



import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ImageGPTImageProcessor



class 		_lowercase		(       unittest.TestCase	):

			def __init__(      self			:       str							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       Any=7							,   __lowerCAmelCase			:       Optional[int]=3							,   __lowerCAmelCase			:       List[Any]=18							,   __lowerCAmelCase			:       str=30							,   __lowerCAmelCase			:       Dict=400							,   __lowerCAmelCase			:       Optional[int]=True							,   __lowerCAmelCase			:       Optional[Any]=None							,   __lowerCAmelCase			:       Any=True							,   )       ->				int:


				"""simple docstring"""






				a						=		size if size is not None else {"height": 18, "width": 18}
				a						=		parent
				a						=		batch_size
				a						=		num_channels
				a						=		image_size
				a						=		min_resolution
				a						=		max_resolution
				a						=		do_resize
				a						=		size
				a						=		do_normalize




			def 		A							(      self			:       Any							)       ->				int:


				"""simple docstring"""






				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
				            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
				        ]							),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}



@require_torch
@require_vision
class 		_lowercase		(       _snake_case,   unittest.TestCase	):
			_UpperCAmelCase     	=			ImageGPTImageProcessor if is_vision_available() else None

			def 		A							(      self			:       Tuple							)       ->				List[str]:


				"""simple docstring"""






				a						=		ImageGPTImageProcessingTester(self							)

			@property
			def 		A							(      self			:       Tuple							)       ->				Any:


				"""simple docstring"""






				return self.image_processor_tester.prepare_image_processor_dict()

			def 		A							(      self			:       Optional[Any]							)       ->				str:


				"""simple docstring"""






				a						=		self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(snake_case_							,   "clusters"							)							)
				self.assertTrue(hasattr(snake_case_							,   "do_resize"							)							)
				self.assertTrue(hasattr(snake_case_							,   "size"							)							)
				self.assertTrue(hasattr(snake_case_							,   "do_normalize"							)							)

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


				"""simple docstring"""






				a						=		self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size							,   {"height": 18, "width": 18}							)

				a						=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							)
				self.assertEqual(image_processor.size							,   {"height": 42, "width": 42}							)

			def 		A							(      self			:       Any							)       ->				Dict:


				"""simple docstring"""






				a						=		self.image_processing_class(**self.image_processor_dict							)
				a						=		json.loads(image_processor.to_json_string()							)
				for key, value in self.image_processor_dict.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(snake_case_							,   obj[key]							)							)
					else:
						self.assertEqual(obj[key]							,   snake_case_							)

			def 		A							(      self			:       Tuple							)       ->				Optional[Any]:


				"""simple docstring"""






				a						=		self.image_processing_class(**self.image_processor_dict							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					a						=		os.path.join(snake_case_							,   "image_processor.json"							)
					image_processor_first.to_json_file(snake_case_							)
					a						=		self.image_processing_class.from_json_file(snake_case_							).to_dict()

				a						=		image_processor_first.to_dict()
				for key, value in image_processor_first.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(snake_case_							,   image_processor_second[key]							)							)
					else:
						self.assertEqual(image_processor_first[key]							,   snake_case_							)

			def 		A							(      self			:       int							)       ->				Optional[Any]:


				"""simple docstring"""






				a						=		self.image_processing_class(**self.image_processor_dict							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					image_processor_first.save_pretrained(snake_case_							)
					a						=		self.image_processing_class.from_pretrained(snake_case_							).to_dict()

				a						=		image_processor_first.to_dict()
				for key, value in image_processor_first.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(snake_case_							,   image_processor_second[key]							)							)
					else:
						self.assertEqual(image_processor_first[key]							,   snake_case_							)




			@unittest.skip("ImageGPT requires clusters at initialization"							)
			def 		A							(      self			:       Tuple							)       ->				Dict:


				"""simple docstring"""






				pass


def 	UpperCAmelCase__      (      ):






	'''simple docstring'''

	a						=		load_dataset("hf-internal-testing/fixtures_image_utils" ,					split="test"					)

	a						=		Image.open(dataset[4]["file"]					)
	a						=		Image.open(dataset[5]["file"]					)

	a						=		[imagea, imagea]

	return images




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

			@slow
			def 		A							(      self			:       str							)       ->				Tuple:


				"""simple docstring"""






				a						=		ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"							)

				a						=		prepare_images()

				# test non-batched
				a						=		image_processing(images[0]							,   return_tensors="pt"							)

				self.assertIsInstance(encoding.input_ids							,   torch.LongTensor							)
				self.assertEqual(encoding.input_ids.shape							,   (1, 1024)							)

				a						=		[306, 191, 191]
				self.assertEqual(encoding.input_ids[0, :3].tolist()							,   snake_case_							)

				# test batched
				a						=		image_processing(snake_case_							,   return_tensors="pt"							)

				self.assertIsInstance(encoding.input_ids							,   torch.LongTensor							)
				self.assertEqual(encoding.input_ids.shape							,   (2, 1024)							)

				a						=		[303, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()							,   snake_case_							)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

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


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

