"""simple docstring"""







import unittest

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 ViTImageProcessor







class  UpperCamelCase	(    unittest.TestCase						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=3  ,__UpperCamelCase=224  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=True  ,__UpperCamelCase=None  ,__UpperCamelCase=True  ,__UpperCamelCase=[0.5, 0.5, 0.5]  ,__UpperCamelCase=[0.5, 0.5, 0.5]  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Any									=			size if size is not None else {'height': 18, 'width': 18}
        lowercase_			:       List[Any]									=			parent
        lowercase_			:       Any									=			batch_size
        lowercase_			:       Dict									=			num_channels
        lowercase_			:       str									=			image_size
        lowercase_			:       Tuple									=			min_resolution
        lowercase_			:       Optional[int]									=			max_resolution
        lowercase_			:       Any									=			do_resize
        lowercase_			:       Optional[int]									=			size
        lowercase_			:       Optional[int]									=			do_normalize
        lowercase_			:       Optional[Any]									=			image_mean
        lowercase_			:       Optional[int]									=			image_std







   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        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  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						ViTImageProcessor if is_vision_available() else None
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			EfficientFormerImageProcessorTester(self    )
   @property
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        return self.image_proc_tester.prepare_image_processor_dict()
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			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    )			->   Optional[int]:


        '''simple docstring'''


        pass
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowercase_			:       Union[str, Any]									=			prepare_image_inputs(self.image_proc_tester  ,equal_resolution=UpperCamelCase__    )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__  ,Image.Image    )

        # Test not batched input
        lowercase_			:       List[Any]									=			image_processor(image_inputs[0]  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)

        # Test batched
        lowercase_			:       Dict									=			image_processor(UpperCamelCase__  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowercase_			:       List[Any]									=			prepare_image_inputs(self.image_proc_tester  ,equal_resolution=UpperCamelCase__  ,numpify=UpperCamelCase__    )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__  ,np.ndarray    )

        # Test not batched input
        lowercase_			:       List[Any]									=			image_processor(image_inputs[0]  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)

        # Test batched
        lowercase_			:       int									=			image_processor(UpperCamelCase__  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)







   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowercase_			:       Optional[Any]									=			prepare_image_inputs(self.image_proc_tester  ,equal_resolution=UpperCamelCase__  ,torchify=UpperCamelCase__    )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__  ,torch.Tensor    )

        # Test not batched input
        lowercase_			:       Any									=			image_processor(image_inputs[0]  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)

        # Test batched
        lowercase_			:       str									=			image_processor(UpperCamelCase__  ,return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )  ,)







"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'switch_transformers'
   lowercase							=						['past_key_values']
   lowercase							=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
   def __init__(    self  ,__UpperCamelCase=3_2128  ,__UpperCamelCase=768  ,__UpperCamelCase=64  ,__UpperCamelCase=2048  ,__UpperCamelCase=64  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=8  ,__UpperCamelCase=False  ,__UpperCamelCase=0.01  ,__UpperCamelCase="float32"  ,__UpperCamelCase=False  ,__UpperCamelCase=32  ,__UpperCamelCase=128  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-6  ,__UpperCamelCase=0.001  ,__UpperCamelCase=0.001  ,__UpperCamelCase=1.0  ,__UpperCamelCase="relu"  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			vocab_size
        lowercase_			:       Optional[Any]									=			d_model
        lowercase_			:       Dict									=			d_kv
        lowercase_			:       Dict									=			d_ff

        lowercase_			:       str									=			num_sparse_encoder_layers

        lowercase_			:       List[str]									=			num_layers
        lowercase_			:       int									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       Optional[Any]									=			num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
             lowercase_			:       Tuple									=			self.num_layers // self.num_sparse_encoder_layers
        else:
             lowercase_			:       Union[str, Any]									=			self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
             lowercase_			:       Union[str, Any]									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
             lowercase_			:       List[Any]									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       Dict									=			num_experts
        lowercase_			:       List[str]									=			expert_capacity
        lowercase_			:       Optional[Any]									=			router_bias
        lowercase_			:       Optional[Any]									=			router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''    )
        lowercase_			:       Optional[Any]									=			router_dtype

        lowercase_			:       Union[str, Any]									=			router_ignore_padding_tokens
        lowercase_			:       Any									=			relative_attention_num_buckets
        lowercase_			:       List[Any]									=			relative_attention_max_distance

        lowercase_			:       str									=			dropout_rate
        lowercase_			:       Any									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       str									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache
        lowercase_			:       str									=			add_router_probs

        lowercase_			:       Tuple									=			router_z_loss_coef
        lowercase_			:       int									=			router_aux_loss_coef

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       List[str]									=			act_info[-1]
        lowercase_			:       Optional[Any]									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 2:
             raise ValueError(
                 f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
             lowercase_			:       Union[str, Any]									=			'gelu_new'

        super().__init__(
            pad_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase  ,is_encoder_decoder=__UpperCamelCase  ,**__UpperCamelCase  ,)

