

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 SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	AudioLDMPipeline
			__A    	=	TEXT_TO_AUDIO_PARAMS
			__A    	=	TEXT_TO_AUDIO_BATCH_PARAMS
			__A    	=	frozenset(
			    [
			        """num_inference_steps""",
			        """num_waveforms_per_prompt""",
			        """generator""",
			        """latents""",
			        """output_type""",
			        """return_dict""",
			        """callback""",
			        """callback_steps""",
			    ]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			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=__UpperCamelCase			,					)
				snake_case_   		=			DDIMScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_one=__UpperCamelCase			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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				)
				snake_case_   		=			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=10_00			,					projection_dim=32			,					)
				snake_case_   		=			ClapTextModelWithProjection(__UpperCamelCase				)
				snake_case_   		=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'			,					model_max_length=77				)

				snake_case_   		=			SpeechTaHifiGanConfig(
				    model_in_dim=8			,					sampling_rate=1_60_00			,					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=__UpperCamelCase			,					)

				snake_case_   		=			SpeechTaHifiGan(__UpperCamelCase				)

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A hammer hitting a wooden surface',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 2_56

				snake_case_   		=			audio[:10]
				snake_case_   		=			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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs['prompt']]

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs.pop('prompt'				)]

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

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

				snake_case_   		=			prompt_embeds

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs.pop('prompt'				)]

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

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

					embeds.append(__UpperCamelCase				)

				snake_case_		,							snake_case_   		=			embeds

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 2_56

				snake_case_   		=			audio[:10]
				snake_case_   		=			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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'A hammer hitting a wooden surface'

				# test num_waveforms_per_prompt=1 (default)
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=2				).audios

				assert audios.shape == (1, 2_56)

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

				assert audios.shape == (batch_size, 2_56)

				# test num_waveforms_per_prompt for single prompt
				snake_case_   		=			2
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=2			,					num_waveforms_per_prompt=__UpperCamelCase				).audios

				assert audios.shape == (num_waveforms_per_prompt, 2_56)

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

				assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.vocoder.config.sampling_rate

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

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

				snake_case_   		=			audioldm_pipe(audio_length_in_s=0.032			,					**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			['hey']

				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=1				)
				snake_case_   		=			output.audios.shape
				assert audio_shape == (1, 2_56)

				snake_case_   		=			audioldm_pipe.vocoder.config
				config.model_in_dim *= 2
				snake_case_   		=			SpeechTaHifiGan(__UpperCamelCase				).to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=1				)
				snake_case_   		=			output.audios.shape
				# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
				assert audio_shape == (1, 2_56)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_inference_batch_single_identical(test_mean_pixel_difference=__UpperCamelCase				)







			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()			,					reason='XFormers attention is only available with CUDA and `xformers` installed'			,					)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCamelCase				)







@slow
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase="cpu"			,					__UpperCamelCase=torch.floataa			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			np.random.RandomState(__UpperCamelCase				).standard_normal((1, 8, 1_28, 16)				)
				snake_case_   		=			torch.from_numpy(__UpperCamelCase				).to(device=__UpperCamelCase			,					dtype=__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A hammer hitting a wooden surface',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 2.5,
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_inputs(__UpperCamelCase				)
				snake_case_   		=			25
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				).audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 8_19_20

				snake_case_   		=			audio[7_72_30:7_72_40]
				snake_case_   		=			np.array(
				    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]				)
				snake_case_   		=			np.abs(expected_slice - audio_slice				).max()
				assert max_diff < 1E-2







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'				)
				snake_case_   		=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_inputs(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				).audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 8_19_20

				snake_case_   		=			audio[2_77_80:2_77_90]
				snake_case_   		=			np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]				)
				snake_case_   		=			np.abs(expected_slice - audio_slice				).max()
				assert max_diff < 3E-2



from jiwer import compute_measures

import datasets


A									=  '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A									=  '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A									=  '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,    _KWARGS_DESCRIPTION				)
class SCREAMING_SNAKE_CASE	(		datasets.Metric				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return datasets.MetricInfo(
				    description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
				        {
				            'predictions': datasets.Value('string'			,					id='sequence'				),
				            'references': datasets.Value('string'			,					id='sequence'				),
				        }				)			,					codebase_urls=['https://github.com/jitsi/jiwer/']			,					reference_urls=[
				        'https://en.wikipedia.org/wiki/Word_error_rate',
				    ]			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return compute_measures(__UpperCamelCase			,					__UpperCamelCase				)["wer"]
				else:
					snake_case_   		=			0
					snake_case_   		=			0
					for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			compute_measures(__UpperCamelCase			,					__UpperCamelCase				)
						incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
						total += measures["substitutions"] + measures["deletions"] + measures["hits"]
					return incorrect / total
