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_video_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import VivitImageProcessor
class   _lowerCAmelCase		(     unittest.TestCase				):







							def __init__(				self     :    Optional[int]	,					__snake_case     :    Tuple	,					__snake_case     :    Any=7	,					__snake_case     :    List[str]=3	,					__snake_case     :    int=10	,					__snake_case     :    Tuple=18	,					__snake_case     :    List[Any]=30	,					__snake_case     :    Optional[int]=400	,					__snake_case     :    str=True	,					__snake_case     :    List[str]=None	,					__snake_case     :    Tuple=True	,					__snake_case     :    Union[str, Any]=[0.5, 0.5, 0.5]	,					__snake_case     :    Optional[int]=[0.5, 0.5, 0.5]	,					__snake_case     :    Optional[int]=None	,					):
										lowerCamelCase						:str						  =     size if size is not None else {'''shortest_edge''': 18}
										lowerCamelCase						:Tuple						  =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										lowerCamelCase						:int						  =     parent
										lowerCamelCase						:Union[str, Any]						  =     batch_size
										lowerCamelCase						:Union[str, Any]						  =     num_channels
										lowerCamelCase						:Optional[int]						  =     num_frames
										lowerCamelCase						:List[str]						  =     image_size
										lowerCamelCase						:Tuple						  =     min_resolution
										lowerCamelCase						:Optional[int]						  =     max_resolution
										lowerCamelCase						:Optional[Any]						  =     do_resize
										lowerCamelCase						:Optional[int]						  =     size
										lowerCamelCase						:Optional[Any]						  =     do_normalize
										lowerCamelCase						:str						  =     image_mean
										lowerCamelCase						:List[str]						  =     image_std
										lowerCamelCase						:Tuple						  =     crop_size







							def 				snake_case  (				self     :    List[str]		):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}


@require_torch
@require_vision
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     VivitImageProcessor if is_vision_available() else None







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Tuple						  =     VivitImageProcessingTester(self		)







							@property
							def 				snake_case  (				self     :    List[Any]		):
										return self.image_processor_tester.prepare_image_processor_dict()







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Optional[int]						  =     self.image_processing_class(**self.image_processor_dict		)
										self.assertTrue(hasattr(__snake_case	,					'''image_mean'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''image_std'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''do_normalize'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''do_resize'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''do_center_crop'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''size'''		)		)







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Optional[Any]						  =     self.image_processing_class.from_dict(self.image_processor_dict		)
										self.assertEqual(image_processor.size	,					{'''shortest_edge''': 18}		)
										self.assertEqual(image_processor.crop_size	,					{'''height''': 18, '''width''': 18}		)

										lowerCamelCase						:Optional[int]						  =     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 				snake_case  (				self     :    List[str]		):
										# Initialize image_processing
										lowerCamelCase						:str						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random PIL videos
										lowerCamelCase						:Tuple						  =     prepare_video_inputs(self.image_processor_tester	,					equal_resolution=__snake_case		)
										for video in video_inputs:
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertIsInstance(video[0]	,					Image.Image		)

										# Test not batched input
										lowerCamelCase						:List[str]						  =     image_processing(video_inputs[0]	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)

										# Test batched
										lowerCamelCase						:Tuple						  =     image_processing(__snake_case	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)







							def 				snake_case  (				self     :    Dict		):
										# Initialize image_processing
										lowerCamelCase						:List[Any]						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random numpy tensors
										lowerCamelCase						:str						  =     prepare_video_inputs(self.image_processor_tester	,					equal_resolution=__snake_case	,					numpify=__snake_case		)
										for video in video_inputs:
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertIsInstance(video[0]	,					np.ndarray		)

										# Test not batched input
										lowerCamelCase						:List[Any]						  =     image_processing(video_inputs[0]	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)

										# Test batched
										lowerCamelCase						:int						  =     image_processing(__snake_case	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)







							def 				snake_case  (				self     :    Tuple		):
										# Initialize image_processing
										lowerCamelCase						:Any						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										lowerCamelCase						:List[Any]						  =     prepare_video_inputs(self.image_processor_tester	,					equal_resolution=__snake_case	,					torchify=__snake_case		)
										for video in video_inputs:
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertIsInstance(video[0]	,					torch.Tensor		)

										# Test not batched input
										lowerCamelCase						:Dict						  =     image_processing(video_inputs[0]	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)

										# Test batched
										lowerCamelCase						:Tuple						  =     image_processing(__snake_case	,					return_tensors='''pt'''		).pixel_values
										self.assertEqual(
										    encoded_videos.shape	,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )	,					)




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

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# 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						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Union[str, Any]						  =     torch.ones((1, 5)	,					device=__snake_case		).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
													lowerCamelCase						:Dict						  =     TextStreamer(__snake_case	,					skip_special_tokens=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=1	,					do_sample=__snake_case	,					streamer=__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
										lowerCamelCase						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

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



