


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = True		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 32		,				_UpperCAmelCase = True		,				_UpperCAmelCase = 1 / 2_55		,				_UpperCAmelCase = True		,				_UpperCAmelCase = True		,				_UpperCAmelCase = [0.48_145_466, 0.4_578_275, 0.40_821_073]		,				_UpperCAmelCase = [0.26_862_954, 0.26_130_258, 0.27_577_711]		,				_UpperCAmelCase = True		,				_UpperCAmelCase=7		,				_UpperCAmelCase=30		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=3		,				):
       snake_case_	=				parent
       snake_case_	=				do_resize
       snake_case_	=				size if size is not None else {'''shortest_edge''': 2_88}
       snake_case_	=				size_divisor
       snake_case_	=				do_rescale
       snake_case_	=				rescale_factor
       snake_case_	=				do_normalize
       snake_case_	=				do_center_crop
       snake_case_	=				image_mean
       snake_case_	=				image_std
       snake_case_	=				do_pad
       snake_case_	=				batch_size
       snake_case_	=				num_channels
       snake_case_	=				min_resolution
       snake_case_	=				max_resolution



 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,
           "size_divisor": self.size_divisor,
       }





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=False ):
       if not batched:
             snake_case_	=				self.size['''shortest_edge''']
             snake_case_	=				image_inputs[0]
             if isinstance(_UpperCAmelCase		,				Image.Image ):
                   snake_case_	,   snake_case_	=				image.size
             else:
                   snake_case_	,   snake_case_	=				image.shape[1], image.shape[2]
             snake_case_	=				size / min(_UpperCAmelCase		,				_UpperCAmelCase )
             if h < w:
                   snake_case_	,   snake_case_	=				size, scale * w
             else:
                   snake_case_	,   snake_case_	=				scale * h, size

             snake_case_	=				int((13_33 / 8_00) * size )
             if max(_UpperCAmelCase		,				_UpperCAmelCase ) > max_size:
                   snake_case_	=				max_size / max(_UpperCAmelCase		,				_UpperCAmelCase )
                   snake_case_	=				newh * scale
                   snake_case_	=				neww * scale

             snake_case_	,   snake_case_	=				int(newh + 0.5 ), int(neww + 0.5 )
             snake_case_	,   snake_case_	=				(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

       else:
             snake_case_	=				[]
             for image in image_inputs:
                   snake_case_	,   snake_case_	=				self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
             snake_case_	=				max(_UpperCAmelCase		,				key=lambda _UpperCAmelCase : item[0] )[0]
             snake_case_	=				max(_UpperCAmelCase		,				key=lambda _UpperCAmelCase : item[1] )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  BridgeTowerImageProcessor if is_vision_available() else None



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BridgeTowerImageProcessingTester(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''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''size_divisor''' ) )



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       # Initialize image processor
       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

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		,				)



 def 						UpperCamelCase__					(     self ):
       # Initialize image processor
       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

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		,				)





 def 						UpperCamelCase__					(     self ):
       # Initialize image processor
       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

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )
       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_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)