



'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, TensorType

a							   =				logging.get_logger(__name__)

a							   =				{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Dict        =				"""imagegpt"""
							UpperCAmelCase			:		Optional[int]        =				["""past_key_values"""]
							UpperCAmelCase			:		Tuple        =				{
							    """hidden_size""": """n_embd""",
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(				self   :						Optional[Any]      ,						a_   :						int=5_1_2 + 1      ,						a_   :						Tuple=3_2 * 3_2      ,						a_   :						int=5_1_2      ,						a_   :						Dict=2_4      ,						a_   :						List[str]=8      ,						a_   :						Dict=None      ,						a_   :						Optional[Any]="quick_gelu"      ,						a_   :						List[Any]=0.1      ,						a_   :						Dict=0.1      ,						a_   :						Any=0.1      ,						a_   :						Dict=1E-5      ,						a_   :						List[str]=0.0_2      ,						a_   :						str=True      ,						a_   :						Optional[Any]=True      ,						a_   :						int=False      ,						a_   :						Union[str, Any]=False      ,						a_   :						List[str]=False      ,						**a_   :						int      ,						)			->					int:
										snake_case:    int      							=vocab_size
										snake_case:    List[str]      							=n_positions
										snake_case:    List[str]      							=n_embd
										snake_case:    Union[str, Any]      							=n_layer
										snake_case:    Any      							=n_head
										snake_case:    str      							=n_inner
										snake_case:    Union[str, Any]      							=activation_function
										snake_case:    Union[str, Any]      							=resid_pdrop
										snake_case:    List[Any]      							=embd_pdrop
										snake_case:    Optional[int]      							=attn_pdrop
										snake_case:    Optional[Any]      							=layer_norm_epsilon
										snake_case:    str      							=initializer_range
										snake_case:    Tuple      							=scale_attn_weights
										snake_case:    List[Any]      							=use_cache
										snake_case:    Any      							=scale_attn_by_inverse_layer_idx
										snake_case:    List[Any]      							=reorder_and_upcast_attn
										snake_case:    Union[str, Any]      							=tie_word_embeddings

										super().__init__(tie_word_embeddings=a_      ,						**a_			)






class 						a_		(     snake_case    ):


							@property
							def    UpperCamelCase     (				self   :						Any			)			->					Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										    ]			)




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						"FeatureExtractionMixin"      ,						a_   :						int = 1      ,						a_   :						int = -1      ,						a_   :						bool = False      ,						a_   :						Optional["TensorType"] = None      ,						a_   :						int = 3      ,						a_   :						int = 3_2      ,						a_   :						int = 3_2      ,						)			->					Mapping[str, Any]:
										snake_case:    int      							=self._generate_dummy_images(a_      ,						a_      ,						a_      ,						a_			)
										snake_case:    Dict      							=dict(preprocessor(images=a_      ,						return_tensors=a_			)			)

										return inputs









'''simple docstring'''

import qiskit







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					qiskit.result.counts.Counts:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=qiskit.Aer.get_backend('aer_simulator'					)

			# Create a Quantum Circuit acting on the q register
			snake_case:    Dict      							=qiskit.QuantumCircuit(__UpperCAmelCase				,							__UpperCAmelCase					)

			# Map the quantum measurement to the classical bits
			circuit.measure([0]				,							[0]					)

			# Execute the circuit on the simulator
			snake_case:    Dict      							=qiskit.execute(__UpperCAmelCase				,							__UpperCAmelCase				,							shots=10_00					)

			# Return the histogram data of the results of the experiment.
			return job.result().get_counts(__UpperCAmelCase					)


if __name__ == "__main__":
	print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")




