"""simple docstring"""




import copy
import os
from typing import Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Any  							=						'bridgetower_vision_model'





						def __init__(				self						,__UpperCAmelCase=7_68						,__UpperCAmelCase=12						,__UpperCAmelCase=3						,__UpperCAmelCase=16						,__UpperCAmelCase=2_88						,__UpperCAmelCase=1						,__UpperCAmelCase=1e-05						,__UpperCAmelCase=False						,__UpperCAmelCase=True						,__UpperCAmelCase=False						,**__UpperCAmelCase						,)    -> List[Any]:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_channels
													A__		   =							patch_size
													A__		   =							image_size
													A__		   =							initializer_factor
													A__		   =							layer_norm_eps
													A__		   =							stop_gradient
													A__		   =							share_layernorm
													A__		   =							remove_last_layer





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> "PretrainedConfig":
													A__   ,     A__		   =							cls.get_config_dict(__UpperCAmelCase						,**__UpperCAmelCase   )

													if config_dict.get('model_type'   ) == "bridgetower":
																				A__		   =							config_dict['text_config']

													if "model_type" in config_dict and hasattr(cls						,'model_type'   ) and config_dict["model_type"] != cls.model_type:
																				logger.warning(
																				    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
																				    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

													return cls.from_dict(__UpperCAmelCase						,**__UpperCAmelCase   )






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Dict  							=						'bridgetower_text_model'





						def __init__(				self						,__UpperCAmelCase=5_02_65						,__UpperCAmelCase=7_68						,__UpperCAmelCase=12						,__UpperCAmelCase=12						,__UpperCAmelCase=1						,__UpperCAmelCase=30_72						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.1						,__UpperCAmelCase=5_14						,__UpperCAmelCase=1						,__UpperCAmelCase=1e-05						,__UpperCAmelCase=1						,__UpperCAmelCase=0						,__UpperCAmelCase=2						,__UpperCAmelCase="absolute"						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> Union[str, Any]:
													super().__init__(**__UpperCAmelCase   )

													A__		   =							vocab_size
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							hidden_act
													A__		   =							initializer_factor
													A__		   =							intermediate_size
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							max_position_embeddings
													A__		   =							type_vocab_size
													A__		   =							layer_norm_eps
													A__		   =							position_embedding_type
													A__		   =							use_cache
													A__		   =							pad_token_id
													A__		   =							bos_token_id
													A__		   =							eos_token_id





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> "PretrainedConfig":
													A__   ,     A__		   =							cls.get_config_dict(__UpperCAmelCase						,**__UpperCAmelCase   )

													if config_dict.get('model_type'   ) == "bridgetower":
																				A__		   =							config_dict['text_config']

													if "model_type" in config_dict and hasattr(cls						,'model_type'   ) and config_dict["model_type"] != cls.model_type:
																				logger.warning(
																				    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
																				    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

													return cls.from_dict(__UpperCAmelCase						,**__UpperCAmelCase   )






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  int  							=						'bridgetower'





						def __init__(				self						,__UpperCAmelCase=True						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=7_68						,__UpperCAmelCase=1						,__UpperCAmelCase=1e-05						,__UpperCAmelCase=False						,__UpperCAmelCase="add"						,__UpperCAmelCase=12						,__UpperCAmelCase=6						,__UpperCAmelCase=False						,__UpperCAmelCase=False						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													# TODO: remove this once the Hub files are updated.
													A__		   =							kwargs.pop('text_config_dict'						,__UpperCAmelCase   )
													A__		   =							kwargs.pop('vision_config_dict'						,__UpperCAmelCase   )

													super().__init__(**__UpperCAmelCase   )
													A__		   =							share_cross_modal_transformer_layers
													A__		   =							hidden_act
													A__		   =							hidden_size
													A__		   =							initializer_factor
													A__		   =							layer_norm_eps
													A__		   =							share_link_tower_layers
													A__		   =							link_tower_type
													A__		   =							num_attention_heads
													A__		   =							num_hidden_layers
													A__		   =							tie_word_embeddings
													A__		   =							init_layernorm_from_vision_encoder

													if text_config is None:
																				A__		   =							{}
																				logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'   )

													if vision_config is None:
																				A__		   =							{}
																				logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'   )

													A__		   =							BridgeTowerTextConfig(**__UpperCAmelCase   )
													A__		   =							BridgeTowerVisionConfig(**__UpperCAmelCase   )





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> List[Any]:
													return cls(text_config=text_config.to_dict()						,vision_config=vision_config.to_dict()						,**__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							copy.deepcopy(self.__dict__   )
													A__		   =							self.text_config.to_dict()
													A__		   =							self.vision_config.to_dict()
													A__		   =							self.__class__.model_type
													return output






"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						[
						    # (model_name, model_kwargs)
						    ('bert-base-cased', {}),
						    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

																				# Remove folder if exists
																				if path.parent.exists():
																											path.parent.rmdir()

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

																				return path
													except Exception as e:
																				self.fail(__UpperCAmelCase   )





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

													# Assert all variables are present
													self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
													self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
													self.assertSequenceEqual(variable_names[:3]						,__UpperCAmelCase   )
													self.assertSequenceEqual(variable_names[3:]						,__UpperCAmelCase   )

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																				self.assertDictEqual(shapes[var_name]						,{0: 'batch', 1: 'sequence'}   )

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes['output_0']						,{0: 'batch', 1: 'sequence'}   )
													self.assertDictEqual(shapes['output_1']						,{0: 'batch'}   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(__UpperCAmelCase   )						,3   )

													# Should have exactly the same input names
													self.assertEqual(set(__UpperCAmelCase   )						,set(__UpperCAmelCase   )   )

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(__UpperCAmelCase						,(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])   )

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__   ,     A__		   =							ensure_valid_input(FuncNonContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,1   )

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]						,tokens['input_ids']   )
													self.assertEqual(ordered_input_names[0]						,'input_ids'   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





