






'''simple docstring'''







import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ASTForAudioClassification, ASTModel
			from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
			    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_torchaudio_available():
			import torchaudio

			from transformers import ASTFeatureExtractor

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Optional[Any]=13			,							UpperCamelCase__:				Optional[Any]=2			,							UpperCamelCase__:				List[str]=24			,							UpperCamelCase__:				Optional[int]=16			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Optional[int]=True			,							UpperCamelCase__:				Any=32			,							UpperCamelCase__:				Tuple=5			,							UpperCamelCase__:				Union[str, Any]=4			,							UpperCamelCase__:				str=37			,							UpperCamelCase__:				Any="gelu"			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				Any=0.1			,							UpperCamelCase__:				str=10			,							UpperCamelCase__:				int=0.02			,							UpperCamelCase__:				str=None			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				Optional[Any]=2			,							):
														lowerCamelCase__					:	Dict         =       parent
														lowerCamelCase__					:	List[Any]         =       batch_size
														lowerCamelCase__					:	List[str]         =       patch_size
														lowerCamelCase__					:	Union[str, Any]         =       max_length
														lowerCamelCase__					:	Union[str, Any]         =       num_mel_bins
														lowerCamelCase__					:	Tuple         =       is_training
														lowerCamelCase__					:	Union[str, Any]         =       use_labels
														lowerCamelCase__					:	str         =       hidden_size
														lowerCamelCase__					:	Dict         =       num_hidden_layers
														lowerCamelCase__					:	Dict         =       num_attention_heads
														lowerCamelCase__					:	int         =       intermediate_size
														lowerCamelCase__					:	Optional[int]         =       hidden_act
														lowerCamelCase__					:	List[str]         =       hidden_dropout_prob
														lowerCamelCase__					:	str         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Union[str, Any]         =       type_sequence_label_size
														lowerCamelCase__					:	Dict         =       initializer_range
														lowerCamelCase__					:	Any         =       scope
														lowerCamelCase__					:	Any         =       frequency_stride
														lowerCamelCase__					:	Optional[Any]         =       time_stride

														# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
														lowerCamelCase__					:	Optional[Any]         =       (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
														lowerCamelCase__					:	List[Any]         =       (self.max_length - self.patch_size) // self.time_stride + 1
														lowerCamelCase__					:	Dict         =       frequency_out_dimension * time_out_dimension
														lowerCamelCase__					:	str         =       num_patches + 2




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	List[Any]         =       floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]     )

														lowerCamelCase__					:	Any         =       None
														if self.use_labels:
																					lowerCamelCase__					:	int         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

														lowerCamelCase__					:	List[str]         =       self.get_config()

														return config, input_values, labels




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														return ASTConfig(
														    patch_size=self.patch_size			,							max_length=self.max_length			,							num_mel_bins=self.num_mel_bins			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							frequency_stride=self.frequency_stride			,							time_stride=self.time_stride			,							)




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	int         =       ASTModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		
														)					:	Tuple         =       config_and_inputs
														lowerCamelCase__					:	Tuple         =       {"""input_values""": input_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (
							    (
							        ASTModel,
							        ASTForAudioClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							a				      =       (
							    {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
							    if is_torch_available()
							    else {}
							)
							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Optional[int]     ):
														if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																					return True

														return False




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	str         =       ASTModelTester(self     )
														lowerCamelCase__					:	List[str]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Any     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""AST does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Dict     ):
														pass




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Any         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)     )
																					lowerCamelCase__					:	Dict         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							nn.Linear     )     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	str         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Tuple         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	List[Any]         =       ["""input_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Union[str, Any]         =       ASTModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Optional[Any]         =       hf_hub_download(
							    repo_id="""nielsr/audio-spectogram-transformer-checkpoint"""				,			filename="""sample_audio.flac"""				,			repo_type="""dataset"""      )

							lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       torchaudio.load(UpperCamelCase      )

							return audio, sampling_rate



@require_torch
@require_torchaudio
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				str     ):
														return (
														    ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""     )
														    if is_torchaudio_available()
														    else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       self.default_feature_extractor
														lowerCamelCase__					:	List[Any]         =       ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Union[str, Any]         =       self.default_feature_extractor
														lowerCamelCase__  ,		lowerCamelCase__					:	str         =       prepare_audio()
														lowerCamelCase__					:	Optional[int]         =       audio.squeeze().numpy()
														lowerCamelCase__					:	Optional[int]         =       feature_extractor(UpperCamelCase__			,							sampling_rate=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	Union[str, Any]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Tuple         =       torch.Size((1, 527)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       torch.tensor([-0.8_760, -7.0_042, -8.6_602]     ).to(UpperCamelCase__     )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_snake_case										=       logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case										=       "\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"







@dataclass
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()

           self.register_modules(
               prior=_a		,     image_encoder=_a		,     image_processor=_a		,     scheduler=_a		,     renderer=_a		,     )

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							str:
           if latents is None:
                 _A							:     str			=						randn_tensor(_a		,     generator=_a		,     device=_a		,     dtype=_a				)
           else:
                 if latents.shape != shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
                 _A							:     Union[str, Any]			=						latents.to(_a				)

           _A							:     int			=						latents * scheduler.init_noise_sigma
           return latents

     def 					a__	(  self		,     _a=0				)						->							Optional[Any]:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

           _A							:     str			=						torch.device(F'''cuda:{gpu_id}'''				)

           _A							:     Any			=						[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a		,     _a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           if self.device != torch.device("""meta"""				) or not hasattr(self.image_encoder		,     """_hf_hook"""				):
                 return self.device
           for module in self.image_encoder.modules():
                 if (
                     hasattr(_a		,     """_hf_hook"""				)
                     and hasattr(module._hf_hook		,     """execution_device"""				)
                     and module._hf_hook.execution_device is not None
                 ):
                       return torch.device(module._hf_hook.execution_device				)
           return self.device

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							Tuple:
           if isinstance(_a		,     _a				) and isinstance(image[0]		,     torch.Tensor				):
                 _A							:     int			=						torch.cat(_a		,     axis=0				) if image[0].ndim == 4 else torch.stack(_a		,     axis=0				)

           if not isinstance(_a		,     torch.Tensor				):
                 _A							:     Dict			=						self.image_processor(_a		,     return_tensors="""pt"""				).pixel_values[0].unsqueeze(0				)

           _A							:     int			=						image.to(dtype=self.image_encoder.dtype		,     device=_a				)

           _A							:     List[Any]			=						self.image_encoder(_a				)["""last_hidden_state"""]
           _A							:     List[Any]			=						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           _A							:     Dict			=						image_embeds.repeat_interleave(_a		,     dim=0				)

           if do_classifier_free_guidance:
                 _A							:     str			=						torch.zeros_like(_a				)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _A							:     List[str]			=						torch.cat([negative_image_embeds, image_embeds]				)

           return image_embeds



     @torch.no_grad()
     @replace_example_docstring(_a				)
     def __call__(  self		,     _a		,     _a = 1		,     _a = 25		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 64		,     _a = "pil"		,     _a = True		,     )						->							Union[str, Any]:

           if isinstance(_a		,     PIL.Image.Image				):
                 _A							:     List[Any]			=						1
           elif isinstance(_a		,     torch.Tensor				):
                 _A							:     Any			=						image.shape[0]
           elif isinstance(_a		,     _a				) and isinstance(image[0]		,     (torch.Tensor, PIL.Image.Image)				):
                 _A							:     Union[str, Any]			=						len(_a				)
           else:
                 raise ValueError(
                     F'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_a				)}'''				)

           _A							:     Optional[int]			=						self._execution_device

           _A							:     Tuple			=						batch_size * num_images_per_prompt

           _A							:     List[Any]			=						guidance_scale > 1.0
           _A							:     Optional[Any]			=						self._encode_image(_a		,     _a		,     _a		,     _a				)

           # prior

           self.scheduler.set_timesteps(_a		,     device=_a				)
           _A							:     Optional[int]			=						self.scheduler.timesteps

           _A							:     List[str]			=						self.prior.config.num_embeddings
           _A							:     int			=						self.prior.config.embedding_dim

           _A							:     Optional[Any]			=						self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     _a		,     _a		,     _a		,     self.scheduler		,     )

           # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
           _A							:     List[Any]			=						latents.reshape(latents.shape[0]		,     _a		,     _a				)

           for i, t in enumerate(self.progress_bar(_a				)				):
                 # expand the latents if we are doing classifier free guidance
                 _A							:     List[str]			=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                 _A							:     int			=						self.scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						self.prior(
                     _a		,     timestep=_a		,     proj_embedding=_a		,     ).predicted_image_embedding

                 # remove the variance
                 _A			,   _A							:     Optional[Any]			=						noise_pred.split(
                     scaled_model_input.shape[2]		,     dim=2				)  # batch_size, num_embeddings, embedding_dim

                 if do_classifier_free_guidance is not None:
                       _A			,   _A							:     Dict			=						noise_pred.chunk(2				)
                       _A							:     Tuple			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                 _A							:     int			=						self.scheduler.step(
                     _a		,     timestep=_a		,     sample=_a		,     ).prev_sample

           if output_type == "latent":
                 return ShapEPipelineOutput(images=_a				)

           _A							:     List[str]			=						[]
           for i, latent in enumerate(_a				):
                 print()
                 _A							:     List[str]			=						self.renderer.decode(
                     latent[None, :]		,     _a		,     size=_a		,     ray_batch_size=4096		,     n_coarse_samples=64		,     n_fine_samples=128		,     )

                 images.append(_a				)

           _A							:     List[Any]			=						torch.stack(_a				)

           if output_type not in ["np", "pil"]:
                 raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}'''				)

           _A							:     List[str]			=						images.cpu().numpy()

           if output_type == "pil":
                 _A							:     List[Any]			=						[self.numpy_to_pil(_a				) for image in images]

           # Offload last model to CPU
           if hasattr(self		,     """final_offload_hook"""				) and self.final_offload_hook is not None:
                 self.final_offload_hook.offload()

           if not return_dict:
                 return (images,)

           return ShapEPipelineOutput(images=_a				)

