
import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[str]			  =       AudioLDMPipeline
		__lowercase : Optional[Any]			  =       TEXT_TO_AUDIO_PARAMS
		__lowercase : Tuple			  =       TEXT_TO_AUDIO_BATCH_PARAMS
		__lowercase : Dict			  =       frozenset(
		    [
		        "num_inference_steps",
		        "num_waveforms_per_prompt",
		        "generator",
		        "latents",
		        "output_type",
		        "return_dict",
		        "callback",
		        "callback_steps",
		    ]     )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0 )
				UpperCamelCase						=  UNetaDConditionModel(
				    block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,   cross_attention_dim=(32, 64)       ,   class_embed_type='simple_projection'       ,   projection_class_embeddings_input_dim=32       ,   class_embeddings_concat=A_       ,   )
				UpperCamelCase						=  DDIMScheduler(
				    beta_start=0.0_0085       ,   beta_end=0.012       ,   beta_schedule='scaled_linear'       ,   clip_sample=A_       ,   set_alpha_to_one=A_       ,   )
				torch.manual_seed(0 )
				UpperCamelCase						=  AutoencoderKL(
				    block_out_channels=[32, 64]       ,   in_channels=1       ,   out_channels=1       ,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,   latent_channels=4       ,   )
				torch.manual_seed(0 )
				UpperCamelCase						=  ClapTextConfig(
				    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=1_000       ,   projection_dim=32       ,   )
				UpperCamelCase						=  ClapTextModelWithProjection(A_ )
				UpperCamelCase						=  RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'       ,   model_max_length=77 )

				UpperCamelCase						=  SpeechTaHifiGanConfig(
				    model_in_dim=8       ,   sampling_rate=16_000       ,   upsample_initial_channel=16       ,   upsample_rates=[2, 2]       ,   upsample_kernel_sizes=[4, 4]       ,   resblock_kernel_sizes=[3, 7]       ,   resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]       ,   normalize_before=A_       ,   )

				UpperCamelCase						=  SpeechTaHifiGan(A_ )

				UpperCamelCase						=  {
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'vocoder': vocoder,
				}
				return components



		def __UpperCamelCase				(				self       ,   A_       ,   A_=0 )						->      Dict:
				"""simple docstring"""
				if str(A_ ).startswith('mps' ):
						UpperCamelCase						=  torch.manual_seed(A_ )
				else:
						UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
				UpperCamelCase						=  {
				    'prompt': 'A hammer hitting a wooden surface',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				}
				return inputs



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator

				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  audioldm_pipe(**A_ )
				UpperCamelCase						=  output.audios[0]

				assert audio.ndim == 1
				assert len(A_ ) == 256

				UpperCamelCase						=  audio[:10]
				UpperCamelCase						=  np.array(
				    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )

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



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * [inputs['prompt']]

				# forward
				UpperCamelCase						=  audioldm_pipe(**A_ )
				UpperCamelCase						=  output.audios[0]

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * [inputs.pop('prompt' )]

				UpperCamelCase						=  audioldm_pipe.tokenizer(
				    A_       ,   padding='max_length'       ,   max_length=audioldm_pipe.tokenizer.model_max_length       ,   truncation=A_       ,   return_tensors='pt'       ,   )
				UpperCamelCase						=  text_inputs['input_ids'].to(A_ )

				UpperCamelCase						=  audioldm_pipe.text_encoder(
				    A_       ,   )
				UpperCamelCase						=  prompt_embeds.text_embeds
				# additional L_2 normalization over each hidden-state
				UpperCamelCase						=  F.normalize(A_       ,   dim=-1 )

				UpperCamelCase						=  prompt_embeds

				# forward
				UpperCamelCase						=  audioldm_pipe(**A_ )
				UpperCamelCase						=  output.audios[0]

				assert np.abs(audio_a - audio_a ).max() < 1e-2



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * ['this is a negative prompt']
				UpperCamelCase						=  negative_prompt
				UpperCamelCase						=  3 * [inputs['prompt']]

				# forward
				UpperCamelCase						=  audioldm_pipe(**A_ )
				UpperCamelCase						=  output.audios[0]

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * [inputs.pop('prompt' )]

				UpperCamelCase						=  []
				for p in [prompt, negative_prompt]:
						UpperCamelCase						=  audioldm_pipe.tokenizer(
						    A_       ,   padding='max_length'       ,   max_length=audioldm_pipe.tokenizer.model_max_length       ,   truncation=A_       ,   return_tensors='pt'       ,   )
						UpperCamelCase						=  text_inputs['input_ids'].to(A_ )

						UpperCamelCase						=  audioldm_pipe.text_encoder(
						    A_       ,   )
						UpperCamelCase						=  text_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						UpperCamelCase						=  F.normalize(A_       ,   dim=-1 )

						embeds.append(A_ )

				UpperCamelCase			,						UpperCamelCase						=  embeds

				# forward
				UpperCamelCase						=  audioldm_pipe(**A_ )
				UpperCamelCase						=  output.audios[0]

				assert np.abs(audio_a - audio_a ).max() < 1e-2



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  PNDMScheduler(skip_prk_steps=A_ )
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  'egg cracking'
				UpperCamelCase						=  audioldm_pipe(**A_       ,   negative_prompt=A_ )
				UpperCamelCase						=  output.audios[0]

				assert audio.ndim == 1
				assert len(A_ ) == 256

				UpperCamelCase						=  audio[:10]
				UpperCamelCase						=  np.array(
				    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )

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



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  PNDMScheduler(skip_prk_steps=A_ )
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  'A hammer hitting a wooden surface'

				# test num_waveforms_per_prompt=1 (default)
				UpperCamelCase						=  audioldm_pipe(A_       ,   num_inference_steps=2 ).audios

				assert audios.shape == (1, 256)

				# test num_waveforms_per_prompt=1 (default) for batch of prompts
				UpperCamelCase						=  2
				UpperCamelCase						=  audioldm_pipe([prompt] * batch_size       ,   num_inference_steps=2 ).audios

				assert audios.shape == (batch_size, 256)

				# test num_waveforms_per_prompt for single prompt
				UpperCamelCase						=  2
				UpperCamelCase						=  audioldm_pipe(A_       ,   num_inference_steps=2       ,   num_waveforms_per_prompt=A_ ).audios

				assert audios.shape == (num_waveforms_per_prompt, 256)

				# test num_waveforms_per_prompt for batch of prompts
				UpperCamelCase						=  2
				UpperCamelCase						=  audioldm_pipe(
				    [prompt] * batch_size       ,   num_inference_steps=2       ,   num_waveforms_per_prompt=A_ ).audios

				assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )
				UpperCamelCase						=  audioldm_pipe.vocoder.config.sampling_rate

				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  audioldm_pipe(audio_length_in_s=0.016       ,   **A_ )
				UpperCamelCase						=  output.audios[0]

				assert audio.ndim == 1
				assert len(A_ ) / vocoder_sampling_rate == 0.016

				UpperCamelCase						=  audioldm_pipe(audio_length_in_s=0.032       ,   **A_ )
				UpperCamelCase						=  output.audios[0]

				assert audio.ndim == 1
				assert len(A_ ) / vocoder_sampling_rate == 0.032



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  AudioLDMPipeline(**A_ )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  ['hey']

				UpperCamelCase						=  audioldm_pipe(A_       ,   num_inference_steps=1 )
				UpperCamelCase						=  output.audios.shape
				assert audio_shape == (1, 256)

				UpperCamelCase						=  audioldm_pipe.vocoder.config
				config.model_in_dim *= 2
				UpperCamelCase						=  SpeechTaHifiGan(A_ ).to(A_ )
				UpperCamelCase						=  audioldm_pipe(A_       ,   num_inference_steps=1 )
				UpperCamelCase						=  output.audios.shape
				# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
				assert audio_shape == (1, 256)



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self._test_inference_batch_single_identical(test_mean_pixel_difference=A_ )



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()       ,   reason='XFormers attention is only available with CUDA and `xformers` installed'       ,   )
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_ )






@slow
class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase				(				self       ,   A_       ,   A_="cpu"       ,   A_=torch.floataa       ,   A_=0 )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
				UpperCamelCase						=  np.random.RandomState(A_ ).standard_normal((1, 8, 128, 16) )
				UpperCamelCase						=  torch.from_numpy(A_ ).to(device=A_       ,   dtype=A_ )
				UpperCamelCase						=  {
				    'prompt': 'A hammer hitting a wooden surface',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 2.5,
				}
				return inputs



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_inputs(A_ )
				UpperCamelCase						=  25
				UpperCamelCase						=  audioldm_pipe(**A_ ).audios[0]

				assert audio.ndim == 1
				assert len(A_ ) == 81_920

				UpperCamelCase						=  audio[77_230:77_240]
				UpperCamelCase						=  np.array(
				    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
				UpperCamelCase						=  np.abs(expected_slice - audio_slice ).max()
				assert max_diff < 1e-2



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
				UpperCamelCase						=  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
				UpperCamelCase						=  audioldm_pipe.to(A_ )
				audioldm_pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  self.get_inputs(A_ )
				UpperCamelCase						=  audioldm_pipe(**A_ ).audios[0]

				assert audio.ndim == 1
				assert len(A_ ) == 81_920

				UpperCamelCase						=  audio[27_780:27_790]
				UpperCamelCase						=  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
				UpperCamelCase						=  np.abs(expected_slice - audio_slice ).max()
				assert max_diff < 3e-2


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase      :				Union[str, Any]          = {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				Dict          = [
					    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GitForCausalLM",
					    "GitModel",
					    "GitPreTrainedModel",
					    "GitVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
					from .processing_git import GitProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_git import (
										    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GitForCausalLM,
										    GitModel,
										    GitPreTrainedModel,
										    GitVisionModel,
										)

else:
					import sys

					_UpperCAmelCase      :				int          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
