


import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						DanceDiffusionPipeline
							_UpperCAmelCase      =						UNCONDITIONAL_AUDIO_GENERATION_PARAMS
							_UpperCAmelCase      =						PipelineTesterMixin.required_optional_params - {
							    """callback""",
							    """latents""",
							    """callback_steps""",
							    """output_type""",
							    """num_images_per_prompt""",
							}
							_UpperCAmelCase      =						UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					UNetaDModel(
										    block_out_channels=(3_2, 3_2, 6_4)			,						extra_in_channels=1_6			,						sample_size=5_1_2			,						sample_rate=1_6_0_0_0			,						in_channels=2			,						out_channels=2			,						flip_sin_to_cos=lowerCAmelCase__			,						use_timestep_embedding=lowerCAmelCase__			,						time_embedding_type='fourier'			,						mid_block_type='UNetMidBlock1D'			,						down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')			,						up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')			,						)
										SCREAMING_SNAKE_CASE_		:     str					=					IPNDMScheduler()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
										    'unet': unet,
										    'scheduler': scheduler,
										}
										return components






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										if str(lowerCAmelCase__       ).startswith('mps'       ):
													SCREAMING_SNAKE_CASE_		:     Dict					=					torch.manual_seed(lowerCAmelCase__       )
										else:
													SCREAMING_SNAKE_CASE_		:     Any					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					{
										    'batch_size': 1,
										    'generator': generator,
										    'num_inference_steps': 4,
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					DanceDiffusionPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					output.audios

										SCREAMING_SNAKE_CASE_		:     int					=					audio[0, -3:, -3:]

										assert audio.shape == (1, 2, components["unet"].sample_size)
										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]       )
										assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1E-2






							@skip_mps
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return super().test_save_load_local()






							@skip_mps
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )






							@skip_mps
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return super().test_save_load_optional_components()






							@skip_mps
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return super().test_attention_slicing_forward_pass()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3       )







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					torch_device

										SCREAMING_SNAKE_CASE_		:     Any					=					DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe(generator=lowerCAmelCase__			,						num_inference_steps=1_0_0			,						audio_length_in_s=4.096       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					output.audios

										SCREAMING_SNAKE_CASE_		:     int					=					audio[0, -3:, -3:]

										assert audio.shape == (1, 2, pipe.unet.sample_size)
										SCREAMING_SNAKE_CASE_		:     int					=					np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch_device

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'			,						torch_dtype=torch.floataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					pipe(generator=lowerCAmelCase__			,						num_inference_steps=1_0_0			,						audio_length_in_s=4.096       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					output.audios

										SCREAMING_SNAKE_CASE_		:     Tuple					=					audio[0, -3:, -3:]

										assert audio.shape == (1, 2, pipe.unet.sample_size)
										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]       )

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









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__			:      Tuple								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[int]								={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git_vision_model"""






							def __init__(      self			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__="quick_gelu"			,						lowerCAmelCase__=1E-5			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_channels
										SCREAMING_SNAKE_CASE_		:     Tuple					=					patch_size
										SCREAMING_SNAKE_CASE_		:     Any					=					image_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Any					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										cls._set_token_in_kwargs(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get('model_type'       ) == "git":
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					config_dict['vision_config']

										if "model_type" in config_dict and hasattr(cls			,						'model_type'       ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
													    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

										return cls.from_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3_0_5_2_2			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1E-12			,						lowerCAmelCase__=0			,						lowerCAmelCase__="absolute"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1_0_1			,						lowerCAmelCase__=1_0_2			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						pad_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if vision_config is None:
													SCREAMING_SNAKE_CASE_		:     Dict					=					{}
													logger.info('vision_config is None. initializing the GitVisionConfig with default values.'       )

										SCREAMING_SNAKE_CASE_		:     str					=					GitVisionConfig(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     int					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     int					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     List[str]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_cache
										SCREAMING_SNAKE_CASE_		:     Any					=					tie_word_embeddings
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_image_with_embedding

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					eos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										SCREAMING_SNAKE_CASE_		:     str					=					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output





