


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														__snake_case						:     Dict									=  tempfile.mkdtemp()

														__snake_case						:     Union[str, Any]									=  BlipImageProcessor()
														__snake_case						:     Union[str, Any]									=  GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)

														__snake_case						:     Optional[int]									=  BlipaProcessor(lowerCamelCase       ,      lowerCamelCase							)

														processor.save_pretrained(self.tmpdirname							)

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    Optional[Any]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).tokenizer

							def        __snake_case   (				self			:    str       ,      **lowerCamelCase			:    List[str]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).image_processor

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

							def        __snake_case   (				self			:    Dict							)      ->       int:
														__snake_case						:     Union[str, Any]									=  [np.random.randint(255       ,      size=(3, 30, 400)       ,      dtype=np.uinta							)]

														__snake_case						:     Optional[int]									=  [Image.fromarray(np.moveaxis(lowerCamelCase       ,      0       ,      -1							)							) for x in image_inputs]

														return image_inputs

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case						:     Optional[int]									=  BlipaProcessor(tokenizer=self.get_tokenizer()       ,      image_processor=self.get_image_processor()							)
														processor.save_pretrained(self.tmpdirname							)

														__snake_case						:     Union[str, Any]									=  self.get_tokenizer(bos_token="(BOS)"       ,      eos_token="(EOS)"							)
														__snake_case						:     Dict									=  self.get_image_processor(do_normalize=lowerCamelCase       ,      padding_value=1.0							)

														__snake_case						:     List[Any]									=  BlipaProcessor.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        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Optional[int]									=  self.get_tokenizer()

														__snake_case						:     Dict									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

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

														__snake_case						:     Any									=  image_processor(lowerCamelCase       ,      return_tensors="np"							)
														__snake_case						:     List[str]									=  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							)

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

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  "lower newer"

														__snake_case						:     Union[str, Any]									=  processor(text=lowerCamelCase							)

														__snake_case						:     Tuple									=  tokenizer(lowerCamelCase       ,      return_token_type_ids=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Union[str, Any]									=  self.get_tokenizer()

														__snake_case						:     Tuple									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     List[Any]									=  "lower newer"
														__snake_case						:     List[Any]									=  self.prepare_image_inputs()

														__snake_case						:     Optional[int]									=  processor(text=lowerCamelCase       ,      images=lowerCamelCase							)

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

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

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

														__snake_case						:     Optional[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														__snake_case						:     Union[str, Any]									=  processor.batch_decode(lowerCamelCase							)
														__snake_case						:     List[str]									=  tokenizer.batch_decode(lowerCamelCase							)

														self.assertListEqual(lowerCamelCase       ,      lowerCamelCase							)






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

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     str									=  "lower newer"
														__snake_case						:     Optional[Any]									=  self.prepare_image_inputs()

														__snake_case						:     int									=  processor(text=lowerCamelCase       ,      images=lowerCamelCase							)

														# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
														self.assertListEqual(list(inputs.keys()							)       ,      ["pixel_values", "input_ids", "attention_mask"]							)



"""simple docstring"""






import math

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
