






from typing import TYPE_CHECKING

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


__magic_name__:	Optional[int]							=      {
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:	List[str]							=      ["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:	Optional[int]							=      ["CLIPFeatureExtractor"]
		__magic_name__:	Dict							=      ["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:	int							=      [
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:	str							=      [
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:	Optional[Any]							=      [
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		__magic_name__:	Union[str, Any]							=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    List[str]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count += 1
			A_		:    Optional[int]						     = """_"""
	if count > 1:
		return False
	else:
		return "".join(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = []
	while True:
		A_		:    Any						     = ["""$"""] * len(snake_case__       )
		A_		:    int						     = []
		for i in range(len(snake_case__       )       ):
			for j in range(i + 1 , len(snake_case__       )       ):
				A_		:    int						     = compare_string(binary[i] , binary[j]       )
				if k is False:
					A_		:    Optional[int]						     = """*"""
					A_		:    str						     = """*"""
					temp.append("""X"""       )
		for i in range(len(snake_case__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(snake_case__       ) == 0:
			return pi
		A_		:    Tuple						     = list(set(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	for minterm in minterms:
		A_		:    Any						     = """"""
		for _ in range(snake_case__       ):
			A_		:    int						     = str(minterm % 2       ) + string
			minterm //= 2
		temp.append(snake_case__       )
	return temp






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    str						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	A_		:    Tuple						     = [0] * len(snake_case__       )
	for i in range(len(chart[0]       )       ):
		A_		:    Optional[int]						     = 0
		A_		:    List[str]						     = -1
		for j in range(len(snake_case__       )       ):
			if chart[j][i] == 1:
				count += 1
				A_		:    Union[str, Any]						     = j
		if count == 1:
			A_		:    str						     = 1
	for i in range(len(snake_case__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(snake_case__       )       ):
						A_		:    Optional[int]						     = 0
			temp.append(prime_implicants[i]       )
	while True:
		A_		:    List[str]						     = 0
		A_		:    List[str]						     = -1
		A_		:    Union[str, Any]						     = 0
		for i in range(len(snake_case__       )       ):
			A_		:    List[str]						     = chart[i].count(1       )
			if count_n > max_n:
				A_		:    Union[str, Any]						     = count_n
				A_		:    Optional[int]						     = i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]       )

		for i in range(len(chart[0]       )       ):
			if chart[rem][i] == 1:
				for j in range(len(snake_case__       )       ):
					A_		:    Optional[Any]						     = 0






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = [[0 for x in range(len(snake_case__       )       )] for x in range(len(snake_case__       )       )]
	for i in range(len(snake_case__       )       ):
		A_		:    List[str]						     = prime_implicants[i].count("""_"""       )
		for j in range(len(snake_case__       )       ):
			if is_for_table(prime_implicants[i] , binary[j] , snake_case__       ):
				A_		:    Union[str, Any]						     = 1

	return chart






def       __UpperCamelCase						(		):
	A_		:    Union[str, Any]						     = int(input("""Enter the no. of variables\n"""       )       )
	A_		:    List[Any]						     = [
	    float(snake_case__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	A_		:    List[str]						     = decimal_to_binary(snake_case__ , snake_case__       )

	A_		:    str						     = check(snake_case__       )
	print("""Prime Implicants are:"""       )
	print(snake_case__       )
	A_		:    List[str]						     = prime_implicant_chart(snake_case__ , snake_case__       )

	A_		:    int						     = selection(snake_case__ , snake_case__       )
	print("""Essential Prime Implicants are:"""       )
	print(snake_case__       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

