





from collections import OrderedDict
from typing import Mapping

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


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

lowercase    : Any    	=  {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}

class  __A(						_A ):
     __A	  =       "distilbert"
     __A	  =       {
         "hidden_size": "dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
     }

     def __init__(      self,       A=3_0522,       A=512,       A=False,       A=6,       A=12,       A=768,       A=4 * 768,       A=0.1,       A=0.1,       A="gelu",       A=0.02,       A=0.1,       A=0.2,       A=0,       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						vocab_size
            _UpperCamelCase              =						max_position_embeddings
            _UpperCamelCase              =						sinusoidal_pos_embds
            _UpperCamelCase              =						n_layers
            _UpperCamelCase              =						n_heads
            _UpperCamelCase              =						dim
            _UpperCamelCase              =						hidden_dim
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation
            _UpperCamelCase              =						initializer_range
            _UpperCamelCase              =						qa_dropout
            _UpperCamelCase              =						seq_classif_dropout
            super().__init__(**A,       pad_token_id=A      )






class  __A(						_A ):

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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













import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                '''stabilityai/stable-diffusion-2''',       revision='''bf16''',       dtype=jnp.bfloataa,       )

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''stabilityai/stable-diffusion-2'''
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxDPMSolverMultistepScheduler.from_pretrained(A,       subfolder='''scheduler'''      )
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                A,       scheduler=A,       revision='''bf16''',       dtype=jnp.bfloataa,       )
            _UpperCamelCase              =						scheduler_params

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






