






'''simple docstring'''







import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





@skip_mps
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					VideoToVideoSDPipeline
			snake_case__	=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}							) - {"image", "width", "height"}
			snake_case__	=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}							) - {"image"}
			snake_case__	=					PipelineTesterMixin.required_optional_params - {"latents"}
			snake_case__	=					False

			# No `output_type`.
			snake_case__	=					frozenset(
			    [
			        "num_inference_steps",
			        "generator",
			        "latents",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]							)






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							torch.manual_seed(0       )
							a_		:	Tuple									=   UNetaDConditionModel(
							    block_out_channels=(32, 64, 64, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=4	,    out_channels=4	,    down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')	,    up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')	,    cross_attention_dim=32	,    attention_head_dim=4	,    )
							a_		:	Optional[Any]									=   DDIMScheduler(
							    beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )
							torch.manual_seed(0       )
							a_		:	Tuple									=   AutoencoderKL(
							    block_out_channels=[32, 64]	,    in_channels=3	,    out_channels=3	,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,    latent_channels=4	,    sample_size=128	,    )
							torch.manual_seed(0       )
							a_		:	Union[str, Any]									=   CLIPTextConfig(
							    bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    hidden_act='''gelu'''	,    projection_dim=512	,    )
							a_		:	List[str]									=   CLIPTextModel(__SCREAMING_SNAKE_CASE       )
							a_		:	List[Any]									=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

							a_		:	Union[str, Any]									=   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							}
							return components






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Dict=0       )      ->      Optional[Any]:
							# 3 frames
							a_		:	Dict									=   floats_tensor((1, 3, 3, 32, 32)	,    rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )

							if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
											a_		:	Union[str, Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							else:
											a_		:	List[str]									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''video''': video,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''pt''',
							}
							return inputs






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[str]:
							a_		:	Any									=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:	Tuple									=   self.get_dummy_components()
							a_		:	int									=   VideoToVideoSDPipeline(**__SCREAMING_SNAKE_CASE       )
							a_		:	List[Any]									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   '''np'''
							a_		:	str									=   sd_pipe(**__SCREAMING_SNAKE_CASE       ).frames
							a_		:	Union[str, Any]									=   frames[0][-3:, -3:, -1]

							assert frames[0].shape == (32, 32, 3)
							a_		:	List[Any]									=   np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]       )

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






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()	,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''	,    )
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE	,    expected_max_diff=5e-3       )






			@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Optional[int]:
							pass






			@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Any:
							pass






			@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      List[str]:
							pass







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							return super().test_progress_bar()





@slow
@skip_mps
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							a_		:	Union[str, Any]									=   VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL'''	,    torch_dtype=torch.floataa       )
							pipe.enable_model_cpu_offload()

							# 10 frames
							a_		:	Union[str, Any]									=   torch.Generator(device='''cpu'''       ).manual_seed(0       )
							a_		:	Any									=   torch.randn((1, 10, 3, 1024, 576)	,    generator=__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   video.to('''cuda'''       )

							a_		:	List[Any]									=   '''Spiderman is surfing'''

							a_		:	Optional[int]									=   pipe(__SCREAMING_SNAKE_CASE	,    video=__SCREAMING_SNAKE_CASE	,    generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=3	,    output_type='''pt'''       ).frames

							a_		:	Any									=   np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]       )
							assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1e-2












'''simple docstring'''







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
__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

__lowerCAmelCase									=   {
    # 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 (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   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
												):
																a_		:	Tuple									=   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}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, Any]									=   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:
																				a_		:	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
				a_		:	Tuple									=   [
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				a_		:	Tuple									=   ['''encoder_no_repeat_ngram_size''']

				# Special cases to be allowed
				a_		:	Optional[int]									=   True
				if not attribute_used:
								a_		:	Optional[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:
																a_		:	Dict									=   True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																a_		:	List[str]									=   True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																a_		:	Optional[int]									=   True
												elif attribute.endswith('''_token_id'''   ):
																a_		:	Tuple									=   True

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

				return attribute_used or case_allowed





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	str									=   dict(inspect.signature(config_class.__init__   ).parameters   )
				a_		:	List[str]									=   [x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
				a_		:	Optional[Any]									=   [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
				a_		:	int									=   {}
				if len(config_class.attribute_map   ) > 0:
								a_		:	List[str]									=   {v: k for k, v in config_class.attribute_map.items()}

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

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

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

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   {}
				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.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	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(__A   )


if __name__ == "__main__":
		check_config_attributes()




