

'''simple docstring'''





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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=tempfile.mkdtemp()

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

										lowercase :							Tuple    		={
										    '''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],
										}
										lowercase :							Optional[Any]    		=os.path.join(self.tmpdirname		,					UpperCAmelCase__   )
										with open(self.image_processor_file		,					'''w'''		,					encoding='''utf-8'''   ) as fp:
																json.dump(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					**UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										return BertTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					**UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										return ViTImageProcessor.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										shutil.rmtree(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		=[np.random.randint(255		,					size=(3, 30, 400)		,					dtype=np.uinta   )]

										lowercase :							Optional[Any]    		=[Image.fromarray(np.moveaxis(UpperCAmelCase__		,					0		,					-1   )   ) for x in image_inputs]

										return image_inputs







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.get_tokenizer()
										lowercase :							Tuple    		=self.get_image_processor()

										lowercase :							Any    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										processor.save_pretrained(self.tmpdirname   )
										lowercase :							List[str]    		=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  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()		,					image_processor=self.get_image_processor()   )
										processor.save_pretrained(self.tmpdirname   )

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

										lowercase :							Tuple    		=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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.get_image_processor()
										lowercase :							List[str]    		=self.get_tokenizer()

										lowercase :							Optional[int]    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										lowercase :							Optional[int]    		=self.prepare_image_inputs()

										lowercase :							Optional[Any]    		=image_processor(UpperCAmelCase__		,					return_tensors='''np'''   )
										lowercase :							List[str]    		=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  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_image_processor()
										lowercase :							str    		=self.get_tokenizer()

										lowercase :							List[str]    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		='''lower newer'''

										lowercase :							List[str]    		=processor(text=UpperCAmelCase__   )

										lowercase :							List[Any]    		=tokenizer(UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.get_image_processor()
										lowercase :							List[str]    		=self.get_tokenizer()

										lowercase :							Any    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										lowercase :							str    		='''lower newer'''
										lowercase :							Union[str, Any]    		=self.prepare_image_inputs()

										lowercase :							Tuple    		=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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.get_image_processor()
										lowercase :							Union[str, Any]    		=self.get_tokenizer()

										lowercase :							Dict    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										lowercase :							Any    		=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowercase :							Union[str, Any]    		=processor.batch_decode(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=tokenizer.batch_decode(UpperCAmelCase__   )

										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_image_processor()
										lowercase :							Tuple    		=self.get_tokenizer()

										lowercase :							Union[str, Any]    		=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__		,					image_processor=UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		='''lower newer'''
										lowercase :							List[str]    		=self.prepare_image_inputs()

										lowercase :							Any    		=processor(text=UpperCAmelCase__		,					images=UpperCAmelCase__   )

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








'''simple docstring'''





import math

import flax.linen as nn
import jax.numpy as jnp






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

						if flip_sin_to_cos:
												lowercase :							Dict    		=jnp.concatenate([jnp.cos(__magic_name__						), jnp.sin(__magic_name__						)]   , axis=1						)
						else:
												lowercase :							Any    		=jnp.concatenate([jnp.sin(__magic_name__						), jnp.cos(__magic_name__						)]   , axis=1						)
						lowercase :							List[str]    		=jnp.reshape(__magic_name__   , [jnp.shape(__magic_name__						)[0], embedding_dim]						)
						return signal





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





