
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 DeiTImageProcessor

class        UpperCamelCase__(    unittest.TestCase	):

	"""simple docstring"""

	def __init__(      self				:			int							,			snake_case__				:			Tuple							,			snake_case__				:			Any=7							,			snake_case__				:			Union[str, Any]=3							,			snake_case__				:			Optional[Any]=18							,			snake_case__				:			Optional[int]=30							,			snake_case__				:			Any=4_00							,			snake_case__				:			Optional[Any]=True							,			snake_case__				:			Any=None							,			snake_case__				:			List[str]=True							,			snake_case__				:			Dict=None							,			snake_case__				:			List[Any]=True							,			snake_case__				:			List[str]=[0.5, 0.5, 0.5]							,			snake_case__				:			Union[str, Any]=[0.5, 0.5, 0.5]							,			):
			"""simple docstring"""





			A				=size if size is not None else {"height": 20, "width": 20}
			A				=crop_size if crop_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_center_crop
			A				=crop_size
			A				=do_normalize
			A				=image_mean
			A				=image_std







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}


@require_torch
@require_vision
class        UpperCamelCase__(    snake_case__						,						unittest.TestCase	):

	"""simple docstring"""

	_A  =		DeiTImageProcessor if is_vision_available() else None
	_A  =		True







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=DeiTImageProcessingTester(self )







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return self.image_processor_tester.prepare_image_processor_dict()







	def        _a					(      self				:			int ):
			"""simple docstring"""





			A				=self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"do_resize" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"size" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"do_center_crop" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"center_crop" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"do_normalize" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"image_mean" ) )
			self.assertTrue(hasattr(UpperCAmelCase_							,			"image_std" ) )







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





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

			A				=self.image_processing_class.from_dict(self.image_processor_dict							,			size=42							,			crop_size=84 )
			self.assertEqual(image_processor.size							,			{"height": 42, "width": 42} )
			self.assertEqual(image_processor.crop_size							,			{"height": 84, "width": 84} )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			pass







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			A				=prepare_image_inputs(self.image_processor_tester							,			equal_resolution=UpperCAmelCase_ )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_							,			Image.Image )

			# Test not batched input
			A				=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
			A				=image_processing(UpperCAmelCase_							,			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        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			A				=prepare_image_inputs(self.image_processor_tester							,			equal_resolution=UpperCAmelCase_							,			numpify=UpperCAmelCase_ )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_							,			np.ndarray )

			# Test not batched input
			A				=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
			A				=image_processing(UpperCAmelCase_							,			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        _a					(      self				:			Any ):
			"""simple docstring"""





			A				=self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			A				=prepare_image_inputs(self.image_processor_tester							,			equal_resolution=UpperCAmelCase_							,			torchify=UpperCAmelCase_ )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_							,			torch.Tensor )

			# Test not batched input
			A				=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
			A				=image_processing(UpperCAmelCase_							,			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"],
			    )							,			)











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




