



"""simple docstring"""






import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp



@slow
@require_flax
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			Tuple:

				"""simple docstring"""

				return F"""gaussian_noise_s={seed}_shape={'_'.join([str(_SCREAMING_SNAKE_CASE) for s in shape])}.npy"""

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Any:

				"""simple docstring"""

				super().tearDown()
				gc.collect()

	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						List[Any]=0			,     _SCREAMING_SNAKE_CASE:						Tuple=(4, 4, 64, 64)			,     _SCREAMING_SNAKE_CASE:						Tuple=False)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    List[Any]				=			jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))			,     dtype=_SCREAMING_SNAKE_CASE)
				return image

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						str="CompVis/stable-diffusion-v1-4")							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    List[str]				=			"bf16" if fpaa else None

				__lowerCAmelCase		,      __lowerCAmelCase :    str				=			FlaxUNetaDConditionModel.from_pretrained(
				    _SCREAMING_SNAKE_CASE			,     subfolder="unet"			,     dtype=_SCREAMING_SNAKE_CASE			,     revision=_SCREAMING_SNAKE_CASE)
				return model, params

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any=0			,     _SCREAMING_SNAKE_CASE:						Any=(4, 77, 768)			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=False)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    Union[str, Any]				=			jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))			,     dtype=_SCREAMING_SNAKE_CASE)
				return hidden_states

	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
	        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
	        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
	        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
	        # fmt: on
	    ])
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Tuple				=			self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"			,     fpaa=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			self.get_latents(_SCREAMING_SNAKE_CASE			,     fpaa=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE			,     fpaa=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			model.apply(
				    {"params": params}			,     _SCREAMING_SNAKE_CASE			,     jnp.array(_SCREAMING_SNAKE_CASE			,     dtype=jnp.intaa)			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     ).sample

				assert sample.shape == latents.shape

				__lowerCAmelCase :    Union[str, Any]				=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))			,     dtype=jnp.floataa)
				__lowerCAmelCase :    int				=			jnp.array(_SCREAMING_SNAKE_CASE			,     dtype=jnp.floataa)

				# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
				assert jnp.allclose(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2)







	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
	        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
	        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
	        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
	        # fmt: on
	    ])
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						List[str])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			self.get_unet_model(model_id="stabilityai/stable-diffusion-2"			,     fpaa=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			self.get_latents(_SCREAMING_SNAKE_CASE			,     shape=(4, 4, 96, 96)			,     fpaa=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE			,     shape=(4, 77, 1024)			,     fpaa=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[int]				=			model.apply(
				    {"params": params}			,     _SCREAMING_SNAKE_CASE			,     jnp.array(_SCREAMING_SNAKE_CASE			,     dtype=jnp.intaa)			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     ).sample

				assert sample.shape == latents.shape

				__lowerCAmelCase :    Tuple				=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))			,     dtype=jnp.floataa)
				__lowerCAmelCase :    str				=			jnp.array(_SCREAMING_SNAKE_CASE			,     dtype=jnp.floataa)

				# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
				assert jnp.allclose(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]