





'''simple docstring'''


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           LayoutLMvaForQuestionAnswering,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaModel,
       )
       from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=99      ,  _lowerCAmelCase=36      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=6      ,  _lowerCAmelCase=6      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=1000      ,  )     ->				Any:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   text_seq_length
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_input_mask
     _lowerCAmelCase						      =   use_token_type_ids
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   type_vocab_size
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   coordinate_size
     _lowerCAmelCase						      =   shape_size
     _lowerCAmelCase						      =   num_labels
     _lowerCAmelCase						      =   num_choices
     _lowerCAmelCase						      =   scope
     _lowerCAmelCase						      =   range_bbox

     # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
     _lowerCAmelCase						      =   text_seq_length
     _lowerCAmelCase						      =   (image_size // patch_size) ** 2 + 1
     _lowerCAmelCase						      =   self.text_seq_length + self.image_seq_length




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.text_seq_length]      ,  self.vocab_size		)

     _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.text_seq_length, 4]      ,  self.range_bbox		)
     # Ensure that bbox is legal
     for i in range(bbox.shape[0]		):
         for j in range(bbox.shape[1]		):
             if bbox[i, j, 3] < bbox[i, j, 1]:
                 _lowerCAmelCase						      =   bbox[i, j, 3]
                 _lowerCAmelCase						      =   bbox[i, j, 1]
                 _lowerCAmelCase						      =   t
             if bbox[i, j, 2] < bbox[i, j, 0]:
                 _lowerCAmelCase						      =   bbox[i, j, 2]
                 _lowerCAmelCase						      =   bbox[i, j, 0]
                 _lowerCAmelCase						      =   t

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase						      =   None
     if self.use_input_mask:
         _lowerCAmelCase						      =   random_attention_mask([self.batch_size, self.text_seq_length]		)

     _lowerCAmelCase						      =   None
     if self.use_token_type_ids:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.text_seq_length]      ,  self.type_vocab_size		)

     _lowerCAmelCase						      =   None
     _lowerCAmelCase						      =   None
     if self.use_labels:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size]      ,  self.type_sequence_label_size		)
         _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.text_seq_length]      ,  self.num_labels		)

     _lowerCAmelCase						      =   LayoutLMvaConfig(
         vocab_size=self.vocab_size      ,  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      ,  max_position_embeddings=self.max_position_embeddings      ,  type_vocab_size=self.type_vocab_size      ,  initializer_range=self.initializer_range      ,  coordinate_size=self.coordinate_size      ,  shape_size=self.shape_size      ,  input_size=self.image_size      ,  patch_size=self.patch_size      ,  )

     return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   LayoutLMvaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     # text + image
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase		)

     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)

     # text only
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.last_hidden_state.shape      ,  (self.batch_size, self.text_seq_length, self.hidden_size)		)

     # image only
     _lowerCAmelCase						      =   model(pixel_values=_lowerCAmelCase		)
     self.parent.assertEqual(
         result.last_hidden_state.shape      ,  (self.batch_size, self.image_seq_length, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   LayoutLMvaForSequenceClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   LayoutLMvaForTokenClassification(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.text_seq_length, self.num_labels)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   LayoutLMvaForQuestionAnswering(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  bbox=_lowerCAmelCase      ,  pixel_values=_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  start_positions=_lowerCAmelCase      ,  end_positions=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.start_logits.shape      ,  (self.batch_size, self.seq_length)		)
     self.parent.assertEqual(result.end_logits.shape      ,  (self.batch_size, self.seq_length)		)





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {
         "input_ids": input_ids,
         "bbox": bbox,
         "pixel_values": pixel_values,
         "token_type_ids": token_type_ids,
         "attention_mask": input_mask,
     }
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[int]								=       False
 __lowerCamelCase    :				Tuple								=       False
 __lowerCamelCase    :				Dict								=       False

 __lowerCamelCase    :				Tuple								=       (
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				Union[str, Any]								=       (
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				int:
     # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
     # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
     # the sequence dimension of the text embedding only.
     # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
     return True




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   LayoutLMvaModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				Dict:
     _lowerCAmelCase						      =   copy.deepcopy(_lowerCAmelCase		)
     if model_class in get_values(_lowerCAmelCase		):
         _lowerCAmelCase						      =   {
             k: v.unsqueeze(1		).expand(-1      ,  self.model_tester.num_choices      ,  -1		).contiguous()
             if isinstance(_lowerCAmelCase      ,  torch.Tensor		) and v.ndim > 1
             else v
             for k, v in inputs_dict.items()
         }
     if return_labels:
         if model_class in get_values(_lowerCAmelCase		):
             _lowerCAmelCase						      =   torch.ones(self.model_tester.batch_size      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
         elif model_class in get_values(_lowerCAmelCase		):
             _lowerCAmelCase						      =   torch.zeros(
                 self.model_tester.batch_size      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
             _lowerCAmelCase						      =   torch.zeros(
                 self.model_tester.batch_size      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
         elif model_class in [
             *get_values(_lowerCAmelCase		),
         ]:
             _lowerCAmelCase						      =   torch.zeros(
                 self.model_tester.batch_size      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
         elif model_class in [
             *get_values(_lowerCAmelCase		),
         ]:
             _lowerCAmelCase						      =   torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,  dtype=torch.long      ,  device=_lowerCAmelCase      ,  )

     return inputs_dict




 def 				_snake_case (						self		)     ->				List[Any]:
     self.config_tester.run_common_tests()




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCAmelCase						      =   type
         self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase		)





 @slow
 def 				_snake_case (						self		)     ->				List[str]:
     for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   LayoutLMvaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 						__a():



    '''simple docstring'''





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





@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 				_snake_case (						self		)     ->				List[str]:
     return LayoutLMvaImageProcessor(apply_ocr=_lowerCAmelCase		) if is_vision_available() else None





 @slow
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.default_image_processor
     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		).pixel_values.to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor([[1, 2]]		)
     _lowerCAmelCase						      =   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

     # forward pass
     _lowerCAmelCase						      =   model(
         input_ids=input_ids.to(_lowerCAmelCase		)      ,  bbox=bbox.to(_lowerCAmelCase		)      ,  pixel_values=pixel_values.to(_lowerCAmelCase		)      ,  )

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 199, 768)		)
     self.assertEqual(outputs.last_hidden_state.shape      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor(
         [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(_lowerCAmelCase		)

     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)









'''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		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						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
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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


