

import functools
import operator

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


_UpperCAmelCase		:       List[str]						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       List[Any]						    =	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      '''sew-d'''




				def __init__( self				,       snake_case=32				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case=2				,       snake_case=512				,       snake_case=256				,       snake_case=True				,       snake_case=True				,       snake_case=("p2c", "c2p")				,       snake_case="layer_norm"				,       snake_case="gelu_python"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=0.0				,       snake_case=0.1				,       snake_case=0.02				,       snake_case=1e-7				,       snake_case=1e-5				,       snake_case="group"				,       snake_case="gelu"				,       snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,       snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,       snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,       snake_case=False				,       snake_case=128				,       snake_case=16				,       snake_case=True				,       snake_case=0.05				,       snake_case=10				,       snake_case=2				,       snake_case=0.0				,       snake_case=10				,       snake_case=0				,       snake_case="mean"				,       snake_case=False				,       snake_case=False				,       snake_case=256				,       snake_case=0				,       snake_case=1				,       snake_case=2				,       **snake_case				,       ):
											super().__init__(**snake_case				,       pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case	)
											snake_case_          =   hidden_size
											snake_case_          =   feat_extract_norm
											snake_case_          =   feat_extract_activation
											snake_case_          =   list(snake_case	)
											snake_case_          =   list(snake_case	)
											snake_case_          =   list(snake_case	)
											snake_case_          =   conv_bias
											snake_case_          =   num_conv_pos_embeddings
											snake_case_          =   num_conv_pos_embedding_groups
											snake_case_          =   len(self.conv_dim	)
											snake_case_          =   num_hidden_layers
											snake_case_          =   intermediate_size
											snake_case_          =   squeeze_factor
											snake_case_          =   max_position_embeddings
											snake_case_          =   position_buckets
											snake_case_          =   share_att_key
											snake_case_          =   relative_attention
											snake_case_          =   norm_rel_ebd
											snake_case_          =   list(snake_case	)
											snake_case_          =   hidden_act
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_dropout
											snake_case_          =   attention_dropout
											snake_case_          =   activation_dropout
											snake_case_          =   feat_proj_dropout
											snake_case_          =   final_dropout
											snake_case_          =   layer_norm_eps
											snake_case_          =   feature_layer_norm_eps
											snake_case_          =   initializer_range
											snake_case_          =   vocab_size

											if (
											    (len(self.conv_stride	) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
											    or (len(self.conv_dim	) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    'Configuration for convolutional layers is incorrect.'
																		    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
																		    F'''but is `len(config.conv_dim) = {len(self.conv_dim	)}`, `len(config.conv_stride)'''
																		    F'''= {len(self.conv_stride	)}`, `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											snake_case_          =   apply_spec_augment
											snake_case_          =   mask_time_prob
											snake_case_          =   mask_time_length
											snake_case_          =   mask_time_min_masks
											snake_case_          =   mask_feature_prob
											snake_case_          =   mask_feature_length
											snake_case_          =   mask_feature_min_masks

											# ctc loss
											snake_case_          =   ctc_loss_reduction
											snake_case_          =   ctc_zero_infinity

											# sequence classification
											snake_case_          =   use_weighted_layer_sum
											snake_case_          =   classifier_proj_size




				@property
				def    a							( self	):
											return functools.reduce(operator.mul				,       self.conv_stride				,       1	)







import os
import sys
import unittest


_UpperCAmelCase		:       str						    =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_UpperCAmelCase		:       List[Any]						    =	os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_UpperCAmelCase		:       Dict						    =	os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")







class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											snake_case_          =   get_test_to_tester_mapping(snake_case	)
											snake_case_          =   get_test_to_tester_mapping(snake_case	)

											snake_case_          =   {'BertModelTest': 'BertModelTester'}

											snake_case_          =   {
											    'BlipModelTest': 'BlipModelTester',
											    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
											    'BlipTextModelTest': 'BlipTextModelTester',
											    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
											    'BlipVQAModelTest': 'BlipVQAModelTester',
											    'BlipVisionModelTest': 'BlipVisionModelTester',
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_test_mapping(snake_case	)
											snake_case_          =   get_model_to_test_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTest'],
											    'BertForMultipleChoice': ['BertModelTest'],
											    'BertForNextSentencePrediction': ['BertModelTest'],
											    'BertForPreTraining': ['BertModelTest'],
											    'BertForQuestionAnswering': ['BertModelTest'],
											    'BertForSequenceClassification': ['BertModelTest'],
											    'BertForTokenClassification': ['BertModelTest'],
											    'BertLMHeadModel': ['BertModelTest'],
											    'BertModel': ['BertModelTest'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
											    'BlipModel': ['BlipModelTest'],
											    'BlipTextModel': ['BlipTextModelTest'],
											    'BlipVisionModel': ['BlipVisionModelTest'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_tester_mapping(snake_case	)
											snake_case_          =   get_model_to_tester_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTester'],
											    'BertForMultipleChoice': ['BertModelTester'],
											    'BertForNextSentencePrediction': ['BertModelTester'],
											    'BertForPreTraining': ['BertModelTester'],
											    'BertForQuestionAnswering': ['BertModelTester'],
											    'BertForSequenceClassification': ['BertModelTester'],
											    'BertForTokenClassification': ['BertModelTester'],
											    'BertLMHeadModel': ['BertModelTester'],
											    'BertModel': ['BertModelTester'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
											    'BlipModel': ['BlipModelTester'],
											    'BlipTextModel': ['BlipTextModelTester'],
											    'BlipVisionModel': ['BlipVisionModelTester'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




