





'''simple docstring'''


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_lowerCamelCase :		Dict 			=				logging.get_logger(__name__)




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



	"""simple docstring"""







	A							     =					r'\w+[.]\d+'
	A							     =					re.findall(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE			)
	for pat in pats:
		A							     =					key.replace(_SCREAMING_SNAKE_CASE				,	"""_""".join(pat.split("""."""			)			)			)
	return key




def       __a       (   UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase			)					->str:



	"""simple docstring"""







	A							     =					pt_tuple_key[:-1] + ('scale',)
	if (
	    any("""norm""" in str_ for str_ in pt_tuple_key			)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		A							     =					pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		A							     =					pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		A							     =					pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	A							     =					pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		A							     =					pt_tensor.transpose(2				,	3				,	1				,	0			)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	A							     =					pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		A							     =					pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	A							     =					pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	A							     =					pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor




def       __a       (   UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase=42			)					->List[str]:



	"""simple docstring"""







	A							     =					{k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	A							     =					flax_model.init_weights(PRNGKey(_SCREAMING_SNAKE_CASE			)			)

	A							     =					flatten_dict(_SCREAMING_SNAKE_CASE			)
	A							     =					{}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		A							     =					rename_key(_SCREAMING_SNAKE_CASE			)
		A							     =					tuple(renamed_pt_key.split("""."""			)			)

		# Correctly rename weight parameters
		A							     =					rename_key_and_reshape_tensor(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE			)

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
				    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)

				# also add unexpected weight so that warning is thrown
		A							     =					jnp.asarray(_SCREAMING_SNAKE_CASE			)

	return unflatten_dict(_SCREAMING_SNAKE_CASE			)


"""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    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     Optional[int]										=	SamImageProcessor()
        a__:     Tuple										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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

        a__:     Optional[Any]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Optional[int]										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

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

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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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

        a__:     List[Any]										=	SamProcessor(image_processor=lowercase)

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

        a__:     Optional[Any]										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Tuple										=	processor(images=lowercase    ,			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)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()

        a__:     List[str]										=	SamProcessor(image_processor=lowercase)
        a__:     Optional[Any]										=	[torch.ones((1, 3, 5, 5))]

        a__:     Union[str, Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     int										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Optional[int]										=	processor.post_process_masks(
            lowercase    ,			torch.tensor(lowercase)    ,			torch.tensor(lowercase))
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Dict										=	[np.ones((1, 3, 5, 5))]
        a__:     Tuple										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))

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

        a__:     Tuple										=	[[1, 0], [0, 1]]
        with self.assertRaises(lowercase):
          a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))







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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     List[Any]										=	SamImageProcessor()
        a__:     Optional[int]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			int:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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

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

        return image_inputs



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Dict										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     Union[str, Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

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



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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

        a__:     str										=	SamProcessor(image_processor=lowercase)

        a__:     int										=	self.prepare_image_inputs()

        a__:     int										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Dict										=	processor(images=lowercase    ,			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)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Any										=	SamProcessor(image_processor=lowercase)
        a__:     str										=	[tf.ones((1, 3, 5, 5))]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     List[Any]										=	[[6_83, 10_24]]
        a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

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

        # should also work with np
        a__:     Optional[Any]										=	[np.ones((1, 3, 5, 5))]
        a__:     int										=	processor.post_process_masks(
            lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')

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

        a__:     List[str]										=	[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError):
          a__:     Any										=	processor.post_process_masks(
              lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')







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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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

        a__:     Any										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	self.get_image_processor()

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

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        a__:     str										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='pt')

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



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Dict										=	SamProcessor(image_processor=lowercase)

        a__:     Any										=	self.prepare_image_inputs()

        a__:     List[Any]										=	image_processor(lowercase    ,			return_tensors='pt')['pixel_values'].numpy()
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='pt')['pixel_values'].numpy()

        a__:     Any										=	image_processor(lowercase    ,			return_tensors='tf')['pixel_values'].numpy()
        a__:     Any										=	processor(images=lowercase    ,			return_tensors='tf')['pixel_values'].numpy()

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


