'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCAmelCase    :     Union[str, Any]         =       logging.get_logger(__name__)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict				):



     if isinstance(lowerCamelCase_   ,		np.ndarray				):
          return list(tensor.shape				)

     _A    		=  tf.shape(lowerCamelCase_				)

     if tensor.shape == tf.TensorShape(lowerCamelCase_				):
          return dynamic

     _A    		=  tensor.shape.as_list()

     return [dynamic[i] if s is None else s for i, s in enumerate(lowerCamelCase_				)]





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]   ,		__snake_case							:			Tuple = None   ,		__snake_case							:			Optional[int] = None				):
     return tf.nn.softmax(logits=logits + 1e-9   ,		axis=lowerCamelCase_   ,		name=lowerCamelCase_				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			str   ,		__snake_case							:			Dict   ,		__snake_case							:			Optional[int]=1e-5   ,		__snake_case							:			int=-1				):



     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase_   ,		lowerCamelCase_				):
          raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'				)

     # Get mean and variance on the axis to be normalized
     _A    		=  tf.nn.moments(lowerCamelCase_   ,		axes=[axis]   ,		keepdims=lowerCamelCase_				)

     if axis != -1:
          # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
          # on every dimension except axis
          _A    		=  [1] * inputs.shape.rank
          _A    		=  shape_list(lowerCamelCase_				)[axis]
          _A    		=  tf.reshape(lowerCamelCase_   ,		lowerCamelCase_				)
          _A    		=  tf.reshape(lowerCamelCase_   ,		lowerCamelCase_				)

     # Compute layer normalization using the batch_normalization
     # function.
     _A    		=  tf.nn.batch_normalization(
         lowerCamelCase_   ,		lowerCamelCase_   ,		lowerCamelCase_   ,		offset=lowerCamelCase_   ,		scale=lowerCamelCase_   ,		variance_epsilon=lowerCamelCase_   ,		)
     return outputs





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Optional[int]=0   ,		__snake_case							:			Tuple=-1				):



     if end_dim < 0:
          end_dim += input.shape.rank
     if start_dim < 0:
          start_dim += input.shape.rank

     if start_dim == end_dim:
          return input

     _A    		=  tf.shape(lowerCamelCase_				)
     _A    		=  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
     _A    		=  tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]   ,		axis=0				)
     return tf.reshape(lowerCamelCase_   ,		lowerCamelCase_				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict				):



     if not isinstance(lowerCamelCase_   ,		tf.Tensor				):
          _A    		=  tf.convert_to_tensor(lowerCamelCase_				)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          _A    		=  encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          _A    		=  encoder_attention_mask[:, None, None, :]
     # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
     # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
     # /transformer/transformer_layers.py#L270
     # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
     # encoder_extended_attention_mask.transpose(-1, -2))
     _A    		=  (
         tf.cast(1   ,		encoder_attention_mask.dtype				) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Optional[Any] = "input_ids"				):
     tf.debugging.assert_less(
         lowerCamelCase_   ,		tf.cast(lowerCamelCase_   ,		dtype=tensor.dtype				)   ,		message=(
             F'The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCamelCase_				)}) must be smaller than the embedding '
             F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         )   ,		)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]   ,		__snake_case							:			str   ,		__snake_case							:			int				):
     _A    		=  6_4_5_1_2
     # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
     # because in that case even chunking the array would not make the saving
     # possible.
     _A    		=  [x for x in data if len(lowerCamelCase_				) > HDF5_OBJECT_HEADER_LIMIT]

     # Expecting this to never be true.
     if bad_attributes:
          raise RuntimeError(
              'The following attributes cannot be saved to HDF5 file because '
              F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
              F'bytes: {bad_attributes}'				)

     _A    		=  np.asarray(lowerCamelCase_				)

     _A    		=  1
     _A    		=  np.array_split(lowerCamelCase_   ,		lowerCamelCase_				)

     # This will never loop forever thanks to the test above.
     while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
          num_chunks += 1
          _A    		=  np.array_split(lowerCamelCase_   ,		lowerCamelCase_				)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(lowerCamelCase_				):
               _A    		=  chunk_data
     else:
          _A    		=  data





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			List[Any]				):



     if name in group.attrs:
          _A    		=  [n.decode('utf8'				) if hasattr(lowerCamelCase_   ,		'decode'				) else n for n in group.attrs[name]]
     else:
          _A    		=  []
          _A    		=  0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode('utf8'				) if hasattr(lowerCamelCase_   ,		'decode'				) else n for n in group.attrs['%s%d' % (name, chunk_id)]]				)
               chunk_id += 1
     return data





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):



     def _expand_single_ad_tensor(__snake_case							:			Union[str, Any]				):
          if isinstance(lowerCamelCase_   ,		tf.Tensor				) and t.shape.rank == 1:
               return tf.expand_dims(lowerCamelCase_   ,		axis=-1				)
          return t

     return tf.nest.map_structure(_expand_single_ad_tensor   ,		lowerCamelCase_				)










'''simple docstring'''




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 lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				List[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Optional[int]		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				int		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

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


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				List[str]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						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 lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			[torch.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				int		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(
							    lowerCamelCase_      ,						torch.tensor(lowerCamelCase_						)      ,						torch.tensor(lowerCamelCase_						)						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)

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

							SCREAMING_SNAKE_CASE :				Tuple		=			[[1, 0], [0, 1]]
							with self.assertRaises(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)







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




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				str		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

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


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			processor(images=lowerCamelCase_      ,						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 lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[tf.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						return_tensors="""tf"""      ,						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				List[str]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)

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

							SCREAMING_SNAKE_CASE :				str		=			[[1, 0], [0, 1]]
							with self.assertRaises(tf.errors.InvalidArgumentError						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
									    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)







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




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				int		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				str		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Tuple      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			np.random.randint(0      ,						2      ,						size=(1, 3, 5, 5)						).astype(np.floataa						)
							SCREAMING_SNAKE_CASE :				str		=			[tf.convert_to_tensor(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[torch.tensor(lowerCamelCase_						)]

							SCREAMING_SNAKE_CASE :				Tuple		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							SCREAMING_SNAKE_CASE :				Any		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""pt"""						)

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



					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				int		=			image_processor(lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Tuple		=			processor(images=lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()

							SCREAMING_SNAKE_CASE :				Dict		=			image_processor(lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()

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



