






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     				=			{'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			['''T5Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			['''T5TokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''T5EncoderModel''',
	    '''T5ForConditionalGeneration''',
	    '''T5Model''',
	    '''T5PreTrainedModel''',
	    '''load_tf_weights_in_t5''',
	    '''T5ForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFT5EncoderModel''',
	    '''TFT5ForConditionalGeneration''',
	    '''TFT5Model''',
	    '''TFT5PreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''FlaxT5EncoderModel''',
	    '''FlaxT5ForConditionalGeneration''',
	    '''FlaxT5Model''',
	    '''FlaxT5PreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_ta import TaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_ta_fast import TaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ta import (
		    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TaEncoderModel,
		    TaForConditionalGeneration,
		    TaForQuestionAnswering,
		    TaModel,
		    TaPreTrainedModel,
		    load_tf_weights_in_ta,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_ta import (
		    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTaEncoderModel,
		    TFTaForConditionalGeneration,
		    TFTaModel,
		    TFTaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_ta import (
		    FlaxTaEncoderModel,
		    FlaxTaForConditionalGeneration,
		    FlaxTaModel,
		    FlaxTaPreTrainedModel,
		)


else:
	import sys

	UpperCAmelCase     				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

