'''simple docstring'''




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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
             _UpperCAmelCase     :  Optional[int]							      =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A       )
             _UpperCAmelCase     :  Any							      =    -1

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

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

             self.assertEqual(A						,					A       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
             _UpperCAmelCase     :  Dict							      =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A       )
             _UpperCAmelCase     :  List[str]							      =    -1

             _UpperCAmelCase     :  Union[str, Any]							      =    ids_tensor((1, 5)						,					vocab_size=model.config.vocab_size       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    model.generate(A						,					max_new_tokens=10						,					do_sample=A       )
             _UpperCAmelCase     :  List[str]							      =    tokenizer.decode(greedy_ids[0]       )

             _UpperCAmelCase     :  Union[str, Any]							      =    TextIteratorStreamer(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
             _UpperCAmelCase     :  Dict							      =    Thread(target=model.generate						,					kwargs=A       )
             thread.start()
             _UpperCAmelCase     :  List[Any]							      =    ""
             for new_text in streamer:
                    streamer_text += new_text

             self.assertEqual(A						,					A       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
             _UpperCAmelCase     :  Optional[Any]							      =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A       )
             _UpperCAmelCase     :  Dict							      =    -1

             _UpperCAmelCase     :  Tuple							      =    ids_tensor((1, 5)						,					vocab_size=model.config.vocab_size       ).to(A       )
             _UpperCAmelCase     :  str							      =    model.generate(A						,					max_new_tokens=10						,					do_sample=A       )
             _UpperCAmelCase     :  List[Any]							      =    greedy_ids[:, input_ids.shape[1] :]
             _UpperCAmelCase     :  str							      =    tokenizer.decode(new_greedy_ids[0]       )

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

             self.assertEqual(A						,					A       )




      def      _A	(       self						:				Dict       ):
             # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
             # with actual models -- the dummy models' tokenizers are not aligned with their models, and
             # `skip_special_tokens=True` has no effect on them
             _UpperCAmelCase     :  int							      =    AutoTokenizer.from_pretrained("distilgpt2"       )
             _UpperCAmelCase     :  List[str]							      =    AutoModelForCausalLM.from_pretrained("distilgpt2"       ).to(A       )
             _UpperCAmelCase     :  Optional[int]							      =    -1

             _UpperCAmelCase     :  int							      =    torch.ones((1, 5)						,					device=A       ).long() * model.config.bos_token_id
             with CaptureStdout() as cs:
                    _UpperCAmelCase     :  int							      =    TextStreamer(A						,					skip_special_tokens=A       )
                    model.generate(A						,					max_new_tokens=1						,					do_sample=A						,					streamer=A       )

             # 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
             _UpperCAmelCase     :  str							      =    cs.out[:-1]  # Remove the final "\n"
             _UpperCAmelCase     :  int							      =    tokenizer(A						,					return_tensors="pt"       )
             self.assertEqual(streamer_text_tokenized.input_ids.shape						,					(1, 1)       )





      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Tuple							      =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
             _UpperCAmelCase     :  Dict							      =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A       )
             _UpperCAmelCase     :  str							      =    -1

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

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


'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						1




      @register_to_config
      def __init__(       self						:				Optional[int]						,					A						:				int = 1000						,					A						:				Optional[Union[np.ndarray, List[float]]] = None       ):
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(A       )

             # standard deviation of the initial noise distribution
             _UpperCAmelCase     :  int							      =    1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             _UpperCAmelCase     :  int							      =    4

             # running values
             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				int						,					A						:				Union[str, torch.device] = None       ):
             _UpperCAmelCase     :  int							      =    num_inference_steps
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.linspace(1						,					0						,					num_inference_steps + 1       )[:-1]
             _UpperCAmelCase     :  Any							      =    torch.cat([steps, torch.tensor([0.0]       )]       )

             if self.config.trained_betas is not None:
                    _UpperCAmelCase     :  str							      =    torch.tensor(self.config.trained_betas						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.sin(steps * math.pi / 2       ) ** 2

             _UpperCAmelCase     :  List[Any]							      =    (1.0 - self.betas**2) ** 0.5

             _UpperCAmelCase     :  List[str]							      =    (torch.atana(self.betas						,					self.alphas       ) / math.pi * 2)[:-1]
             _UpperCAmelCase     :  Dict							      =    timesteps.to(A       )

             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				torch.FloatTensor						,					A						:				int						,					A						:				torch.FloatTensor						,					A						:				bool = True						,					):
             if self.num_inference_steps is None:
                    raise ValueError(
                        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

             _UpperCAmelCase     :  Tuple							      =    (self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase     :  Optional[Any]							      =    timestep_index + 1

             _UpperCAmelCase     :  int							      =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(A       )

             if len(self.ets       ) == 1:
                    _UpperCAmelCase     :  List[Any]							      =    self.ets[-1]
             elif len(self.ets       ) == 2:
                    _UpperCAmelCase     :  str							      =    (3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets       ) == 3:
                    _UpperCAmelCase     :  Tuple							      =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
             else:
                    _UpperCAmelCase     :  Union[str, Any]							      =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

             _UpperCAmelCase     :  Union[str, Any]							      =    self._get_prev_sample(A						,					A						,					A						,					A       )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					*A						:				Union[str, Any]						,					**A						:				Dict       ):
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[Any]						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    self.alphas[timestep_index]
             _UpperCAmelCase     :  List[Any]							      =    self.betas[timestep_index]

             _UpperCAmelCase     :  Optional[Any]							      =    self.alphas[prev_timestep_index]
             _UpperCAmelCase     :  Dict							      =    self.betas[prev_timestep_index]

             _UpperCAmelCase     :  Tuple							      =    (sample - sigma * ets) / max(A						,					1E-8       )
             _UpperCAmelCase     :  List[str]							      =    next_alpha * pred + ets * next_sigma

             return prev_sample





      def __len__(       self						:				Union[str, Any]       ):
             return self.config.num_train_timesteps

