


'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Optional[int]					      =			'''instructblip_vision_model'''
			def __init__(  self							:			Dict			,    lowerCAmelCase_							:			List[Any]=1_408			,    lowerCAmelCase_							:			List[str]=6_144			,    lowerCAmelCase_							:			str=39			,    lowerCAmelCase_							:			Any=16			,    lowerCAmelCase_							:			Any=224			,    lowerCAmelCase_							:			List[Any]=14			,    lowerCAmelCase_							:			Union[str, Any]="gelu"			,    lowerCAmelCase_							:			int=1E-6			,    lowerCAmelCase_							:			Any=0.0			,    lowerCAmelCase_							:			Optional[int]=1E-10			,    lowerCAmelCase_							:			Optional[int]=True			,    **lowerCAmelCase_							:			Optional[int]			,    )      ->		Dict:
						super().__init__(**lowerCAmelCase_	)

						__A=	hidden_size
						__A=	intermediate_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	patch_size
						__A=	image_size
						__A=	initializer_range
						__A=	attention_dropout
						__A=	layer_norm_eps
						__A=	hidden_act
						__A=	qkv_bias

			@classmethod
			def 					lowerCAmelCase     (  cls							:			str			,    lowerCAmelCase_							:			Union[str, os.PathLike]			,    **lowerCAmelCase_							:			Dict	)      ->		"PretrainedConfig":
						cls._set_token_in_kwargs(lowerCAmelCase_	)

						__A,	__A=	cls.get_config_dict(lowerCAmelCase_			,    **lowerCAmelCase_	)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'	) == "instructblip":
									__A=	config_dict['vision_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(lowerCAmelCase_			,    **lowerCAmelCase_	)


class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''instructblip_qformer'''
			def __init__(  self							:			str			,    lowerCAmelCase_							:			List[Any]=30_522			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Dict=12			,    lowerCAmelCase_							:			List[str]=12			,    lowerCAmelCase_							:			Union[str, Any]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[Any]=0.1			,    lowerCAmelCase_							:			List[Any]=0.1			,    lowerCAmelCase_							:			Union[str, Any]=512			,    lowerCAmelCase_							:			List[str]=0.02			,    lowerCAmelCase_							:			str=1E-12			,    lowerCAmelCase_							:			Union[str, Any]=0			,    lowerCAmelCase_							:			int="absolute"			,    lowerCAmelCase_							:			int=2			,    lowerCAmelCase_							:			Dict=1_408			,    **lowerCAmelCase_							:			int			,    )      ->		Optional[int]:
						super().__init__(pad_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	cross_attention_frequency
						__A=	encoder_hidden_size

			@classmethod
			def 					lowerCAmelCase     (  cls							:			Dict			,    lowerCAmelCase_							:			Union[str, os.PathLike]			,    **lowerCAmelCase_							:			Any	)      ->		"PretrainedConfig":
						cls._set_token_in_kwargs(lowerCAmelCase_	)

						__A,	__A=	cls.get_config_dict(lowerCAmelCase_			,    **lowerCAmelCase_	)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'	) == "instructblip":
									__A=	config_dict['qformer_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(lowerCAmelCase_			,    **lowerCAmelCase_	)


class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Optional[int]					      =			'''instructblip'''
			A						:				str					      =			True
			def __init__(  self							:			int			,    lowerCAmelCase_							:			List[str]=None			,    lowerCAmelCase_							:			str=None			,    lowerCAmelCase_							:			Dict=None			,    lowerCAmelCase_							:			str=32			,    **lowerCAmelCase_							:			Optional[int]	)      ->		Any:
						super().__init__(**lowerCAmelCase_	)

						if vision_config is None:
									__A=	{}
									logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'	)

						if qformer_config is None:
									__A=	{}
									logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'	)

						if text_config is None:
									__A=	{}
									logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'	)

						__A=	InstructBlipVisionConfig(**lowerCAmelCase_	)
						__A=	InstructBlipQFormerConfig(**lowerCAmelCase_	)
						__A=	text_config['model_type'] if 'model_type' in text_config else 'opt'
						__A=	CONFIG_MAPPING[text_model_type](**lowerCAmelCase_	)

						__A=	self.text_config.tie_word_embeddings
						__A=	self.text_config.is_encoder_decoder

						__A=	num_query_tokens
						__A=	self.vision_config.hidden_size
						__A=	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						__A=	1.0
						__A=	0.02
			@classmethod
			def 					lowerCAmelCase     (  cls							:			Optional[Any]			,    lowerCAmelCase_							:			InstructBlipVisionConfig			,    lowerCAmelCase_							:			InstructBlipQFormerConfig			,    lowerCAmelCase_							:			PretrainedConfig			,    **lowerCAmelCase_							:			List[str]			,    )      ->		Union[str, Any]:
						return cls(
						    vision_config=vision_config.to_dict()			,    qformer_config=qformer_config.to_dict()			,    text_config=text_config.to_dict()			,    **lowerCAmelCase_			,    )

			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Union[str, Any]:
						__A=	copy.deepcopy(self.__dict__	)
						__A=	self.vision_config.to_dict()
						__A=	self.qformer_config.to_dict()
						__A=	self.text_config.to_dict()
						__A=	self.__class__.model_type
						return output







'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



