
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				"ylacombe/bark-small"
          snake_case__						=				tempfile.mkdtemp()
          snake_case__						=				"en_speaker_1"
          snake_case__						=				"This is a test string"
          snake_case__						=				"speaker_embeddings_path.json"
          snake_case__						=				"speaker_embeddings"
      def A_			(			self		,	**lowerCamelCase ):
          return AutoTokenizer.from_pretrained(self.checkpoint		,	**lowerCamelCase )
      def A_			(			self ):
          shutil.rmtree(self.tmpdirname )
      def A_			(			self ):
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				BarkProcessor(tokenizer=lowerCamelCase )

          processor.save_pretrained(self.tmpdirname )
          snake_case__						=				BarkProcessor.from_pretrained(self.tmpdirname )

          self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer.get_vocab() )
      @slow
      def A_			(			self ):
          snake_case__						=				BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint		,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,	)
          processor.save_pretrained(
              self.tmpdirname		,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,	speaker_embeddings_directory=self.speaker_embeddings_directory		,	)

          snake_case__						=				self.get_tokenizer(bos_token="(BOS)"		,	eos_token="(EOS)" )

          snake_case__						=				BarkProcessor.from_pretrained(
              self.tmpdirname		,	self.speaker_embeddings_dict_path		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	)

          self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer_add_kwargs.get_vocab() )
      def A_			(			self ):
          snake_case__						=				BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint		,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,	)

          snake_case__						=				35
          snake_case__						=				2
          snake_case__						=				8

          snake_case__						=				{
              "semantic_prompt": np.ones(lowerCamelCase ),
              "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
              "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
          }

          # test providing already loaded voice_preset
          snake_case__						=				processor(text=self.input_string		,	voice_preset=lowerCamelCase )

          snake_case__						=				inputs["history_prompt"]
          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()		,	processed_voice_preset.get(lowerCamelCase		,	np.array([] ) ).tolist() )

          # test loading voice preset from npz file
          snake_case__						=				os.path.join(self.tmpdirname		,	"file.npz" )
          np.savez(lowerCamelCase		,	**lowerCamelCase )
          snake_case__						=				processor(text=self.input_string		,	voice_preset=lowerCamelCase )
          snake_case__						=				inputs["history_prompt"]

          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()		,	processed_voice_preset.get(lowerCamelCase		,	np.array([] ) ).tolist() )

          # test loading voice preset from the hub
          snake_case__						=				processor(text=self.input_string		,	voice_preset=self.voice_preset )



      def A_			(			self ):
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				BarkProcessor(tokenizer=lowerCamelCase )

          snake_case__						=				processor(text=self.input_string )

          snake_case__						=				tokenizer(
              self.input_string		,	padding="max_length"		,	max_length=2_56		,	add_special_tokens=lowerCamelCase		,	return_attention_mask=lowerCamelCase		,	return_token_type_ids=lowerCamelCase		,	)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__            =  {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPTokenizerFast''']

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPFeatureExtractor''']
     __magic_name__            =  ['''CLIPImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CLIPModel''',
         '''CLIPPreTrainedModel''',
         '''CLIPTextModel''',
         '''CLIPTextModelWithProjection''',
         '''CLIPVisionModel''',
         '''CLIPVisionModelWithProjection''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCLIPModel''',
         '''TFCLIPPreTrainedModel''',
         '''TFCLIPTextModel''',
         '''TFCLIPVisionModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''FlaxCLIPModel''',
         '''FlaxCLIPPreTrainedModel''',
         '''FlaxCLIPTextModel''',
         '''FlaxCLIPTextPreTrainedModel''',
         '''FlaxCLIPVisionModel''',
         '''FlaxCLIPVisionPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

     __magic_name__            =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



