






'''simple docstring'''




import unittest

import numpy as np

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

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 DPTImageProcessor

class    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   def __init__(		self :	str      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	List[str]=7      ,      __UpperCAmelCase :	Any=3      ,      __UpperCAmelCase :	Tuple=18      ,      __UpperCAmelCase :	Any=30      ,      __UpperCAmelCase :	List[Any]=400      ,      __UpperCAmelCase :	List[Any]=True      ,      __UpperCAmelCase :	Optional[Any]=None      ,      __UpperCAmelCase :	str=True      ,      __UpperCAmelCase :	Optional[Any]=[0.5, 0.5, 0.5]      ,      __UpperCAmelCase :	List[Any]=[0.5, 0.5, 0.5]      ,      ):





         '''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
         _A			     =	image_mean
         _A			     =	image_std

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





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





@require_torch
@require_vision
class    _UpperCAmelCase			(     snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						DPTImageProcessor if is_vision_available() else None

   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





         _A			     =	DPTImageProcessingTester(self		)

   @property
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         _A			     =	self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "image_mean"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "image_std"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "do_normalize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "do_resize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "size"		)		)

   def 						lowerCAmelCase (		self :	Optional[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 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''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.size["height"],
                 self.image_processor_tester.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.size["height"],
                 self.image_processor_tester.size["width"],
             )      ,      )

   def 						lowerCAmelCase (		self :	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.size["height"],
                 self.image_processor_tester.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.size["height"],
                 self.image_processor_tester.size["width"],
             )      ,      )

   def 						lowerCAmelCase (		self :	Optional[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.size["height"],
                 self.image_processor_tester.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.size["height"],
                 self.image_processor_tester.size["width"],
             )      ,      )









"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






