCase			(			self :      Union[str, Any]    ):

					"""simple docstring"""






					shutil.rmtree(self.tmpdirname    )

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			[np.random.randint(2_5_5							,    size=(3, 3_0, 4_0_0)							,    dtype=np.uinta    )]

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

					return image_inputs

				def UpperCamelCase			(			self :      List[Any]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
					SCREAMING_SNAKE_CASE								=			self.get_image_processor()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

					processor.save_pretrained(self.tmpdirname    )
					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

					self.assertEqual(processor.tokenizer.get_vocab()							,    tokenizer.get_vocab()    )
					self.assertIsInstance(processor.tokenizer							,    (BertTokenizer, BertTokenizerFast)    )

					self.assertEqual(processor.image_processor.to_json_string()							,    image_processor.to_json_string()    )
					self.assertIsInstance(processor.image_processor							,    UpperCamelCase_    )

				def UpperCamelCase			(			self :      List[str]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(
					    tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					SCREAMING_SNAKE_CASE								=			self.get_tokenizer(bos_token='(BOS)'							,    eos_token='(EOS)'    )
					SCREAMING_SNAKE_CASE								=			self.get_image_processor(do_normalize=UpperCamelCase_							,    padding_value=1.0    )

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor.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							,    (BertTokenizer, BertTokenizerFast)    )

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

				def UpperCamelCase			(			self :      Optional[int]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_image_processor()
					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

					SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE								=			image_processor(UpperCamelCase_							,    return_tensors='np'    )
					SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Dict    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_image_processor()
					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

					SCREAMING_SNAKE_CASE								=			'lower newer'

					SCREAMING_SNAKE_CASE								=			processor(text=UpperCamelCase_    )

					SCREAMING_SNAKE_CASE								=			tokenizer(UpperCamelCase_    )

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

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_image_processor()
					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

					SCREAMING_SNAKE_CASE								=			'lower newer'
					SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE								=			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 self.assertRaises(UpperCamelCase_    ):
						processor()

				def UpperCamelCase			(			self :      str    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_image_processor()
					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

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

					SCREAMING_SNAKE_CASE								=			processor.batch_decode(UpperCamelCase_    )
					SCREAMING_SNAKE_CASE								=			tokenizer.batch_decode(UpperCamelCase_    )

					self.assertListEqual(UpperCamelCase_							,    UpperCamelCase_    )




				def UpperCamelCase			(			self :      List[str]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.get_image_processor()
					SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

					SCREAMING_SNAKE_CASE								=			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_							,    image_processor=UpperCamelCase_    )

					SCREAMING_SNAKE_CASE								=			'lower newer'
					SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE								=			processor(text=UpperCamelCase_							,    images=UpperCamelCase_    )

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




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

