
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM

@require_torch
class __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Optional[Any]       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(snake_case      )
           lowercase							:     Tuple       	=      -1

           lowercase							:     Any       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(snake_case      )
           lowercase							:     Dict       	=      model.generate(snake_case		,max_new_tokens=10		,do_sample=snake_case      )
           lowercase							:     Union[str, Any]       	=      tokenizer.decode(greedy_ids[0]      )

           with CaptureStdout() as cs:
                  lowercase							:     Optional[Any]       	=      TextStreamer(snake_case      )
                  model.generate(snake_case		,max_new_tokens=10		,do_sample=snake_case		,streamer=snake_case      )
           # The greedy text should be printed to stdout, except for the final "\n" in the streamer
           lowercase							:     str       	=      cs.out[:-1]

           self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Dict       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(snake_case      )
           lowercase							:     Optional[int]       	=      -1

           lowercase							:     int       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(snake_case      )
           lowercase							:     List[str]       	=      model.generate(snake_case		,max_new_tokens=10		,do_sample=snake_case      )
           lowercase							:     Any       	=      tokenizer.decode(greedy_ids[0]      )

           lowercase							:     Optional[int]       	=      TextIteratorStreamer(snake_case      )
           lowercase							:     Tuple       	=      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
           lowercase							:     Optional[int]       	=      Thread(target=model.generate		,kwargs=snake_case      )
           thread.start()
           lowercase							:     List[str]       	=      """"""
           for new_text in streamer:
                  streamer_text += new_text

           self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Tuple       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(snake_case      )
           lowercase							:     Union[str, Any]       	=      -1

           lowercase							:     Optional[Any]       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(snake_case      )
           lowercase							:     Tuple       	=      model.generate(snake_case		,max_new_tokens=10		,do_sample=snake_case      )
           lowercase							:     Optional[int]       	=      greedy_ids[:, input_ids.shape[1] :]
           lowercase							:     Dict       	=      tokenizer.decode(new_greedy_ids[0]      )

           with CaptureStdout() as cs:
                  lowercase							:     Any       	=      TextStreamer(snake_case		,skip_prompt=snake_case      )
                  model.generate(snake_case		,max_new_tokens=10		,do_sample=snake_case		,streamer=snake_case      )
           # The greedy text should be printed to stdout, except for the final "\n" in the streamer
           lowercase							:     int       	=      cs.out[:-1]

           self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      AutoTokenizer.from_pretrained("""distilgpt2"""      )
           lowercase							:     List[Any]       	=      AutoModelForCausalLM.from_pretrained("""distilgpt2"""      ).to(snake_case      )
           lowercase							:     List[Any]       	=      -1

           lowercase							:     Tuple       	=      torch.ones((1, 5)		,device=snake_case      ).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
                  lowercase							:     str       	=      TextStreamer(snake_case		,skip_special_tokens=snake_case      )
                  model.generate(snake_case		,max_new_tokens=1		,do_sample=snake_case		,streamer=snake_case      )

           # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
           # re-tokenized, must only contain one token
           lowercase							:     Dict       	=      cs.out[:-1]  # Remove the final "\n"
           lowercase							:     List[str]       	=      tokenizer(snake_case		,return_tensors="""pt"""      )
           self.assertEqual(streamer_text_tokenized.input_ids.shape		,(1, 1)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Any       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(snake_case      )
           lowercase							:     Tuple       	=      -1

           lowercase							:     List[str]       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(snake_case      )
           lowercase							:     Tuple       	=      TextIteratorStreamer(snake_case		,timeout=0.001      )
           lowercase							:     List[str]       	=      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
           lowercase							:     int       	=      Thread(target=model.generate		,kwargs=snake_case      )
           thread.start()

           # The streamer will timeout after 0.001 seconds, so an exception will be raised
           with self.assertRaises(snake_case      ):
                  lowercase							:     str       	=      """"""
                  for new_text in streamer:
                         streamer_text += new_text


# 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		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "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		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

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

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







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