


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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    List[Any]=0      ,   __lowerCamelCase							:    List[Any]=(4, 4, 64, 64)      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						jnp.bfloataa if fpaa else jnp.floataa
             lowerCamelCase__	:       Any					=						jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase ) )      ,   dtype=__lowerCamelCase )
             return image





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    Optional[Any]="CompVis/stable-diffusion-v1-4" ):


             '''simple docstring'''



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

             lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						FlaxUNetaDConditionModel.from_pretrained(
                 __lowerCamelCase      ,   subfolder="unet"      ,   dtype=__lowerCamelCase      ,   revision=__lowerCamelCase )
             return model, params





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Tuple=0      ,   __lowerCamelCase							:    List[Any]=(4, 77, 768)      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						jnp.bfloataa if fpaa else jnp.floataa
             lowerCamelCase__	:       Optional[Any]					=						jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase ) )      ,   dtype=__lowerCamelCase )
             return hidden_states





       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
               [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
               [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
               [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
               # fmt: on
           ] )
       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       int					=						self.get_latents(__lowerCamelCase      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       int					=						self.get_encoder_hidden_states(__lowerCamelCase      ,   fpaa=__lowerCamelCase )

             lowerCamelCase__	:       int					=						model.apply(
                 {"params": params}      ,   __lowerCamelCase      ,   jnp.array(__lowerCamelCase      ,   dtype=jnp.intaa )      ,   encoder_hidden_states=__lowerCamelCase      ,   ).sample

             assert sample.shape == latents.shape

             lowerCamelCase__	:       Optional[int]					=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )      ,   dtype=jnp.floataa )
             lowerCamelCase__	:       List[Any]					=						jnp.array(__lowerCamelCase      ,   dtype=jnp.floataa )

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





       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
               [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
               [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
               [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
               # fmt: on
           ] )
       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.get_unet_model(model_id="stabilityai/stable-diffusion-2"      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.get_latents(__lowerCamelCase      ,   shape=(4, 4, 96, 96)      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       str					=						self.get_encoder_hidden_states(__lowerCamelCase      ,   shape=(4, 77, 1024)      ,   fpaa=__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						model.apply(
                 {"params": params}      ,   __lowerCamelCase      ,   jnp.array(__lowerCamelCase      ,   dtype=jnp.intaa )      ,   encoder_hidden_states=__lowerCamelCase      ,   ).sample

             assert sample.shape == latents.shape

             lowerCamelCase__	:       str					=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )      ,   dtype=jnp.floataa )
             lowerCamelCase__	:       Optional[Any]					=						jnp.array(__lowerCamelCase      ,   dtype=jnp.floataa )

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

A   :					Dict	=      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "ibert"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]=30522      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Any=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    Any=1E-1_2      ,   __lowerCamelCase							:    int=1      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    int="absolute"      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Dict="none"      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Any					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       int					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						hidden_act
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Any					=						type_vocab_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       List[str]					=						quant_mode
             lowerCamelCase__	:       int					=						force_dequant







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


