
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (IPNDMScheduler,)
  SCREAMING_SNAKE_CASE__         =      (("""num_inference_steps""", 50),)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {"""num_train_timesteps""": 10_00}
       config.update(**SCREAMING_SNAKE_CASE_						)
       return config







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    dict(self.forward_default_kwargs						)
       UpperCamelCase__				=    kwargs.pop("""num_inference_steps"""	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.dummy_sample
       UpperCamelCase__				=    0.1 * sample
       UpperCamelCase__				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__				=    self.get_scheduler_config(**SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    scheduler_class(**SCREAMING_SNAKE_CASE_						)
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)
            # copy over dummy past residuals
            UpperCamelCase__				=    dummy_past_residuals[:]

            if time_step is None:
                 UpperCamelCase__				=    scheduler.timesteps[len(scheduler.timesteps						) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_						)
                 new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)
                 # copy over dummy past residuals
                 UpperCamelCase__				=    dummy_past_residuals[:]

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    new_scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    new_scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    dict(self.forward_default_kwargs						)
       UpperCamelCase__				=    kwargs.pop("""num_inference_steps"""	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.dummy_sample
       UpperCamelCase__				=    0.1 * sample
       UpperCamelCase__				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__				=    self.get_scheduler_config()
            UpperCamelCase__				=    scheduler_class(**SCREAMING_SNAKE_CASE_						)
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)

            # copy over dummy past residuals (must be after setting timesteps)
            UpperCamelCase__				=    dummy_past_residuals[:]

            if time_step is None:
                 UpperCamelCase__				=    scheduler.timesteps[len(scheduler.timesteps						) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_						)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)

                 # copy over dummy past residual (must be after setting timesteps)
                 UpperCamelCase__				=    dummy_past_residuals[:]

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    new_scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    new_scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config(**SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    scheduler_class(**SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    10
       UpperCamelCase__				=    self.dummy_model()
       UpperCamelCase__				=    self.dummy_sample_deter
       scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)

       for i, t in enumerate(scheduler.timesteps						):
            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						).prev_sample

       for i, t in enumerate(scheduler.timesteps						):
            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						).prev_sample

       return sample







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    dict(self.forward_default_kwargs						)

       UpperCamelCase__				=    kwargs.pop("""num_inference_steps"""	,      SCREAMING_SNAKE_CASE_						)

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__				=    self.get_scheduler_config()
            UpperCamelCase__				=    scheduler_class(**SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    self.dummy_sample
            UpperCamelCase__				=    0.1 * sample

            if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_	,      """set_timesteps"""						):
                 scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)
            elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_	,      """set_timesteps"""						):
                 UpperCamelCase__				=    num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            UpperCamelCase__				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            UpperCamelCase__				=    dummy_past_residuals[:]

            UpperCamelCase__				=    scheduler.timesteps[5]
            UpperCamelCase__				=    scheduler.timesteps[6]

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            self.assertEqual(output_a.shape	,      sample.shape						)
            self.assertEqual(output_a.shape	,      output_a.shape						)

            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample
            UpperCamelCase__				=    scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						).prev_sample

            self.assertEqual(output_a.shape	,      sample.shape						)
            self.assertEqual(output_a.shape	,      output_a.shape						)







  def 	UpperCAmelCase_ (self						):
       for timesteps in [1_00, 10_00]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	,      time_step=SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       for t, num_inference_steps in zip([1, 5, 10]	,      [10, 50, 1_00]						):
            self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_	,      time_step=SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.full_loop()
       UpperCamelCase__				=    torch.mean(torch.abs(SCREAMING_SNAKE_CASE_						)						)

       assert abs(result_mean.item() - 2_54_05_29						) < 10







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      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

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

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

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

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # 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__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_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
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

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





