





import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
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
UpperCAmelCase_  							=				'src/transformers'


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

UpperCAmelCase_  							=				transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase_  							=				{
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[Any]   )   ->			int:
				UpperCamelCase__							:       Optional[Any]      =   False
				for attribute in attributes:
								for modeling_source in source_strings:
												# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
												if (
												    f"config.{attribute}" in modeling_source
												    or f"getattr(config, \"{attribute}\"" in modeling_source
												    or f"getattr(self.config, \"{attribute}\"" in modeling_source
												):
																UpperCamelCase__							:       str      =   True
												# Deal with multi-line cases
												elif (
												    re.search(
												        rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""	,     __UpperCAmelCase	,     )
												    is not None
												):
																UpperCamelCase__							:       List[str]      =   True
												# `SequenceSummary` is called with `SequenceSummary(config)`
												elif attribute in [
												    "summary_type",
												    "summary_use_proj",
												    "summary_activation",
												    "summary_last_dropout",
												    "summary_proj_to_labels",
												    "summary_first_dropout",
												]:
																if "SequenceSummary" in modeling_source:
																				UpperCamelCase__							:       Any      =   True
												if attribute_used:
																break
								if attribute_used:
												break

    # common and important attributes, even if they do not always appear in the modeling files
				UpperCamelCase__							:       Dict      =   [
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				UpperCamelCase__							:       Dict      =   ['''encoder_no_repeat_ngram_size''']

				# Special cases to be allowed
				UpperCamelCase__							:       str      =   True
				if not attribute_used:
								UpperCamelCase__							:       Union[str, Any]      =   False
								for attribute in attributes:
												# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
												if attribute in ["is_encoder_decoder"] and default_value is True:
																UpperCamelCase__							:       Optional[Any]      =   True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																UpperCamelCase__							:       str      =   True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																UpperCamelCase__							:       Union[str, Any]      =   True
												elif attribute.endswith('''_token_id'''   ):
																UpperCamelCase__							:       List[str]      =   True

												# configuration class specific cases
												if not case_allowed:
																UpperCamelCase__							:       List[Any]      =   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__	,     []   )
																UpperCamelCase__							:       Optional[int]      =   allowed_cases is True or attribute in allowed_cases

				return attribute_used or case_allowed




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
				UpperCamelCase__							:       int      =   dict(inspect.signature(config_class.__init__   ).parameters   )
				UpperCamelCase__							:       List[Any]      =   [x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
				UpperCamelCase__							:       int      =   [signature[param].default for param in parameter_names]

				# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
				# as one variant is used, the test should pass
				UpperCamelCase__							:       Any      =   {}
				if len(config_class.attribute_map   ) > 0:
								UpperCamelCase__							:       List[Any]      =   {v: k for k, v in config_class.attribute_map.items()}

				# Get the path to modeling source files
				UpperCamelCase__							:       Optional[int]      =   inspect.getsourcefile(__UpperCAmelCase   )
				UpperCamelCase__							:       Union[str, Any]      =   os.path.dirname(__UpperCAmelCase   )
				# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
				UpperCamelCase__							:       Dict      =   [os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   ) for fn in os.listdir(__UpperCAmelCase   ) if fn.startswith('''modeling_'''   )]

				# Get the source code strings
				UpperCamelCase__							:       List[str]      =   []
				for path in modeling_paths:
								if os.path.isfile(__UpperCAmelCase   ):
												with open(__UpperCAmelCase   ) as fp:
																modeling_sources.append(fp.read()   )

				UpperCamelCase__							:       Union[str, Any]      =   []
				for config_param, default_value in zip(__UpperCAmelCase	,     __UpperCAmelCase   ):
								# `attributes` here is all the variant names for `config_param`
								UpperCamelCase__							:       int      =   [config_param]
								# some configuration classes have non-empty `attribute_map`, and both names could be used in the
								# corresponding modeling files. As long as one of them appears, it is fine.
								if config_param in reversed_attribute_map:
												attributes.append(reversed_attribute_map[config_param]   )

								if not check_attribute_being_used(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   ):
												unused_attributes.append(attributes[0]   )

				return sorted(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						)   ->			List[str]:
				UpperCamelCase__							:       Tuple      =   {}
				for _config_class in list(CONFIG_MAPPING.values()   ):
								# Skip deprecated models
								if "models.deprecated" in _config_class.__module__:
												continue
								# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
								UpperCamelCase__							:       Optional[int]      =   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )	,     lambda __UpperCAmelCase   : inspect.isclass(__UpperCAmelCase   )
								        and issubclass(__UpperCAmelCase	,     __UpperCAmelCase   )
								        and inspect.getmodule(__UpperCAmelCase   ) == inspect.getmodule(_config_class   )	,     )
								]
								for config_class in config_classes_in_module:
												UpperCamelCase__							:       List[str]      =   check_config_attributes_being_used(__UpperCAmelCase   )
												if len(__UpperCAmelCase   ) > 0:
																UpperCamelCase__							:       Tuple      =   unused_attributes

				if len(__UpperCAmelCase   ) > 0:
								UpperCamelCase__							:       Dict      =   '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
								for name, attributes in configs_with_unused_attributes.items():
												error += f"{name}: {attributes}\n"

								raise ValueError(__UpperCAmelCase   )


if __name__ == "__main__":
		check_config_attributes()








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






@nightly
@require_torch_gpu
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




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






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2

