



import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf



@require_vision
@require_torchvision
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  tempfile.mkdtemp()
         lowercase__     =  SamImageProcessor()
         lowercase__     =  SamProcessor(__a)
         processor.save_pretrained(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				**lowerCAmelCase  :			List[str])	->				int:




         """simple docstring"""

         return AutoProcessor.from_pretrained(self.tmpdirname			,				**__a).image_processor


   def 			UpperCAmelCase		(						self  :			Dict)	->				Any:




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			List[str])	->				str:




         """simple docstring"""

         lowercase__     =  [np.random.randint(2_55			,				size=(3, 30, 4_00)			,				dtype=np.uinta)]

         lowercase__     =  [Image.fromarray(np.moveaxis(__a			,				0			,				-1)) for x in image_inputs]

         return image_inputs


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Tuple:




         """simple docstring"""

         lowercase__     =  SamProcessor(image_processor=self.get_image_processor())
         processor.save_pretrained(self.tmpdirname)

         lowercase__     =  self.get_image_processor(do_normalize=__a			,				padding_value=1.0)

         lowercase__     =  SamProcessor.from_pretrained(self.tmpdirname			,				do_normalize=__a			,				padding_value=1.0)

         self.assertEqual(processor.image_processor.to_json_string()			,				image_processor_add_kwargs.to_json_string())
         self.assertIsInstance(processor.image_processor			,				__a)


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)

         lowercase__     =  self.prepare_image_inputs()

         lowercase__     =  image_processor(__a			,				return_tensors='np')
         lowercase__     =  processor(images=__a			,				return_tensors='np')

         input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes')  # pop original_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()			,				input_processor[key].sum()			,				delta=1E-2)



   @require_torch
   def 			UpperCAmelCase		(						self  :			Tuple)	->				str:




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)
         lowercase__     =  [torch.ones((1, 3, 5, 5))]

         lowercase__     =  [[17_64, 26_46]]

         lowercase__     =  [[6_83, 10_24]]
         lowercase__     =  processor.post_process_masks(__a			,				__a			,				__a)
         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         lowercase__     =  processor.post_process_masks(
             __a			,				torch.tensor(__a)			,				torch.tensor(__a))
         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         # should also work with np
         lowercase__     =  [np.ones((1, 3, 5, 5))]
         lowercase__     =  processor.post_process_masks(__a			,				np.array(__a)			,				np.array(__a))

         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         lowercase__     =  [[1, 0], [0, 1]]
         with self.assertRaises(__a):
               lowercase__     =  processor.post_process_masks(__a			,				np.array(__a)			,				np.array(__a))



@require_vision
@require_tf
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  tempfile.mkdtemp()
         lowercase__     =  SamImageProcessor()
         lowercase__     =  SamProcessor(__a)
         processor.save_pretrained(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				**lowerCAmelCase  :			str)	->				List[Any]:




         """simple docstring"""

         return AutoProcessor.from_pretrained(self.tmpdirname			,				**__a).image_processor


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




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  [np.random.randint(2_55			,				size=(3, 30, 4_00)			,				dtype=np.uinta)]

         lowercase__     =  [Image.fromarray(np.moveaxis(__a			,				0			,				-1)) for x in image_inputs]

         return image_inputs


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         lowercase__     =  SamProcessor(image_processor=self.get_image_processor())
         processor.save_pretrained(self.tmpdirname)

         lowercase__     =  self.get_image_processor(do_normalize=__a			,				padding_value=1.0)

         lowercase__     =  SamProcessor.from_pretrained(self.tmpdirname			,				do_normalize=__a			,				padding_value=1.0)

         self.assertEqual(processor.image_processor.to_json_string()			,				image_processor_add_kwargs.to_json_string())
         self.assertIsInstance(processor.image_processor			,				__a)


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




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)

         lowercase__     =  self.prepare_image_inputs()

         lowercase__     =  image_processor(__a			,				return_tensors='np')
         lowercase__     =  processor(images=__a			,				return_tensors='np')

         input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes')  # pop reshaped_input_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()			,				input_processor[key].sum()			,				delta=1E-2)



   @require_tf
   def 			UpperCAmelCase		(						self  :			Any)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)
         lowercase__     =  [tf.ones((1, 3, 5, 5))]

         lowercase__     =  [[17_64, 26_46]]

         lowercase__     =  [[6_83, 10_24]]
         lowercase__     =  processor.post_process_masks(__a			,				__a			,				__a			,				return_tensors='tf')
         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         lowercase__     =  processor.post_process_masks(
             __a			,				tf.convert_to_tensor(__a)			,				tf.convert_to_tensor(__a)			,				return_tensors='tf'			,				)
         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         # should also work with np
         lowercase__     =  [np.ones((1, 3, 5, 5))]
         lowercase__     =  processor.post_process_masks(
             __a			,				np.array(__a)			,				np.array(__a)			,				return_tensors='tf')

         self.assertEqual(masks[0].shape			,				(1, 3, 17_64, 26_46))

         lowercase__     =  [[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError):
               lowercase__     =  processor.post_process_masks(
                   __a			,				np.array(__a)			,				np.array(__a)			,				return_tensors='tf')



@require_vision
@require_torchvision
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Any)	->				str:




         """simple docstring"""

         lowercase__     =  tempfile.mkdtemp()
         lowercase__     =  SamImageProcessor()
         lowercase__     =  SamProcessor(__a)
         processor.save_pretrained(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			Dict			,				**lowerCAmelCase  :			Optional[Any])	->				List[str]:




         """simple docstring"""

         return AutoProcessor.from_pretrained(self.tmpdirname			,				**__a).image_processor


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  [np.random.randint(2_55			,				size=(3, 30, 4_00)			,				dtype=np.uinta)]

         lowercase__     =  [Image.fromarray(np.moveaxis(__a			,				0			,				-1)) for x in image_inputs]

         return image_inputs


   @is_pt_tf_cross_test
   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)
         lowercase__     =  np.random.randint(0			,				2			,				size=(1, 3, 5, 5)).astype(np.floataa)
         lowercase__     =  [tf.convert_to_tensor(__a)]
         lowercase__     =  [torch.tensor(__a)]

         lowercase__     =  [[17_64, 26_46]]

         lowercase__     =  [[6_83, 10_24]]
         lowercase__     =  processor.post_process_masks(
             __a			,				__a			,				__a			,				return_tensors='tf')
         lowercase__     =  processor.post_process_masks(
             __a			,				__a			,				__a			,				return_tensors='pt')

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))



   @is_pt_tf_cross_test
   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.get_image_processor()

         lowercase__     =  SamProcessor(image_processor=__a)

         lowercase__     =  self.prepare_image_inputs()

         lowercase__     =  image_processor(__a			,				return_tensors='pt')["pixel_values"].numpy()
         lowercase__     =  processor(images=__a			,				return_tensors='pt')["pixel_values"].numpy()

         lowercase__     =  image_processor(__a			,				return_tensors='tf')["pixel_values"].numpy()
         lowercase__     =  processor(images=__a			,				return_tensors='tf')["pixel_values"].numpy()

         self.assertTrue(np.allclose(__a			,				__a))
         self.assertTrue(np.allclose(__a			,				__a))
         self.assertTrue(np.allclose(__a			,				__a))











from typing import TYPE_CHECKING

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


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
