
import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=[30, 30]	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=10	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=8	,      SCREAMING_SNAKE_CASE_=10	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    patch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    num_labels
       UpperCamelCase__				=    scope
       UpperCamelCase__				=    n_targets
       UpperCamelCase__				=    num_detection_tokens
       # we set the expected sequence length (which is used in several tests)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
       UpperCamelCase__				=    (image_size[1] // patch_size) * (image_size[0] // patch_size)
       UpperCamelCase__				=    num_patches + 1 + self.num_detection_tokens







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

       UpperCamelCase__				=    None
       if self.use_labels:
            # labels is a list of Dict (each Dict being the labels for a given example in the batch)
            UpperCamelCase__				=    []
            for i in range(self.batch_size						):
                 UpperCamelCase__				=    {}
                 UpperCamelCase__				=    torch.randint(
                     high=self.num_labels	,      size=(self.n_targets,)	,      device=SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    torch.rand(self.n_targets	,      4	,      device=SCREAMING_SNAKE_CASE_						)
                 labels.append(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.get_config()

       return config, pixel_values, labels







  def 	UpperCAmelCase_ (self						):
       return YolosConfig(
           image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      is_decoder=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      num_detection_tokens=self.num_detection_tokens	,      num_labels=self.num_labels	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    YolosModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(
           result.last_hidden_state.shape	,      (self.batch_size, self.expected_seq_len, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    YolosForObjectDetection(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

       UpperCamelCase__				=    model(pixel_values=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)

       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
       self.parent.assertEqual(result.pred_boxes.shape	,      (self.batch_size, self.num_detection_tokens, 4)						)

       UpperCamelCase__				=    model(pixel_values=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)

       self.parent.assertEqual(result.loss.shape	,      ()						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
       self.parent.assertEqual(result.pred_boxes.shape	,      (self.batch_size, self.num_detection_tokens, 4)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    config_and_inputs
       UpperCamelCase__				=    {"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__         =      (
      {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
  )

  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False						):
       UpperCamelCase__				=    super()._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      return_labels=SCREAMING_SNAKE_CASE_						)

       if return_labels:
            if model_class.__name__ == "YolosForObjectDetection":
                 UpperCamelCase__				=    []
                 for i in range(self.model_tester.batch_size						):
                      UpperCamelCase__				=    {}
                      UpperCamelCase__				=    torch.ones(
                          size=(self.model_tester.n_targets,)	,      device=SCREAMING_SNAKE_CASE_	,      dtype=torch.long						)
                      UpperCamelCase__				=    torch.ones(
                          self.model_tester.n_targets	,      4	,      device=SCREAMING_SNAKE_CASE_	,      dtype=torch.float						)
                      labels.append(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    labels

       return inputs_dict







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    YolosModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      has_text_modality=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  def 	UpperCAmelCase_ (self						):
       # YOLOS does not use inputs_embeds
       pass







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            self.assertIsInstance(model.get_input_embeddings()	,      (nn.Module)						)
            UpperCamelCase__				=    model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_	,      nn.Linear						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    inspect.signature(model.forward						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__				=    [*signature.parameters.keys()]

            UpperCamelCase__				=    ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__				=    True

       # in YOLOS, the seq_len is different
       UpperCamelCase__				=    self.model_tester.expected_seq_len
       for model_class in self.all_model_classes:
            UpperCamelCase__				=    True
            UpperCamelCase__				=    False
            UpperCamelCase__				=    True
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.to(SCREAMING_SNAKE_CASE_						)
            model.eval()
            with torch.no_grad():
                 UpperCamelCase__				=    model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)
            UpperCamelCase__				=    outputs.attentions
            self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      self.model_tester.num_hidden_layers						)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            UpperCamelCase__				=    True
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.to(SCREAMING_SNAKE_CASE_						)
            model.eval()
            with torch.no_grad():
                 UpperCamelCase__				=    model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)
            UpperCamelCase__				=    outputs.attentions
            self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      self.model_tester.num_hidden_layers						)

            self.assertListEqual(
                list(attentions[0].shape[-3:]						)	,      [self.model_tester.num_attention_heads, seq_len, seq_len]	,      )
            UpperCamelCase__				=    len(SCREAMING_SNAKE_CASE_						)

            # Check attention is always last and order is fine
            UpperCamelCase__				=    True
            UpperCamelCase__				=    True
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.to(SCREAMING_SNAKE_CASE_						)
            model.eval()
            with torch.no_grad():
                 UpperCamelCase__				=    model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)

            UpperCamelCase__				=    1
            self.assertEqual(out_len + added_hidden_states	,      len(SCREAMING_SNAKE_CASE_						)						)

            UpperCamelCase__				=    outputs.attentions

            self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      self.model_tester.num_hidden_layers						)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]						)	,      [self.model_tester.num_attention_heads, seq_len, seq_len]	,      )







  def 	UpperCAmelCase_ (self						):
       def check_hidden_states_output(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.to(SCREAMING_SNAKE_CASE_						)
            model.eval()

            with torch.no_grad():
                 UpperCamelCase__				=    model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)

            UpperCamelCase__				=    outputs.hidden_states

            UpperCamelCase__				=    getattr(
                self.model_tester	,      """expected_num_hidden_layers"""	,      self.model_tester.num_hidden_layers + 1						)
            self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      SCREAMING_SNAKE_CASE_						)

            # YOLOS has a different seq_length
            UpperCamelCase__				=    self.model_tester.expected_seq_len

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]						)	,      [seq_length, self.model_tester.hidden_size]	,      )

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    True
            check_hidden_states_output(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCamelCase__				=    True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=    YolosModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     return image




@require_torch
@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @cached_property
  def 	UpperCAmelCase_ (self						):
       return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""						) if is_vision_available() else None







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""						).to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.default_image_processor
       UpperCamelCase__				=    prepare_img()
       UpperCamelCase__				=    image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						).to(SCREAMING_SNAKE_CASE_						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__				=    model(inputs.pixel_values						)

       # verify outputs
       UpperCamelCase__				=    torch.Size((1, 1_00, 92)						)
       self.assertEqual(outputs.logits.shape	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    torch.tensor(
           [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]	,      device=SCREAMING_SNAKE_CASE_	,      )
       UpperCamelCase__				=    torch.tensor(
           [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]	,      device=SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)
       self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)

       # verify postprocessing
       UpperCamelCase__				=    image_processor.post_process_object_detection(
           SCREAMING_SNAKE_CASE_	,      threshold=0.3	,      target_sizes=[image.size[::-1]]						)[0]
       UpperCamelCase__				=    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    [75, 75, 17, 63, 17]
       UpperCamelCase__				=    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]						).to(SCREAMING_SNAKE_CASE_						)

       self.assertEqual(len(results["""scores"""]						)	,      5						)
       self.assertTrue(torch.allclose(results["""scores"""]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)
       self.assertSequenceEqual(results["""labels"""].tolist()	,      SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(results["""boxes"""][0, :]	,      SCREAMING_SNAKE_CASE_						)						)







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





