
"""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 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




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

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

											with CaptureStdout() as cs:
																		SCREAMING_SNAKE_CASE__		: str     =     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
											SCREAMING_SNAKE_CASE__		: List[str]     =     cs.out[:-1]

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""					).to(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     -1

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

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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

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

											with CaptureStdout() as cs:
																		SCREAMING_SNAKE_CASE__		: str     =     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
											SCREAMING_SNAKE_CASE__		: Dict     =     cs.out[:-1]

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.ones((1, 5)     , device=SCREAMING_SNAKE_CASE__					).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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
											SCREAMING_SNAKE_CASE__		: int     =     cs.out[:-1]  # Remove the final "\n"
											SCREAMING_SNAKE_CASE__		: str     =     tokenizer(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					)
											self.assertEqual(streamer_text_tokenized.input_ids.shape     , (1, 1)					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: int     =     ids_tensor((1, 5)     , vocab_size=model.config.vocab_size					).to(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     TextIteratorStreamer(SCREAMING_SNAKE_CASE__     , timeout=0.001					)
											SCREAMING_SNAKE_CASE__		: Any     =     {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
											SCREAMING_SNAKE_CASE__		: int     =     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__					):
																		SCREAMING_SNAKE_CASE__		: str     =     """"""
																		for new_text in streamer:
																									streamer_text += new_text






"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor



class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=18     , SCREAMING_SNAKE_CASE__=30     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , )      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     size if size is not None else {"""shortest_edge""": 20}
											SCREAMING_SNAKE_CASE__		: List[Any]     =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__		: Any     =     parent
											SCREAMING_SNAKE_CASE__		: List[str]     =     batch_size
											SCREAMING_SNAKE_CASE__		: Dict     =     num_channels
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_size
											SCREAMING_SNAKE_CASE__		: Dict     =     min_resolution
											SCREAMING_SNAKE_CASE__		: Dict     =     max_resolution
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     do_resize
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_center_crop
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     crop_size
											SCREAMING_SNAKE_CASE__		: List[str]     =     do_flip_channel_order



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}






@require_torch
@require_vision
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		str       =     MobileViTImageProcessor if is_vision_available() else None



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileViTImageProcessingTester(self					)



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_resize"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """size"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_flip_channel_order"""					)					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 20}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 18, """width""": 18}					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 42}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 84, """width""": 84}					)



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , Image.Image					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Dict     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__		: Tuple     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , numpify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , np.ndarray					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__		: Any     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , torchify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , torch.Tensor					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )




