


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 lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=3		,				_UpperCAmelCase=18		,				_UpperCAmelCase=30		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=True		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase=[0.5, 0.5, 0.5]		,				_UpperCAmelCase=[0.5, 0.5, 0.5]		,				):
      snake_case_	=				size if size is not None else {'height': 18, 'width': 18}
      snake_case_	=				parent
      snake_case_	=				batch_size
      snake_case_	=				num_channels
      snake_case_	=				image_size
      snake_case_	=				min_resolution
      snake_case_	=				max_resolution
      snake_case_	=				do_resize
      snake_case_	=				size
      snake_case_	=				do_normalize
      snake_case_	=				image_mean
      snake_case_	=				image_std





 def 						UpperCamelCase__					(     self ):
      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 lowerCAmelCase_						(			__UpperCamelCase ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  DPTImageProcessor if is_vision_available() else None



 def 						UpperCamelCase__					(     self ):
      snake_case_	=				DPTImageProcessingTester(self )



 @property
 def 						UpperCamelCase__					(     self ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 						UpperCamelCase__					(     self ):
      snake_case_	=				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 						UpperCamelCase__					(     self ):
      snake_case_	=				self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size		,				{'''height''': 18, '''width''': 18} )

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



 def 						UpperCamelCase__					(     self ):
      # Initialize image_processing
      snake_case_	=				self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      snake_case_	=				prepare_image_inputs(self.image_processor_tester		,				equal_resolution=_UpperCAmelCase )
      for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase		,				Image.Image )

      # Test not batched input
      snake_case_	=				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
      snake_case_	=				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 						UpperCamelCase__					(     self ):
      # Initialize image_processing
      snake_case_	=				self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      snake_case_	=				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
      snake_case_	=				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
      snake_case_	=				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 						UpperCamelCase__					(     self ):
      # Initialize image_processing
      snake_case_	=				self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      snake_case_	=				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
      snake_case_	=				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
      snake_case_	=				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'''],
          )		,				)



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,
)


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

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

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

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


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


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """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

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