



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     tempfile.mkdtemp()

								# fmt: off
								A__	  =     ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
								# fmt: on
								A__	  =     dict(zip(_snake_case					,       range(len(_snake_case       )       )       )       )
								A__	  =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								A__	  =     {'unk_token': '<unk>'}

								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['vocab_file']       )
								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['merges_file']       )
								with open(self.vocab_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write(json.dumps(_snake_case       ) + '\n'       )
								with open(self.merges_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write('\n'.join(_snake_case       )       )

								A__	  =     {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
								    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								A__	  =     os.path.join(self.tmpdirname					,       _snake_case       )
								with open(self.image_processor_file					,       'w'					,       encoding='utf-8'       ) as fp:
											json.dump(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]					,       **_snake_case       :							str       ):






								"""simple docstring"""


								return CLIPTokenizer.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							str       ):






								"""simple docstring"""


								return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							int					,       **_snake_case       :							List[str]       ):






								"""simple docstring"""


								return CLIPImageProcessor.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     [np.random.randint(2_55					,       size=(3, 30, 4_00)					,       dtype=np.uinta       )]

								A__	  =     [Image.fromarray(np.moveaxis(_snake_case					,       0					,       -1       )       ) for x in image_inputs]

								return image_inputs



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.get_tokenizer()
								A__	  =     self.get_rust_tokenizer()
								A__	  =     self.get_image_processor()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_slow.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPProcessor.from_pretrained(self.tmpdirname					,       use_fast=_snake_case       )

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_fast.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPProcessor.from_pretrained(self.tmpdirname       )

								self.assertEqual(processor_slow.tokenizer.get_vocab()					,       tokenizer_slow.get_vocab()       )
								self.assertEqual(processor_fast.tokenizer.get_vocab()					,       tokenizer_fast.get_vocab()       )
								self.assertEqual(tokenizer_slow.get_vocab()					,       tokenizer_fast.get_vocab()       )
								self.assertIsInstance(processor_slow.tokenizer					,       _snake_case       )
								self.assertIsInstance(processor_fast.tokenizer					,       _snake_case       )

								self.assertEqual(processor_slow.image_processor.to_json_string()					,       image_processor.to_json_string()       )
								self.assertEqual(processor_fast.image_processor.to_json_string()					,       image_processor.to_json_string()       )
								self.assertIsInstance(processor_slow.image_processor					,       _snake_case       )
								self.assertIsInstance(processor_fast.image_processor					,       _snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     CLIPProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								A__	  =     self.get_tokenizer(bos_token='(BOS)'					,       eos_token='(EOS)'       )
								A__	  =     self.get_image_processor(do_normalize=_snake_case					,       padding_value=1.0       )

								A__	  =     CLIPProcessor.from_pretrained(
								    self.tmpdirname					,       bos_token='(BOS)'					,       eos_token='(EOS)'					,       do_normalize=_snake_case					,       padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer					,       _snake_case       )

								self.assertEqual(processor.image_processor.to_json_string()					,       image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor					,       _snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()

								A__	  =     image_processor(_snake_case					,       return_tensors='np'       )
								A__	  =     processor(images=_snake_case					,       return_tensors='np'       )

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()					,       input_processor[key].sum()					,       delta=1E-2       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'

								A__	  =     processor(text=_snake_case       )

								A__	  =     tokenizer(_snake_case       )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

								self.assertListEqual(list(inputs.keys()       )					,       ['input_ids', 'attention_mask', 'pixel_values']       )

								# test if it raises when no input is passed
								with pytest.raises(_snake_case       ):
											processor()



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								A__	  =     processor.batch_decode(_snake_case       )
								A__	  =     tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

								self.assertListEqual(list(inputs.keys()       )					,       processor.model_input_names       )






"""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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  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.'''							)

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

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         with torch.no_grad():
           return self.model.generate_speech(**A_							)



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         with torch.no_grad():
           return self.post_processor(A_							).cpu().detach()
