
"""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  ( lowerCAmelCase			):
				a__:      Tuple          =    'microsoft/speecht5_tts'
				a__:      Optional[Any]          =    (
				    '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.'
				)
				a__:      Dict          =    'text_reader'
				a__:      Tuple          =    SpeechTaProcessor
				a__:      int          =    SpeechTaForTextToSpeech
				a__:      str          =    SpeechTaHifiGan

				a__:      Any          =    ['text']
				a__:      Union[str, Any]          =    ['audio']




				def        UpperCAmelCase__						(				self   ):
							if self.post_processor is None:
										lowerCamelCase_					=  '''microsoft/speecht5_hifigan'''
							super().setup()




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							lowerCamelCase_					=  self.pre_processor(text=UpperCAmelCase							,						return_tensors='''pt'''							,						truncation=UpperCAmelCase   )

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

										lowerCamelCase_					=  load_dataset('''Matthijs/cmu-arctic-xvectors'''							,						split='''validation'''   )
										lowerCamelCase_					=  torch.tensor(embeddings_dataset[7305]['''xvector''']   ).unsqueeze(0   )

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							with torch.no_grad():
										return self.model.generate_speech(**UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							with torch.no_grad():
										return self.post_processor(UpperCAmelCase   ).cpu().detach()








"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard

 from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[int]		):
           with tempfile.TemporaryDirectory() as tmpdirname:
                # pipeline has Flax weights
                _UpperCAmelCase     :      Union[str, Any]														=       FlaxDiffusionPipeline.from_pretrained(
                    "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A	,      cache_dir=A		)

                _UpperCAmelCase     :      List[str]														=       [t[-1] for t in os.walk(os.path.join(A	,      os.listdir(A		)[0]	,      "snapshots"		)		)]
                _UpperCAmelCase     :      Union[str, Any]														=       [item for sublist in all_root_files for item in sublist]

                # None of the downloaded files should be a PyTorch file even if we have some here:
                # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
                assert not any(f.endswith(".bin"		) for f in files		)







@slow
@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       FlaxStableDiffusionPipeline.from_pretrained(
               "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      int														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       4

           _UpperCAmelCase     :      Any														=       jax.device_count()
           _UpperCAmelCase     :      Tuple														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Union[str, Any]														=       replicate(A		)
           _UpperCAmelCase     :      List[Any]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      List[Any]														=       shard(A		)

           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
           if jax.device_count() == 8:
                assert np.abs(np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 4.1_514_745		) < 1e-3
                assert np.abs(np.abs(A	,      dtype=np.floataa		).sum() - 49_947.875		) < 5e-1

           _UpperCAmelCase     :      Optional[int]														=       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
           assert len(A		) == num_samples



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="flax"	,      safety_checker=A		)

           _UpperCAmelCase     :      Dict														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Dict														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      str														=       5_0

           _UpperCAmelCase     :      List[str]														=       jax.device_count()
           _UpperCAmelCase     :      Any														=       num_samples * [prompt]
           _UpperCAmelCase     :      Dict														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Dict														=       replicate(A		)
           _UpperCAmelCase     :      str														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Optional[Any]														=       shard(A		)

           _UpperCAmelCase     :      List[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.05_652_401)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_383_808.2)		) < 5e-1



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A		)

           _UpperCAmelCase     :      Any														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      List[Any]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[Any]														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      Optional[int]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Optional[int]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      str														=       shard(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[int]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      Dict														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      str														=       num_samples * [prompt]
           _UpperCAmelCase     :      int														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Optional[int]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Dict														=       shard(A		)

           _UpperCAmelCase     :      Optional[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       FlaxDDIMScheduler(
               beta_start=0.00_085	,      beta_end=0.012	,      beta_schedule="scaled_linear"	,      set_alpha_to_one=A	,      steps_offset=1	,      )

           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      scheduler=A	,      safety_checker=A	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       scheduler.create_state()

           _UpperCAmelCase     :      str														=       scheduler_state

           _UpperCAmelCase     :      Union[str, Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      str														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       5_0

           _UpperCAmelCase     :      Optional[int]														=       jax.device_count()
           _UpperCAmelCase     :      int														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      List[Any]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Union[str, Any]														=       shard(A		)

           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.045_043_945)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_347_693.5)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[Any]														=       jax.device_count()
           _UpperCAmelCase     :      Union[str, Any]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[Any]														=       jax.random.split(jax.random.PRNGKey(0		)	,      A		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      )

           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      int														=       shard(A		)
           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      List[str]														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # With memory efficient attention
           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      use_memory_efficient_attention=A	,      )

           _UpperCAmelCase     :      Any														=       replicate(A		)
           _UpperCAmelCase     :      str														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      Tuple														=       shard(A		)
           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      str														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
           # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
           assert abs(slice_eff - slice		).max() < 1e-2
