





'''simple docstring'''






import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 		lowerCamelCase  (      unittest.TestCase       ):



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    List[str]						=   UNetaDModel(
			    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
			return model



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    int						=   VQModel(
			    block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=3		,      )
			return model



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[Any]						=   CLIPTextConfig(
			    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_000		,      )
			return CLIPTextModel(a_	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.dummy_uncond_unet
			lowerCAmelCase     :    List[str]						=   DDIMScheduler()
			lowerCAmelCase     :    Any						=   self.dummy_vq_model

			lowerCAmelCase     :    List[str]						=   LDMPipeline(unet=a_		,      vqvae=a_		,      scheduler=a_	)
			ldm.to(a_	)
			ldm.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    str						=   torch.manual_seed(0	)
			lowerCAmelCase     :    List[Any]						=   ldm(generator=a_		,      num_inference_steps=2		,      output_type="numpy"	).images

			lowerCAmelCase     :    Optional[int]						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Tuple						=   ldm(generator=a_		,      num_inference_steps=2		,      output_type="numpy"		,      return_dict=a_	)[0]

			lowerCAmelCase     :    Tuple						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    Union[str, Any]						=   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase     :    Optional[Any]						=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
			lowerCAmelCase     :    Union[str, Any]						=   1e-2 if torch_device != "mps" else 3e-2

			assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance






@slow
@require_torch
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
			ldm.to(a_	)
			ldm.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    List[Any]						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Any						=   ldm(generator=a_		,      num_inference_steps=5		,      output_type="numpy"	).images

			lowerCAmelCase     :    Dict						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			lowerCAmelCase     :    int						=   np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
			lowerCAmelCase     :    List[Any]						=   1e-2 if torch_device != "mps" else 3e-2

			assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



