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


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

lowercase__		:Union[str, Any]   =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__		:int   =    {
    # 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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						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
															):
																				lowercase							=						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}"' ,			lowerCAmelCase__ ,			)
															    is not None
															):
																				lowercase							=						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:
																									lowercase							=						True
															if attribute_used:
																				break
										if attribute_used:
															break

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

					# Special cases to be allowed
					lowercase							=						True
					if not attribute_used:
										lowercase							=						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:
																				lowercase							=						True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				lowercase							=						True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				lowercase							=						True
															elif attribute.endswith('''_token_id'''		):
																				lowercase							=						True

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

					return attribute_used or case_allowed



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						dict(inspect.signature(config_class.__init__		).parameters		)
					lowercase							=						[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
					lowercase							=						[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
					lowercase							=						{}
					if len(config_class.attribute_map		) > 0:
										lowercase							=						{v: k for k, v in config_class.attribute_map.items()}

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

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

					lowercase							=						[]
					for config_param, default_value in zip(lowerCAmelCase__ ,			lowerCAmelCase__		):
										# `attributes` here is all the variant names for `config_param`
										lowercase							=						[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(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
															unused_attributes.append(attributes[0]		)

					return sorted(lowerCAmelCase__		)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						{}
					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.)
										lowercase							=						[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class		) ,			lambda lowerCAmelCase__		: inspect.isclass(lowerCAmelCase__		)
										        and issubclass(lowerCAmelCase__ ,			lowerCAmelCase__		)
										        and inspect.getmodule(lowerCAmelCase__		) == inspect.getmodule(_config_class		) ,			)
										]
										for config_class in config_classes_in_module:
															lowercase							=						check_config_attributes_being_used(lowerCAmelCase__		)
															if len(lowerCAmelCase__		) > 0:
																				lowercase							=						unused_attributes

					if len(lowerCAmelCase__		) > 0:
										lowercase							=						'''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(lowerCAmelCase__		)


if __name__ == "__main__":
							check_config_attributes()






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Optional[Any]       					=IFPipeline
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_       :       int       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()




							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)





							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







@slow
@require_torch_gpu
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def  A__      (				self):
												# if

												lowercase							=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa)

												lowercase							=						IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa   ,text_encoder=A__   ,tokenizer=A__)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda''')

												lowercase	,						lowercase							=						pipe_a.encode_prompt('''anime turtle'''   ,device='''cuda''')

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												lowercase							=						None
												lowercase							=						None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												lowercase							=						IFImgaImgPipeline(**pipe_a.components)
												lowercase							=						IFImgaImgSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_imgaimg(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												lowercase							=						IFInpaintingPipeline(**pipe_a.components)
												lowercase							=						IFInpaintingSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_inpainting(A__   ,A__   ,A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_3 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)





							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(1)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(1)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()




