

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







			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase=0      ,      lowercase=(4, 4, 64, 64)      ,      lowercase=False       )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase=False      ,      lowercase="CompVis/stable-diffusion-v1-4"       )  ->						Tuple:
							'''simple docstring'''






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

							A__    ,				A__        =			FlaxUNetaDConditionModel.from_pretrained(
							    lowercase      ,      subfolder="unet"      ,      dtype=lowercase      ,      revision=lowercase       )
							return model, params


			def      UpperCamelCase		(    self      ,      lowercase=0      ,      lowercase=(4, 77, 768)      ,      lowercase=False       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			jnp.bfloataa if fpaa else jnp.floataa
							A__        =			jnp.array(load_hf_numpy(self.get_file_format(lowercase      ,      lowercase       )       )      ,      dtype=lowercase       )
							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, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]       )
			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






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

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

							assert sample.shape == latents.shape

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

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
							assert jnp.allclose(lowercase      ,      lowercase      ,      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, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]       )
			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__    ,				A__        =			self.get_unet_model(model_id="stabilityai/stable-diffusion-2"      ,      fpaa=lowercase       )
							A__        =			self.get_latents(lowercase      ,      shape=(4, 4, 96, 96)      ,      fpaa=lowercase       )
							A__        =			self.get_encoder_hidden_states(lowercase      ,      shape=(4, 77, 1024)      ,      fpaa=lowercase       )

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

							assert sample.shape == latents.shape

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

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




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
lowerCAmelCase__	   = """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__	   = direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase__	   = 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)`
lowerCAmelCase__	   = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


lowerCAmelCase__	   = {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Dict:




				'''simple docstring'''





				A__        =			None

				# source code of `config_class`
				A__        =			inspect.getsource(SCREAMING_SNAKE_CASE_      )
				A__        =			_re_checkpoint.findall(SCREAMING_SNAKE_CASE_      )

				# 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("/"      ):
												A__        =			ckpt_link[:-1]

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

				return checkpoint



def 					lowerCAmelCase__							(	)			->      List[str]:




				'''simple docstring'''





				A__        =			[]

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

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

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


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()

