"""simple docstring"""



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


__lowercase		:		Dict														=							logging.get_logger(__name__)

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



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					'''switch_transformers'''
    UpperCamelCase_	:					Any							=					['''past_key_values''']
    UpperCamelCase_	:					Optional[int]							=					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
    def __init__(		self							:     Optional[int]	,      A_							:     List[str]=32_128	,      A_							:     Tuple=768	,      A_							:     List[str]=64	,      A_							:     Optional[Any]=2_048	,      A_							:     Dict=64	,      A_							:     Optional[int]=12	,      A_							:     int=3	,      A_							:     Tuple=12	,      A_							:     List[str]=3	,      A_							:     List[str]=12	,      A_							:     List[Any]=8	,      A_							:     List[str]=False	,      A_							:     Dict=0.01	,      A_							:     Dict="float32"	,      A_							:     int=False	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=128	,      A_							:     Union[str, Any]=0.1	,      A_							:     Any=1E-6	,      A_							:     int=0.0_01	,      A_							:     Union[str, Any]=0.0_01	,      A_							:     Dict=1.0	,      A_							:     List[str]="relu"	,      A_							:     List[str]=True	,      A_							:     Optional[Any]=False	,      A_							:     Union[str, Any]=True	,      A_							:     Dict=0	,      A_							:     List[Any]=1	,      **A_							:     Dict	,      )    ->       Tuple:
         __snake_case				=							vocab_size
         __snake_case				=							d_model
         __snake_case				=							d_kv
         __snake_case				=							d_ff

         __snake_case				=							num_sparse_encoder_layers

         __snake_case				=							num_layers
         __snake_case				=							(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __snake_case				=							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:
              __snake_case				=							self.num_layers // self.num_sparse_encoder_layers
         else:
              __snake_case				=							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:
              __snake_case				=							self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              __snake_case				=							self.num_decoder_layers  # HACK: this will create 0 sparse layers

         __snake_case				=							num_heads
         __snake_case				=							num_experts
         __snake_case				=							expert_capacity
         __snake_case				=							router_bias
         __snake_case				=							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}"    )
         __snake_case				=							router_dtype

         __snake_case				=							router_ignore_padding_tokens
         __snake_case				=							relative_attention_num_buckets
         __snake_case				=							relative_attention_max_distance

         __snake_case				=							dropout_rate
         __snake_case				=							layer_norm_epsilon
         __snake_case				=							initializer_factor
         __snake_case				=							feed_forward_proj
         __snake_case				=							use_cache
         __snake_case				=							add_router_probs

         __snake_case				=							router_z_loss_coef
         __snake_case				=							router_aux_loss_coef

         __snake_case				=							self.feed_forward_proj.split('''-'''    )
         __snake_case				=							act_info[-1]
         __snake_case				=							act_info[0] == '''gated'''

         if len(A_    ) > 1 and act_info[0] != "gated" or len(A_    ) > 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":
              __snake_case				=							'''gelu_new'''

         super().__init__(
             pad_token_id=A_	,      eos_token_id=A_	,      is_encoder_decoder=A_	,      **A_	,      )
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       List[str]:
         __snake_case				=							tempfile.mkdtemp()

         # fmt: off
         __snake_case				=							['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
         # fmt: on
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
         __snake_case				=							{'''unk_token''': '''<unk>'''}

         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''merges_file''']    )
         with open(self.vocab_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write(json.dumps(A_    ) + '''\n'''    )
         with open(self.merges_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write('''\n'''.join(A_    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     Optional[Any]	,      **A_							:     Dict    )    ->       Any:
         return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     str    )    ->       str:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Any	,      **A_							:     Tuple    )    ->       Tuple:
         return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

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

         return image_inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							CLIPSegProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       List[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(images=A_	,      visual_prompt=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''pixel_values''', '''conditional_pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )