



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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       List[str]				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


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



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[str]=0				,					_snake_case					:       Tuple=(4, 4, 64, 64)				,					_snake_case					:       Union[str, Any]=False					):






								"""simple docstring"""


								A__       =    jnp.bfloataa if fpaa else jnp.floataa
								A__       =    jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__				,					UpperCamelCase__					)					)				,					dtype=UpperCamelCase__					)
								return image



					def     _a							(     self					:       str				,					_snake_case					:       List[Any]=False				,					_snake_case					:       Optional[int]="CompVis/stable-diffusion-v1-4"					):






								"""simple docstring"""


								A__       =    jnp.bfloataa if fpaa else jnp.floataa
								A__       =    'bf16' if fpaa else None

								A__     , A__       =    FlaxUNetaDConditionModel.from_pretrained(
								    UpperCamelCase__				,					subfolder='unet'				,					dtype=UpperCamelCase__				,					revision=UpperCamelCase__					)
								return model, params



					def     _a							(     self					:       Tuple				,					_snake_case					:       Union[str, Any]=0				,					_snake_case					:       List[str]=(4, 77, 7_68)				,					_snake_case					:       List[str]=False					):






								"""simple docstring"""


								A__       =    jnp.bfloataa if fpaa else jnp.floataa
								A__       =    jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__				,					UpperCamelCase__					)					)				,					dtype=UpperCamelCase__					)
								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, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
					        # fmt: on
					    ]					)
					def     _a							(     self					:       List[str]				,					_snake_case					:       Tuple				,					_snake_case					:       Optional[Any]				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'				,					fpaa=UpperCamelCase__					)
								A__       =    self.get_latents(UpperCamelCase__				,					fpaa=UpperCamelCase__					)
								A__       =    self.get_encoder_hidden_states(UpperCamelCase__				,					fpaa=UpperCamelCase__					)

								A__       =    model.apply(
								    {'params': params}				,					UpperCamelCase__				,					jnp.array(UpperCamelCase__				,					dtype=jnp.intaa					)				,					encoder_hidden_states=UpperCamelCase__				,					).sample

								assert sample.shape == latents.shape

								A__       =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)				,					dtype=jnp.floataa					)
								A__       =    jnp.array(UpperCamelCase__				,					dtype=jnp.floataa					)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(UpperCamelCase__				,					UpperCamelCase__				,					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, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
					        # fmt: on
					    ]					)
					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       Dict				,					_snake_case					:       List[str]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


								A__     , A__       =    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'				,					fpaa=UpperCamelCase__					)
								A__       =    self.get_latents(UpperCamelCase__				,					shape=(4, 4, 96, 96)				,					fpaa=UpperCamelCase__					)
								A__       =    self.get_encoder_hidden_states(UpperCamelCase__				,					shape=(4, 77, 10_24)				,					fpaa=UpperCamelCase__					)

								A__       =    model.apply(
								    {'params': params}				,					UpperCamelCase__				,					jnp.array(UpperCamelCase__				,					dtype=jnp.intaa					)				,					encoder_hidden_states=UpperCamelCase__				,					).sample

								assert sample.shape == latents.shape

								A__       =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)				,					dtype=jnp.floataa					)
								A__       =    jnp.array(UpperCamelCase__				,					dtype=jnp.floataa					)

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









from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


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

								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    position_embedding_type
								A__       =    use_cache
								A__       =    classifier_dropout







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


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




