


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    tempfile.mkdtemp()

             lowerCamelCase__						:							Optional[Any]       =    CLIPImageProcessor()
             lowerCamelCase__						:							Optional[int]       =    BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"   , model_input_names=["input_ids", "attention_mask"]			)

             lowerCamelCase__						:							Optional[int]       =    GitProcessor(_lowerCAmelCase   , _lowerCAmelCase			)

             processor.save_pretrained(self.tmpdirname			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , **__lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             return AutoProcessor.from_pretrained(self.tmpdirname   , **_lowerCAmelCase			).tokenizer





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


             '''simple docstring'''



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





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             shutil.rmtree(self.tmpdirname			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    [np.random.randint(255   , size=(3, 30, 400)   , dtype=np.uinta			)]

             lowerCamelCase__						:							Tuple       =    [Image.fromarray(np.moveaxis(_lowerCAmelCase   , 0   , -1			)			) for x in image_inputs]

             return image_inputs





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    GitProcessor(tokenizer=self.get_tokenizer()   , image_processor=self.get_image_processor()			)
             processor.save_pretrained(self.tmpdirname			)

             lowerCamelCase__						:							Union[str, Any]       =    self.get_tokenizer(bos_token="(BOS)"   , eos_token="(EOS)"			)
             lowerCamelCase__						:							int       =    self.get_image_processor(do_normalize=_lowerCAmelCase   , padding_value=1.0			)

             lowerCamelCase__						:							Union[str, Any]       =    GitProcessor.from_pretrained(
                 self.tmpdirname   , bos_token="(BOS)"   , eos_token="(EOS)"   , do_normalize=_lowerCAmelCase   , padding_value=1.0			)

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

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





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    self.get_image_processor()
             lowerCamelCase__						:							Union[str, Any]       =    self.get_tokenizer()

             lowerCamelCase__						:							List[str]       =    GitProcessor(tokenizer=_lowerCAmelCase   , image_processor=_lowerCAmelCase			)

             lowerCamelCase__						:							Union[str, Any]       =    self.prepare_image_inputs()

             lowerCamelCase__						:							Optional[Any]       =    image_processor(_lowerCAmelCase   , return_tensors="np"			)
             lowerCamelCase__						:							str       =    processor(images=_lowerCAmelCase   , 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 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    self.get_image_processor()
             lowerCamelCase__						:							Optional[Any]       =    self.get_tokenizer()

             lowerCamelCase__						:							Optional[Any]       =    GitProcessor(tokenizer=_lowerCAmelCase   , image_processor=_lowerCAmelCase			)

             lowerCamelCase__						:							Any       =    """lower newer"""

             lowerCamelCase__						:							Tuple       =    processor(text=_lowerCAmelCase			)

             lowerCamelCase__						:							int       =    tokenizer(_lowerCAmelCase   , return_token_type_ids=_lowerCAmelCase			)

             for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key]   , encoded_processor[key]			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.get_image_processor()
             lowerCamelCase__						:							Tuple       =    self.get_tokenizer()

             lowerCamelCase__						:							Optional[int]       =    GitProcessor(tokenizer=_lowerCAmelCase   , image_processor=_lowerCAmelCase			)

             lowerCamelCase__						:							Dict       =    """lower newer"""
             lowerCamelCase__						:							Any       =    self.prepare_image_inputs()

             lowerCamelCase__						:							Tuple       =    processor(text=_lowerCAmelCase   , images=_lowerCAmelCase			)

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

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





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.get_image_processor()
             lowerCamelCase__						:							Optional[Any]       =    self.get_tokenizer()

             lowerCamelCase__						:							List[Any]       =    GitProcessor(tokenizer=_lowerCAmelCase   , image_processor=_lowerCAmelCase			)

             lowerCamelCase__						:							str       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             lowerCamelCase__						:							Optional[int]       =    processor.batch_decode(_lowerCAmelCase			)
             lowerCamelCase__						:							Dict       =    tokenizer.batch_decode(_lowerCAmelCase			)

             self.assertListEqual(_lowerCAmelCase   , _lowerCAmelCase			)





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


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.get_image_processor()
             lowerCamelCase__						:							int       =    self.get_tokenizer()

             lowerCamelCase__						:							Dict       =    GitProcessor(tokenizer=_lowerCAmelCase   , image_processor=_lowerCAmelCase			)

             lowerCamelCase__						:							List[str]       =    """lower newer"""
             lowerCamelCase__						:							int       =    self.prepare_image_inputs()

             lowerCamelCase__						:							List[Any]       =    processor(text=_lowerCAmelCase   , images=_lowerCAmelCase			)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()			)   , ["input_ids", "attention_mask", "pixel_values"]			)



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



