






from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase_ :						Union[str, Any]      =      logging.get_logger(__name__)


UpperCAmelCase_ :						List[str]      =      OrderedDict(
    [
        # Base model mapping
        ('''albert''', '''FlaxAlbertModel'''),
        ('''bart''', '''FlaxBartModel'''),
        ('''beit''', '''FlaxBeitModel'''),
        ('''bert''', '''FlaxBertModel'''),
        ('''big_bird''', '''FlaxBigBirdModel'''),
        ('''blenderbot''', '''FlaxBlenderbotModel'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
        ('''clip''', '''FlaxCLIPModel'''),
        ('''distilbert''', '''FlaxDistilBertModel'''),
        ('''electra''', '''FlaxElectraModel'''),
        ('''gpt-sw3''', '''FlaxGPT2Model'''),
        ('''gpt2''', '''FlaxGPT2Model'''),
        ('''gpt_neo''', '''FlaxGPTNeoModel'''),
        ('''gptj''', '''FlaxGPTJModel'''),
        ('''longt5''', '''FlaxLongT5Model'''),
        ('''marian''', '''FlaxMarianModel'''),
        ('''mbart''', '''FlaxMBartModel'''),
        ('''mt5''', '''FlaxMT5Model'''),
        ('''opt''', '''FlaxOPTModel'''),
        ('''pegasus''', '''FlaxPegasusModel'''),
        ('''regnet''', '''FlaxRegNetModel'''),
        ('''resnet''', '''FlaxResNetModel'''),
        ('''roberta''', '''FlaxRobertaModel'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
        ('''roformer''', '''FlaxRoFormerModel'''),
        ('''t5''', '''FlaxT5Model'''),
        ('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
        ('''vit''', '''FlaxViTModel'''),
        ('''wav2vec2''', '''FlaxWav2Vec2Model'''),
        ('''whisper''', '''FlaxWhisperModel'''),
        ('''xglm''', '''FlaxXGLMModel'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
    ]
)

UpperCAmelCase_ :						Tuple      =      OrderedDict(
    [
        # Model for pre-training mapping
        ('''albert''', '''FlaxAlbertForPreTraining'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForPreTraining'''),
        ('''big_bird''', '''FlaxBigBirdForPreTraining'''),
        ('''electra''', '''FlaxElectraForPreTraining'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
        ('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

UpperCAmelCase_ :						List[str]      =      OrderedDict(
    [
        # Model for Masked LM mapping
        ('''albert''', '''FlaxAlbertForMaskedLM'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForMaskedLM'''),
        ('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
        ('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
        ('''electra''', '''FlaxElectraForMaskedLM'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

UpperCAmelCase_ :						str      =      OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
        ('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''marian''', '''FlaxMarianMTModel'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
    ]
)

UpperCAmelCase_ :						List[Any]      =      OrderedDict(
    [
        # Model for Image-classsification
        ('''beit''', '''FlaxBeitForImageClassification'''),
        ('''regnet''', '''FlaxRegNetForImageClassification'''),
        ('''resnet''', '''FlaxResNetForImageClassification'''),
        ('''vit''', '''FlaxViTForImageClassification'''),
    ]
)

UpperCAmelCase_ :						Any      =      OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

UpperCAmelCase_ :						str      =      OrderedDict(
    [
        # Model for Causal LM mapping
        ('''bart''', '''FlaxBartForCausalLM'''),
        ('''bert''', '''FlaxBertForCausalLM'''),
        ('''big_bird''', '''FlaxBigBirdForCausalLM'''),
        ('''electra''', '''FlaxElectraForCausalLM'''),
        ('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt2''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
        ('''gptj''', '''FlaxGPTJForCausalLM'''),
        ('''opt''', '''FlaxOPTForCausalLM'''),
        ('''roberta''', '''FlaxRobertaForCausalLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
        ('''xglm''', '''FlaxXGLMForCausalLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
    ]
)

UpperCAmelCase_ :						int      =      OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('''albert''', '''FlaxAlbertForSequenceClassification'''),
        ('''bart''', '''FlaxBartForSequenceClassification'''),
        ('''bert''', '''FlaxBertForSequenceClassification'''),
        ('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
        ('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
        ('''electra''', '''FlaxElectraForSequenceClassification'''),
        ('''mbart''', '''FlaxMBartForSequenceClassification'''),
        ('''roberta''', '''FlaxRobertaForSequenceClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
        ('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
    ]
)

UpperCAmelCase_ :						int      =      OrderedDict(
    [
        # Model for Question Answering mapping
        ('''albert''', '''FlaxAlbertForQuestionAnswering'''),
        ('''bart''', '''FlaxBartForQuestionAnswering'''),
        ('''bert''', '''FlaxBertForQuestionAnswering'''),
        ('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
        ('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
        ('''electra''', '''FlaxElectraForQuestionAnswering'''),
        ('''mbart''', '''FlaxMBartForQuestionAnswering'''),
        ('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
        ('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
    ]
)

UpperCAmelCase_ :						Any      =      OrderedDict(
    [
        # Model for Token Classification mapping
        ('''albert''', '''FlaxAlbertForTokenClassification'''),
        ('''bert''', '''FlaxBertForTokenClassification'''),
        ('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
        ('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
        ('''electra''', '''FlaxElectraForTokenClassification'''),
        ('''roberta''', '''FlaxRobertaForTokenClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
        ('''roformer''', '''FlaxRoFormerForTokenClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
    ]
)

UpperCAmelCase_ :						Optional[Any]      =      OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('''albert''', '''FlaxAlbertForMultipleChoice'''),
        ('''bert''', '''FlaxBertForMultipleChoice'''),
        ('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
        ('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
        ('''electra''', '''FlaxElectraForMultipleChoice'''),
        ('''roberta''', '''FlaxRobertaForMultipleChoice'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
        ('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
    ]
)

UpperCAmelCase_ :						Optional[Any]      =      OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

UpperCAmelCase_ :						Any      =      OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

UpperCAmelCase_ :						int      =      OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

UpperCAmelCase_ :						Optional[Any]      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ :						int      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ :						Optional[Any]      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ :						List[Any]      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ :						List[str]      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ :						Tuple      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ :						Optional[Any]      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ :						Dict      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ :						Optional[Any]      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ :						Any      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ :						Union[str, Any]      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ :						Optional[Any]      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ :						int      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ :						Any      =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     int     	=  FLAX_MODEL_MAPPING
UpperCAmelCase_ :						str      =      auto_class_update(FlaxAutoModel)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Any     	=  FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ :						List[str]      =      auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     List[Any]     	=  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ :						Tuple      =      auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Optional[Any]     	=  FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ :						Any      =      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     int     	=  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ :						int      =      auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     str     	=  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ :						Union[str, Any]      =      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     int     	=  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ :						List[Any]      =      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Union[str, Any]     	=  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ :						str      =      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Any     	=  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ :						Optional[Any]      =      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     List[Any]     	=  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ :						int      =      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Optional[Any]     	=  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ :						Optional[int]      =      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Dict     	=  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ :						Union[str, Any]      =      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')

class     _SCREAMING_SNAKE_CASE       (			_BaseAutoModelClass      ):
	snake_case__							:     Union[str, Any]     	=  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ :						Union[str, Any]      =      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)








from collections.abc import Generator
from math import sin

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	if len(__magic_name__					) != 32:
		raise ValueError("""Input must be of length 32"""					)

	UpperCamelCase     :int          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			bytes:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :Any          =	format(__magic_name__	,			"""08x"""					)[-8:]
	UpperCamelCase     :Union[str, Any]          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""					)
	return little_endian_hex

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :str          =	B""""""
	for char in message:
		bit_string += format(__magic_name__	,			"""08b"""					).encode("""utf-8"""					)
	UpperCamelCase     :Any          =	format(len(__magic_name__					)	,			"""064b"""					).encode("""utf-8"""					)

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(__magic_name__					) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]					) + to_little_endian(start_len[:32]					)

	return bit_string

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			Generator[list[int], None, None]:






	"""simple docstring"""



	if len(__magic_name__					) % 512 != 0:
		raise ValueError("""Input must have length that's a multiple of 512"""					)

	for pos in range(0	,			len(__magic_name__					)	,			512					):
		UpperCamelCase     :Tuple          =	bit_string[pos : pos + 512]
		UpperCamelCase     :Optional[int]          =	[]
		for i in range(0	,			512	,			32					):
			block_words.append(int(to_little_endian(block[i : i + 32]					)	,			2					)					)
		yield block_words

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :List[str]          =	format(__magic_name__	,			"""032b"""					)
	UpperCamelCase     :Any          =	""""""
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(__magic_name__	,			2					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	return (a + b) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)
	if shift < 0:
		raise ValueError("""Shift must be non-negative"""					)
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	preprocess(__magic_name__					)

	UpperCamelCase     :List[str]          =	[int(2**32 * abs(sin(i + 1					)					)					) for i in range(64					)]

	# Starting states
	UpperCamelCase     :Union[str, Any]          =	0X67_45_23_01
	UpperCamelCase     :Union[str, Any]          =	0XEF_CD_AB_89
	UpperCamelCase     :List[str]          =	0X98_BA_DC_FE
	UpperCamelCase     :int          =	0X10_32_54_76

	UpperCamelCase     :int          =	[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(__magic_name__					):
		UpperCamelCase     :Optional[Any]          =	aa
		UpperCamelCase     :Any          =	ba
		UpperCamelCase     :Tuple          =	ca
		UpperCamelCase     :List[str]          =	da

		# Hash current chunk
		for i in range(64					):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				UpperCamelCase     :int          =	d ^ (b & (c ^ d))
				UpperCamelCase     :Optional[int]          =	i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				UpperCamelCase     :str          =	c ^ (d & (b ^ c))
				UpperCamelCase     :Union[str, Any]          =	(5 * i + 1) % 16
			elif i <= 47:
				UpperCamelCase     :str          =	b ^ c ^ d
				UpperCamelCase     :Optional[int]          =	(3 * i + 5) % 16
			else:
				UpperCamelCase     :List[str]          =	c ^ (b | not_aa(__magic_name__					))
				UpperCamelCase     :int          =	(7 * i) % 16
			UpperCamelCase     :Dict          =	(f + a + added_consts[i] + block_words[g]) % 2**32
			UpperCamelCase     :Tuple          =	d
			UpperCamelCase     :str          =	c
			UpperCamelCase     :Tuple          =	b
			UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			left_rotate_aa(__magic_name__	,			shift_amounts[i]					)					)

		# Add hashed chunk to running total
		UpperCamelCase     :List[str]          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :str          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :int          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :Optional[Any]          =	reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					)
	return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()
