


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
A  :     List[str]    					=    "src/transformers"


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

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


A  :     Union[str, Any]    					=    {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    None

			# source code of `config_class`
			__lowerCAmelCase       					=    inspect.getsource(lowercase__   )
			__lowerCAmelCase       					=    _re_checkpoint.findall(lowercase__   )

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

						# verify the checkpoint name corresponds to the checkpoint link
						__lowerCAmelCase       					=    f"https://huggingface.co/{ckpt_name}"
						if ckpt_link == ckpt_link_from_name:
									__lowerCAmelCase       					=    ckpt_name
									break

			return checkpoint


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

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

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

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


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




