






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A_       :Tuple       =						'''src/transformers'''
A_       :str       =						'''docs/source/en/tasks'''






def 							A     (       a_							,a_							,a_					)     ->						Optional[int]:
					with open(a_							,'r'							,encoding='utf-8'							,newline='\n'					) as f:
										__UpperCamelCase      :					Optional[int]       		=f.readlines()
					# Find the start prompt.
					__UpperCamelCase      :					Optional[Any]       		=0
					while not lines[start_index].startswith(a_					):
										start_index += 1
					start_index += 1

					__UpperCamelCase      :					Any       		=start_index
					while not lines[end_index].startswith(a_					):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]					) <= 1:
										start_index += 1
					while len(lines[end_index]					) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]					), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A_       :int       =						direct_transformers_import(TRANSFORMERS_PATH)

A_       :Union[str, Any]       =						{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A_       :Optional[Any]       =						{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def 							A     (       a_					)     ->						Tuple:
					__UpperCamelCase      :					int       		=TASK_GUIDE_TO_MODELS[task_guide]
					__UpperCamelCase      :					Dict       		=SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_							,set()					)
					__UpperCamelCase      :					List[Any]       		={
					    code: name
					    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
					    if (code in model_maping_names or code in special_model_types)
					}
					return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]					) + "\n"






def 							A     (       a_							,a_=False					)     ->						Optional[int]:
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=_find_text_in_file(
					    filename=os.path.join(a_							,a_					)							,start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,end_prompt='<!--End of the generated tip-->'							,)

					__UpperCamelCase      :					Optional[Any]       		=get_model_list_for_task(a_					)

					if current_list != new_list:
										if overwrite:
															with open(os.path.join(a_							,a_					)							,'w'							,encoding='utf-8'							,newline='\n'					) as f:
																				f.writelines(lines[:start_index] + [new_list] + lines[end_index:]					)
										else:
															raise ValueError(
															    F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
															    ' to fix this.'					)


if __name__ == "__main__":
							A_       :List[Any]       =						argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							A_       :int       =						parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)







import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def __UpperCamelCase			(       _A = 3  ):
						if isinstance(_A ,  _A  ):
												raise TypeError('''number of qubits must be a integer.'''  )
						if number_of_qubits <= 0:
												raise ValueError('''number of qubits must be > 0.'''  )
						if math.floor(_A  ) != number_of_qubits:
												raise ValueError('''number of qubits must be exact integer.'''  )
						if number_of_qubits > 10:
												raise ValueError('''number of qubits too large to simulate(>10).'''  )

						lowerCAmelCase_      			=  QuantumRegister(_A ,  '''qr'''  )
						lowerCAmelCase_      			=  ClassicalRegister(_A ,  '''cr'''  )

						lowerCAmelCase_      			=  QuantumCircuit(_A ,  _A  )

						lowerCAmelCase_      			=  number_of_qubits

						for i in range(_A  ):
												quantum_circuit.h(number_of_qubits - i - 1  )
												counter -= 1
												for j in range(_A  ):
																		quantum_circuit.cp(np.pi / 2 ** (counter - j) ,  _A ,  _A  )

						for k in range(number_of_qubits // 2  ):
												quantum_circuit.swap(_A ,  number_of_qubits - k - 1  )

						# measure all the qubits
						quantum_circuit.measure(_A ,  _A  )
						# simulate with 10000 shots
						lowerCAmelCase_      			=  Aer.get_backend('''qasm_simulator'''  )
						lowerCAmelCase_      			=  execute(_A ,  _A ,  shots=10000  )

						return job.result().get_counts(_A  )


if __name__ == "__main__":
						print(
						    f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
						)




