






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     a    ( unittest.TestCase   ):



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Union[str, Any]           =  """ylacombe/bark-small"""
        __UpperCAmelCase							:	Tuple           =  tempfile.mkdtemp()
        __UpperCAmelCase							:	str           =  """en_speaker_1"""
        __UpperCAmelCase							:	List[str]           =  """This is a test string"""
        __UpperCAmelCase							:	List[str]           =  """speaker_embeddings_path.json"""
        __UpperCAmelCase							:	int           =  """speaker_embeddings"""
      def 	UpperCAmelCase      (      self					:			Any		,      **__lowercase					:			Optional[int]					)							->      int:
        return AutoTokenizer.from_pretrained(self.checkpoint		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			str					)							->      Any:
        shutil.rmtree(self.tmpdirname					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Optional[int]           =  self.get_tokenizer()

        __UpperCAmelCase							:	Tuple           =  BarkProcessor(tokenizer=__lowercase					)

        processor.save_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	Tuple           =  BarkProcessor.from_pretrained(self.tmpdirname					)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer.get_vocab()					)
      @slow
      def 	UpperCAmelCase      (      self					:			Any					)							->      str:
        __UpperCAmelCase							:	Optional[Any]           =  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		,      )

        __UpperCAmelCase							:	List[Any]           =  self.get_tokenizer(bos_token="""(BOS)"""		,      eos_token="""(EOS)"""					)

        __UpperCAmelCase							:	Optional[Any]           =  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 	UpperCAmelCase      (      self					:			Dict					)							->      str:
        __UpperCAmelCase							:	Dict           =  BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint		,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,      )

        __UpperCAmelCase							:	Optional[int]           =  35
        __UpperCAmelCase							:	Optional[Any]           =  2
        __UpperCAmelCase							:	List[str]           =  8

        __UpperCAmelCase							:	Optional[int]           =  {
            """semantic_prompt""": np.ones(__lowercase					),
            """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
            """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
        }

        # test providing already loaded voice_preset
        __UpperCAmelCase							:	Optional[Any]           =  processor(text=self.input_string		,      voice_preset=__lowercase					)

        __UpperCAmelCase							:	List[Any]           =  inputs["""history_prompt"""]
        for key in voice_preset:
          self.assertListEqual(voice_preset[key].tolist()		,      processed_voice_preset.get(__lowercase		,      np.array([]					)					).tolist()					)

        # test loading voice preset from npz file
        __UpperCAmelCase							:	Union[str, Any]           =  os.path.join(self.tmpdirname		,      """file.npz"""					)
        np.savez(__lowercase		,      **__lowercase					)
        __UpperCAmelCase							:	Dict           =  processor(text=self.input_string		,      voice_preset=__lowercase					)
        __UpperCAmelCase							:	Any           =  inputs["""history_prompt"""]

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

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




      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Any:
        __UpperCAmelCase							:	Union[str, Any]           =  self.get_tokenizer()

        __UpperCAmelCase							:	int           =  BarkProcessor(tokenizer=__lowercase					)

        __UpperCAmelCase							:	Union[str, Any]           =  processor(text=self.input_string					)

        __UpperCAmelCase							:	int           =  tokenizer(
            self.input_string		,      padding="""max_length"""		,      max_length=256		,      add_special_tokens=__lowercase		,      return_attention_mask=__lowercase		,      return_token_type_ids=__lowercase		,      )

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










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Dict			     = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	't5'
      a  : Dict							       =	['past_key_values']
      a  : Tuple							       =	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
      def __init__(      self					:			str		,      __lowercase					:			Optional[int]=32128		,      __lowercase					:			Optional[int]=512		,      __lowercase					:			int=64		,      __lowercase					:			Any=2048		,      __lowercase					:			Tuple=6		,      __lowercase					:			Tuple=None		,      __lowercase					:			int=8		,      __lowercase					:			List[Any]=32		,      __lowercase					:			Dict=128		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			int=1e-6		,      __lowercase					:			List[str]=1.0		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Dict=True		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Tuple=0		,      __lowercase					:			List[str]=1		,      **__lowercase					:			Any		,      )							->      str:
        __UpperCAmelCase							:	Tuple           =  vocab_size
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Union[str, Any]           =  d_kv
        __UpperCAmelCase							:	Union[str, Any]           =  d_ff
        __UpperCAmelCase							:	int           =  num_layers
        __UpperCAmelCase							:	Any           =  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCAmelCase							:	Dict           =  num_heads
        __UpperCAmelCase							:	List[Any]           =  relative_attention_num_buckets
        __UpperCAmelCase							:	List[str]           =  relative_attention_max_distance
        __UpperCAmelCase							:	Union[str, Any]           =  dropout_rate
        __UpperCAmelCase							:	List[str]           =  layer_norm_epsilon
        __UpperCAmelCase							:	str           =  initializer_factor
        __UpperCAmelCase							:	Dict           =  feed_forward_proj
        __UpperCAmelCase							:	Optional[int]           =  use_cache

        __UpperCAmelCase							:	List[Any]           =  self.feed_forward_proj.split("""-"""					)
        __UpperCAmelCase							:	Tuple           =  act_info[-1]
        __UpperCAmelCase							:	int           =  act_info[0] == """gated"""

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 2:
          raise ValueError(
              f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
              """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
              """'gated-gelu' or 'relu'"""					)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          __UpperCAmelCase							:	Dict           =  """gelu_new"""

        super().__init__(
            pad_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      **__lowercase		,      )


class     a    ( lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase							:	Union[str, Any]           =  {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
          __UpperCAmelCase							:	List[Any]           =  """past_encoder_sequence + sequence"""
          __UpperCAmelCase							:	Optional[int]           =  {0: """batch"""}
          __UpperCAmelCase							:	Union[str, Any]           =  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
          self.fill_with_past_key_values_(__lowercase		,      direction="""inputs"""					)

        return common_inputs




      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      int:
        return 13


