


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      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      tempfile.mkdtemp()

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

          __UpperCAmelCase     :			List[Any]  							=      {
              '''do_resize''': True,
              '''size''': {'''height''': 18, '''width''': 18},
              '''do_normalize''': True,
              '''image_mean''': [0.5, 0.5, 0.5],
              '''image_std''': [0.5, 0.5, 0.5],
          }
          __UpperCAmelCase     :			int  							=      os.path.join(self.tmpdirname							,			snake_case      )
          with open(self.image_processor_file							,			'''w'''							,			encoding='''utf-8'''      ) as fp:
               json.dump(snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]							,			**snake_case					:		str      )						->							Optional[Any]:
          return BertTokenizer.from_pretrained(self.tmpdirname							,			**snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			**snake_case					:		Union[str, Any]      )						->							Tuple:
          return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**snake_case      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							str:
          shutil.rmtree(self.tmpdirname      )

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Any:
          __UpperCAmelCase     :			Any  							=      [np.random.randint(255							,			size=(3, 30, 400)							,			dtype=np.uinta      )]

          __UpperCAmelCase     :			str  							=      [Image.fromarray(np.moveaxis(snake_case							,			0							,			-1      )      ) for x in image_inputs]

          return image_inputs

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			str  							=      self.get_tokenizer()
          __UpperCAmelCase     :			int  							=      self.get_image_processor()

          __UpperCAmelCase     :			List[Any]  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          processor.save_pretrained(self.tmpdirname      )
          __UpperCAmelCase     :			int  							=      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							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							int:
          __UpperCAmelCase     :			Any  							=      VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()      )
          processor.save_pretrained(self.tmpdirname      )

          __UpperCAmelCase     :			List[str]  							=      self.get_tokenizer(bos_token='''(BOS)'''							,			eos_token='''(EOS)'''      )
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_image_processor(do_normalize=snake_case							,			padding_value=1.0      )

          __UpperCAmelCase     :			int  							=      VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname							,			bos_token='''(BOS)'''							,			eos_token='''(EOS)'''							,			do_normalize=snake_case							,			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							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							int:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_image_processor()
          __UpperCAmelCase     :			List[str]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Union[str, Any]  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          __UpperCAmelCase     :			str  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			List[Any]  							=      image_processor(snake_case							,			return_tensors='''np'''      )
          __UpperCAmelCase     :			Union[str, Any]  							=      processor(images=snake_case							,			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[int]      )						->							str:
          __UpperCAmelCase     :			int  							=      self.get_image_processor()
          __UpperCAmelCase     :			Dict  							=      self.get_tokenizer()

          __UpperCAmelCase     :			List[str]  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      '''lower newer'''

          __UpperCAmelCase     :			List[Any]  							=      processor(text=snake_case      )

          __UpperCAmelCase     :			List[Any]  							=      tokenizer(snake_case      )

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

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Tuple:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_image_processor()
          __UpperCAmelCase     :			Any  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Optional[Any]  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          __UpperCAmelCase     :			str  							=      '''lower newer'''
          __UpperCAmelCase     :			Dict  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			Optional[int]  							=      processor(text=snake_case							,			images=snake_case      )

          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(snake_case      ):
               processor()

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Tuple:
          __UpperCAmelCase     :			Tuple  							=      self.get_image_processor()
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			List[Any]  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCAmelCase     :			List[str]  							=      processor.batch_decode(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      tokenizer.batch_decode(snake_case      )

          self.assertListEqual(snake_case							,			snake_case      )






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Any:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_image_processor()
          __UpperCAmelCase     :			str  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Any  							=      VisionTextDualEncoderProcessor(tokenizer=snake_case							,			image_processor=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      '''lower newer'''
          __UpperCAmelCase     :			Any  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			Optional[int]  							=      processor(text=snake_case							,			images=snake_case      )

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



'''simple docstring'''



import os
import sys
import unittest


__UpperCAmelCase      :Optional[int]			    =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase      :Dict			    =       os.path.join(git_repo_path, "src", "diffusers")



class      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase     :			Optional[Any]  							=      find_backend('''    if not is_torch_available():'''      )
          self.assertEqual(snake_case							,			'''torch'''      )

          # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
          # self.assertEqual(backend_with_underscore, "tensorflow_text")

          __UpperCAmelCase     :			Union[str, Any]  							=      find_backend('''    if not (is_torch_available() and is_transformers_available()):'''      )
          self.assertEqual(snake_case							,			'''torch_and_transformers'''      )

          # double_backend_with_underscore = find_backend(
          #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
          # )
          # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

          __UpperCAmelCase     :			List[str]  							=      find_backend(
              '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''      )
          self.assertEqual(snake_case							,			'''torch_and_transformers_and_onnx'''      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Tuple  							=      read_init()
          # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
          self.assertIn('''torch'''							,			snake_case      )
          self.assertIn('''torch_and_transformers'''							,			snake_case      )
          self.assertIn('''flax_and_transformers'''							,			snake_case      )
          self.assertIn('''torch_and_transformers_and_onnx'''							,			snake_case      )

          # Likewise, we can't assert on the exact content of a key
          self.assertIn('''UNet2DModel'''							,			objects['''torch''']      )
          self.assertIn('''FlaxUNet2DConditionModel'''							,			objects['''flax''']      )
          self.assertIn('''StableDiffusionPipeline'''							,			objects['''torch_and_transformers''']      )
          self.assertIn('''FlaxStableDiffusionPipeline'''							,			objects['''flax_and_transformers''']      )
          self.assertIn('''LMSDiscreteScheduler'''							,			objects['''torch_and_scipy''']      )
          self.assertIn('''OnnxStableDiffusionPipeline'''							,			objects['''torch_and_transformers_and_onnx''']      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[Any]:
          __UpperCAmelCase     :			str  							=      create_dummy_object('''CONSTANT'''							,			'''\'torch\''''      )
          self.assertEqual(snake_case							,			'''\nCONSTANT = None\n'''      )

          __UpperCAmelCase     :			Union[str, Any]  							=      create_dummy_object('''function'''							,			'''\'torch\''''      )
          self.assertEqual(
              snake_case							,			'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''      )

          __UpperCAmelCase     :			Optional[int]  							=      '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
          __UpperCAmelCase     :			Optional[Any]  							=      create_dummy_object('''FakeClass'''							,			'''\'torch\''''      )
          self.assertEqual(snake_case							,			snake_case      )






     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
          __UpperCAmelCase     :			Optional[int]  							=      create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}      )
          self.assertEqual(dummy_files['''torch''']							,			snake_case      )