


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class 		_snake_case  (				unittest.TestCase							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=7 ,  _lowerCamelCase=3 ,  _lowerCamelCase=18 ,  _lowerCamelCase=30 ,  _lowerCamelCase=400 ,  _lowerCamelCase=True ,  _lowerCamelCase=None ,  _lowerCamelCase=True ,  _lowerCamelCase=None ,  _lowerCamelCase=True ,  ):
       a						:Any										=	size if size is not None else {'''shortest_edge''': 20}
       a						:Dict										=	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       a						:Optional[Any]										=	parent
       a						:List[Any]										=	batch_size
       a						:List[str]										=	num_channels
       a						:Union[str, Any]										=	image_size
       a						:Optional[int]										=	min_resolution
       a						:List[str]										=	max_resolution
       a						:Union[str, Any]										=	do_resize
       a						:Optional[int]										=	size
       a						:Optional[int]										=	do_center_crop
       a						:List[Any]										=	crop_size
       a						:int										=	do_flip_channel_order




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }







@require_torch
@require_vision
class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	MobileViTImageProcessor if is_vision_available() else None




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	MobileViTImageProcessingTester(self   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_resize'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''size'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_center_crop'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''center_crop'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_flip_channel_order'''   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size ,  {'''shortest_edge''': 20}   )
       self.assertEqual(image_processor.crop_size ,  {'''height''': 18, '''width''': 18}   )

       a						:Union[str, Any]										=	self.image_processing_class.from_dict(self.image_processor_dict ,  size=42 ,  crop_size=84   )
       self.assertEqual(image_processor.size ,  {'''shortest_edge''': 42}   )
       self.assertEqual(image_processor.crop_size ,  {'''height''': 84, '''width''': 84}   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:List[Any]										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       a						:Dict										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  Image.Image   )

       # Test not batched input
       a						:Tuple										=	image_processing(image_inputs[0] ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )

       # Test batched
       a						:Tuple										=	image_processing(_lowerCamelCase ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:Tuple										=	self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       a						:int										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase ,  numpify=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  np.ndarray   )

       # Test not batched input
       a						:Optional[int]										=	image_processing(image_inputs[0] ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )

       # Test batched
       a						:List[Any]										=	image_processing(_lowerCamelCase ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:Any										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       a						:Optional[Any]										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase ,  torchify=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  torch.Tensor   )

       # Test not batched input
       a						:Union[str, Any]										=	image_processing(image_inputs[0] ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )

       # Test batched
       a						:Tuple										=	image_processing(_lowerCamelCase ,  return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ) ,  )









# 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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'microsoft/speecht5_tts'
 SCREAMING_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.'
 )
 SCREAMING_SNAKE_CASE__				=	'text_reader'
 SCREAMING_SNAKE_CASE__				=	SpeechTaProcessor
 SCREAMING_SNAKE_CASE__				=	SpeechTaForTextToSpeech
 SCREAMING_SNAKE_CASE__				=	SpeechTaHifiGan

 SCREAMING_SNAKE_CASE__				=	['text']
 SCREAMING_SNAKE_CASE__				=	['audio']




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.post_processor is None:
             a						:List[Any]										=	'''microsoft/speecht5_hifigan'''
       super().setup()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:Tuple										=	self.pre_processor(text=_lowerCamelCase ,  return_tensors='''pt''' ,  truncation=_lowerCamelCase   )

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

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

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




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




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





