


'''simple docstring'''
import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__magic_name__ : List[str]					='src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
__magic_name__ : Optional[Any]					=direct_transformers_import(PATH_TO_TRANSFORMERS)

__magic_name__ : str					=transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__magic_name__ : List[str]					=re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


__magic_name__ : str					={
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}




def   __snake_case							(							lowerCamelCase_			:					Dict    ):
				'''simple docstring'''




				__magic_name__							=			None

				# source code of `config_class`
				__magic_name__							=			inspect.getsource(lowerCamelCase_    )
				__magic_name__							=			_re_checkpoint.findall(lowerCamelCase_    )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith("/"    ):
												__magic_name__							=			ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								__magic_name__							=			F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												__magic_name__							=			ckpt_name
												break

				return checkpoint




def   __snake_case							(							):
				'''simple docstring'''




				__magic_name__							=			[]

				for config_class in list(CONFIG_MAPPING.values()    ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								__magic_name__							=			get_checkpoint_from_config_class(lowerCamelCase_    )

								__magic_name__							=			config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCamelCase_    )

				if len(lowerCamelCase_    ) > 0:
								__magic_name__							=			"\n".join(sorted(lowerCamelCase_    )    )
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'    )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()





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						):

						def 			A_					(      self							,				a							,				a					)       ->   Optional[int]:


								'''simple docstring'''

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

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


								'''simple docstring'''

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

						def 			A_					(      self							,				a=0							,				a=(4, 4, 64, 64)							,				a=False					)       ->   Optional[int]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a=False							,				a="CompVis/stable-diffusion-v1-4"					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						"""bf16""" if fpaa else None

								_UpperCamelCase     ,						_UpperCamelCase         =						FlaxUNetaDConditionModel.from_pretrained(
								    a							,				subfolder="""unet"""							,				dtype=a							,				revision=a					)
								return model, params

						def 			A_					(      self							,				a=0							,				a=(4, 77, 7_68)							,				a=False					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								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							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

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

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(a							,				a							,				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							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				shape=(4, 4, 96, 96)							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				shape=(4, 77, 10_24)							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

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

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



