





from collections import OrderedDict

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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Tuple       =       FLAX_MODEL_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModel)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			List[str]       =       FLAX_MODEL_FOR_PRETRAINING_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[Any]       =       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			List[Any]       =       FLAX_MODEL_FOR_MASKED_LM_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[Any]       =       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Union[str, Any]       =       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			int       =       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[Any]       =       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			List[str]       =       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			str       =       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[int]       =       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[Any]       =       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class SCREAMING_SNAKE_CASE_						(					_BaseAutoModelClass					):
						__magic_name__:			Optional[int]       =       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_SCREAMING_SNAKE_CASE      	=		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)








from __future__ import annotations


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a = None   , __a = None   , __a = False   , ):
	snake_case_		:			int											=	cipher_alphabet or [chr(__a   ) for i in range(97   , 1_23   )]

	# If the argument is None or the user provided an empty dictionary
	if not frequencies_dict:
		# Frequencies of letters in the english language (how much they show up)
		snake_case_		:			Dict											=	{
		    'a': 0.08497,
		    'b': 0.01492,
		    'c': 0.02202,
		    'd': 0.04253,
		    'e': 0.11162,
		    'f': 0.02228,
		    'g': 0.02015,
		    'h': 0.06094,
		    'i': 0.07546,
		    'j': 0.00153,
		    'k': 0.01292,
		    'l': 0.04025,
		    'm': 0.02406,
		    'n': 0.06749,
		    'o': 0.07507,
		    'p': 0.01929,
		    'q': 0.00095,
		    'r': 0.07587,
		    's': 0.06327,
		    't': 0.09356,
		    'u': 0.02758,
		    'v': 0.00978,
		    'w': 0.02560,
		    'x': 0.00150,
		    'y': 0.01994,
		    'z': 0.00077,
		}
	else:
		# Custom frequencies dictionary
		snake_case_		:			int											=	frequencies_dict

	if not case_sensitive:
		snake_case_		:			Optional[Any]											=	ciphertext.lower()

	# Chi squared statistic values
	snake_case_		:			dict[int, tuple[float, str]]											=	{}

	# cycle through all of the shifts
	for shift in range(len(__a   )   ):
		snake_case_		:			int											=	''

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				snake_case_		:			List[Any]											=	(alphabet_letters.index(letter.lower()   ) - shift) % len(
				    __a   )
				decrypted_with_shift += (
				    alphabet_letters[new_key].upper()
				    if case_sensitive and letter.isupper()
				    else alphabet_letters[new_key]
				)
			except ValueError:
				# Append the character if it isn't in the alphabet
				decrypted_with_shift += letter

		snake_case_		:			Union[str, Any]											=	0.0

		# Loop through each letter in the decoded message with the shift
		for letter in decrypted_with_shift:
			if case_sensitive:
				snake_case_		:			List[Any]											=	letter.lower()
				if letter in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Optional[Any]											=	decrypted_with_shift.lower().count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			Tuple											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[int]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value
			else:
				if letter.lower() in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Any											=	decrypted_with_shift.count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			List[str]											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[Any]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
		snake_case_		:			Any											=	(
		    chi_squared_statistic,
		    decrypted_with_shift,
		)

	# Get the most likely cipher by finding the cipher with the smallest chi squared
	# statistic
	def chi_squared_statistic_values_sorting_key(__a   ) -> tuple[float, str]:
		return chi_squared_statistic_values[key]

	snake_case_		:			int											=	min(
	    __a   , key=__a   , )

	# Get all the data from the most likely cipher (key, decoded message)
	(
	    (
	    snake_case_
	)						,(
	    snake_case_
	)						,
	)		:			str											=	chi_squared_statistic_values[most_likely_cipher]

	# Return the data on the most likely shift
	return (
	    most_likely_cipher,
	    most_likely_cipher_chi_squared_value,
	    decoded_most_likely_cipher,
	)

