




import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)




@require_vision
class    snake_case__							(unittest.TestCase       ):
	"""simple docstring"""



	def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[Any]:
				a   =      tempfile.mkdtemp()

				# fmt: off
				a   =      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
				# fmt: on
				a   =      os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["vocab_file"]							)

				with open(self.vocab_file ,				"w" ,				encoding="utf-8"							) as fp:
							fp.write("".join([x + "\n" for x in vocab_tokens]							)							)

				a   =      {
				    "image_mean": FLAVA_IMAGE_MEAN,
				    "image_std": FLAVA_IMAGE_STD,
				    "do_normalize": True,
				    "do_resize": True,
				    "size": 2_24,
				    "do_center_crop": True,
				    "crop_size": 2_24,
				    "input_size_patches": 14,
				    "total_mask_patches": 75,
				    "mask_group_max_patches": None,
				    "mask_group_min_patches": 16,
				    "mask_group_min_aspect_ratio": 0.3,
				    "mask_group_max_aspect_ratio": None,
				    "codebook_do_resize": True,
				    "codebook_size": 1_12,
				    "codebook_do_center_crop": True,
				    "codebook_crop_size": 1_12,
				    "codebook_do_map_pixels": True,
				    "codebook_do_normalize": True,
				    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
				    "codebook_image_std": FLAVA_CODEBOOK_STD,
				}

				a   =      os.path.join(self.tmpdirname ,				__lowerCamelCase							)
				with open(self.image_processor_file ,				"w" ,				encoding="utf-8"							) as fp:
							json.dump(__lowerCamelCase ,				__lowerCamelCase							)





	def 							__UpperCAmelCase							(				self    :					str ,				**__lowerCamelCase    :					str							) ->					Dict:
				return BertTokenizer.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





	def 							__UpperCAmelCase							(				self    :					int ,				**__lowerCamelCase    :					Tuple							) ->					Optional[int]:
				return BertTokenizerFast.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





	def 							__UpperCAmelCase							(				self    :					List[Any] ,				**__lowerCamelCase    :					Tuple							) ->					str:
				return FlavaImageProcessor.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





	def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Dict:
				shutil.rmtree(self.tmpdirname							)





	def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
				a   =      [np.random.randint(2_55 ,				size=(3, 30, 4_00) ,				dtype=np.uinta							)]

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

				return image_inputs





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

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)
				processor_slow.save_pretrained(self.tmpdirname							)
				a   =      FlavaProcessor.from_pretrained(self.tmpdirname ,				use_fast=__lowerCamelCase							)

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)
				processor_fast.save_pretrained(self.tmpdirname							)
				a   =      FlavaProcessor.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 ,				__lowerCamelCase							)
				self.assertIsInstance(processor_fast.tokenizer ,				__lowerCamelCase							)

				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 ,				__lowerCamelCase							)
				self.assertIsInstance(processor_fast.image_processor ,				__lowerCamelCase							)





	def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
				a   =      FlavaProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()							)
				processor.save_pretrained(self.tmpdirname							)

				a   =      self.get_tokenizer(bos_token="(BOS)" ,				eos_token="(EOS)"							)
				a   =      self.get_image_processor(do_normalize=__lowerCamelCase ,				padding_value=1.0							)

				a   =      FlavaProcessor.from_pretrained(
				    self.tmpdirname ,				bos_token="(BOS)" ,				eos_token="(EOS)" ,				do_normalize=__lowerCamelCase ,				padding_value=1.0							)

				self.assertEqual(processor.tokenizer.get_vocab() ,				tokenizer_add_kwargs.get_vocab()							)
				self.assertIsInstance(processor.tokenizer ,				__lowerCamelCase							)

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





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

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

				a   =      self.prepare_image_inputs()

				a   =      image_processor(__lowerCamelCase ,				return_tensors="np"							)
				a   =      processor(images=__lowerCamelCase ,				return_tensors="np"							)

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

				# With rest of the args
				random.seed(12_34							)
				a   =      image_processor(
				    __lowerCamelCase ,				return_image_mask=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				return_tensors="np"							)
				random.seed(12_34							)
				a   =      processor(
				    images=__lowerCamelCase ,				return_image_mask=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				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							) ->					List[Any]:
				a   =      self.get_image_processor()
				a   =      self.get_tokenizer()

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

				a   =      "lower newer"

				a   =      processor(text=__lowerCamelCase							)

				a   =      tokenizer(__lowerCamelCase							)

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





	def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[Any]:
				a   =      self.get_image_processor()
				a   =      self.get_tokenizer()

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

				a   =      "lower newer"
				a   =      self.prepare_image_inputs()

				a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase							)

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

				# add extra args
				a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				return_image_mask=__lowerCamelCase							)

				self.assertListEqual(
				    list(inputs.keys()							) ,				[
				        "input_ids",
				        "token_type_ids",
				        "attention_mask",
				        "pixel_values",
				        "codebook_pixel_values",
				        "bool_masked_pos",
				    ] ,				)

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





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

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

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

				a   =      processor.batch_decode(__lowerCamelCase							)
				a   =      tokenizer.batch_decode(__lowerCamelCase							)

				self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





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

				a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

				a   =      "lower newer"
				a   =      self.prepare_image_inputs()

				a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase							)

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












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






