


import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, 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		)					->			str:




									"""simple docstring"""





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






		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'		)
									_UpperCAmelCase											=     load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'		)

									_UpperCAmelCase											=     'xvjiarui/stable-diffusion-2-inpainting'
									_UpperCAmelCase						,			_UpperCAmelCase											=     FlaxStableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE     ,       safety_checker=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     'Face of a yellow cat, high resolution, sitting on a park bench'

									_UpperCAmelCase											=     jax.random.PRNGKey(0		)
									_UpperCAmelCase											=     50

									_UpperCAmelCase											=     jax.device_count()
									_UpperCAmelCase											=     num_samples * [prompt]
									_UpperCAmelCase											=     num_samples * [init_image]
									_UpperCAmelCase											=     num_samples * [mask_image]
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     pipeline.prepare_inputs(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									# shard inputs and rng
									_UpperCAmelCase											=     replicate(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     jax.random.split(_SCREAMING_SNAKE_CASE     ,       jax.device_count()		)
									_UpperCAmelCase											=     shard(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     shard(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     shard(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     pipeline(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       jit=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     output.images.reshape(_SCREAMING_SNAKE_CASE     ,       512     ,       512     ,       3		)

									_UpperCAmelCase											=     images[0, 253:256, 253:256, -1]

									_UpperCAmelCase											=     jnp.asarray(jax.device_get(image_slice.flatten()		)		)
									_UpperCAmelCase											=     jnp.array(
									    [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]		)
									print(f'''output_slice: {output_slice}'''		)
									assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase__    :Dict       =				logging.get_logger(__name__)

lowerCAmelCase__    :Optional[int]       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      'perceiver'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=26     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="kv"     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=262     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=56     ,       _SCREAMING_SNAKE_CASE=[368, 496]     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=1920     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=[1, 16, 224, 224]     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     num_latents
									_UpperCAmelCase											=     d_latents
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     num_blocks
									_UpperCAmelCase											=     num_self_attends_per_block
									_UpperCAmelCase											=     num_self_attention_heads
									_UpperCAmelCase											=     num_cross_attention_heads
									_UpperCAmelCase											=     qk_channels
									_UpperCAmelCase											=     v_channels
									_UpperCAmelCase											=     cross_attention_shape_for_attention
									_UpperCAmelCase											=     self_attention_widening_factor
									_UpperCAmelCase											=     cross_attention_widening_factor
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     use_query_residual
									# masked language modeling attributes
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									# image classification attributes
									_UpperCAmelCase											=     image_size
									# flow attributes
									_UpperCAmelCase											=     train_size
									# multimodal autoencoding attributes
									_UpperCAmelCase											=     num_frames
									_UpperCAmelCase											=     audio_samples_per_frame
									_UpperCAmelCase											=     samples_per_patch
									_UpperCAmelCase											=     output_shape



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('inputs', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
		@property
		def       UpperCAmelCase__							(     self		)					->			float:




									"""simple docstring"""





									return 1e-4






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 3     ,       _SCREAMING_SNAKE_CASE = 40     ,       _SCREAMING_SNAKE_CASE = 40     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch     ,       num_token_to_add=0		)
																# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																_UpperCAmelCase											=     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)
																# Generate dummy inputs according to compute batch and sequence
																_UpperCAmelCase											=     [' '.join(['a']		) * seq_length] * batch_size
																_UpperCAmelCase											=     dict(preprocessor(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('input_ids'		)
																return inputs
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) and preprocessor.model_input_names[0] == "pixel_values":
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch		)
																_UpperCAmelCase											=     self._generate_dummy_images(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     dict(preprocessor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('pixel_values'		)
																return inputs
									else:
																raise ValueError(
																    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'		)
