


'''simple docstring'''






from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
		from transformers import (
		    TFAutoModel,
		    TFAutoModelForCausalLM,
		    TFAutoModelForMaskedLM,
		    TFAutoModelForPreTraining,
		    TFAutoModelForQuestionAnswering,
		    TFAutoModelForSeqaSeqLM,
		    TFAutoModelForSequenceClassification,
		    TFAutoModelForTableQuestionAnswering,
		    TFAutoModelForTokenClassification,
		    TFAutoModelWithLMHead,
		    TFBertForMaskedLM,
		    TFBertForPreTraining,
		    TFBertForQuestionAnswering,
		    TFBertForSequenceClassification,
		    TFBertModel,
		    TFFunnelBaseModel,
		    TFFunnelModel,
		    TFGPTaLMHeadModel,
		    TFRobertaForMaskedLM,
		    TFTaForConditionalGeneration,
		    TFTapasForQuestionAnswering,
		)
		from transformers.models.auto.modeling_tf_auto import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TF_MODEL_MAPPING,
		)
		from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     List[str]					=   "new-model"


if is_tf_available():




		class 	a__       (							a__						):





									'''simple docstring'''






									lowercase__    :     Any					=   NewModelConfig




@require_tf
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			'''bert-base-cased'''
														lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
														self.assertIsNotNone(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			TFAutoModel.from_pretrained(lowerCamelCase_     )
														self.assertIsNotNone(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			'''bert-base-cased'''
														lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
														self.assertIsNotNone(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			TFAutoModelForPreTraining.from_pretrained(lowerCamelCase_     )
														self.assertIsNotNone(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForCausalLM.from_pretrained(lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			TFAutoModelForCausalLM.from_pretrained(lowerCamelCase_      ,					output_loading_info=lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase_      ,					output_loading_info=lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_      ,					output_loading_info=lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														for model_name in ["bert-base-uncased"]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForSequenceClassification.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														for model_name in ["bert-base-uncased"]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForQuestionAnswering.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							@slow
							@require_tensorflow_probability
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																					lowerCAmelCase__      =			AutoConfig.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			TFAutoModelForTableQuestionAnswering.from_pretrained(
																					    lowerCamelCase_      ,					output_loading_info=lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )
														self.assertEqual(model.num_parameters()      ,					1_44_10     )
														self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase_     )      ,					1_44_10     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )
														self.assertEqual(model.num_parameters()      ,					1_44_10     )
														self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase_     )      ,					1_44_10     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
														lowerCAmelCase__      =			TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			copy.deepcopy(model.config     )
														lowerCAmelCase__      =			['''FunnelBaseModel''']
														lowerCAmelCase__      =			TFAutoModel.from_config(lowerCamelCase_     )
														self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

														with tempfile.TemporaryDirectory() as tmp_dir:
																					model.save_pretrained(lowerCamelCase_     )
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained(lowerCamelCase_     )
																					self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														try:
																					AutoConfig.register('''new-model'''      ,					lowerCamelCase_     )

																					lowerCAmelCase__      =			[
																					    TFAutoModel,
																					    TFAutoModelForCausalLM,
																					    TFAutoModelForMaskedLM,
																					    TFAutoModelForPreTraining,
																					    TFAutoModelForQuestionAnswering,
																					    TFAutoModelForSequenceClassification,
																					    TFAutoModelForTokenClassification,
																					]

																					for auto_class in auto_classes:
																												with self.subTest(auto_class.__name__     ):
																																			# Wrong config class will raise an error
																																			with self.assertRaises(lowerCamelCase_     ):
																																										auto_class.register(lowerCamelCase_      ,					lowerCamelCase_     )
																																			auto_class.register(lowerCamelCase_      ,					lowerCamelCase_     )
																																			# Trying to register something existing in the Transformers library will raise an error
																																			with self.assertRaises(lowerCamelCase_     ):
																																										auto_class.register(lowerCamelCase_      ,					lowerCamelCase_     )

																																			# Now that the config is registered, it can be used as any other config with the auto-API
																																			lowerCAmelCase__      =			BertModelTester(self     ).get_config()
																																			lowerCAmelCase__      =			NewModelConfig(**tiny_config.to_dict()     )
																																			lowerCAmelCase__      =			auto_class.from_config(lowerCamelCase_     )
																																			self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

																																			with tempfile.TemporaryDirectory() as tmp_dir:
																																										model.save_pretrained(lowerCamelCase_     )
																																										lowerCAmelCase__      =			auto_class.from_pretrained(lowerCamelCase_     )
																																										self.assertIsInstance(lowerCamelCase_      ,					lowerCamelCase_     )

														finally:
																					if "new-model" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["new-model"]
																					for mapping in (
																					    TF_MODEL_MAPPING,
																					    TF_MODEL_FOR_PRETRAINING_MAPPING,
																					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																					    TF_MODEL_FOR_MASKED_LM_MAPPING,
																					):
																												if NewModelConfig in mapping._extra_content:
																																			del mapping._extra_content[NewModelConfig]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														with self.assertRaisesRegex(
														    lowerCamelCase_      ,					'''bert-base is not a local folder and is not a valid model identifier'''     ):
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained('''bert-base'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														with self.assertRaisesRegex(
														    lowerCamelCase_      ,					r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''     ):
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained(lowerCamelCase_      ,					revision='''aaaaaa'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														with self.assertRaisesRegex(
														    lowerCamelCase_      ,					'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''      ,					):
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														with self.assertRaisesRegex(lowerCamelCase_      ,					'''Use `from_pt=True` to load this model'''     ):
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														# Make sure we have cached the model.
														lowerCAmelCase__      =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''     )
														with RequestCounter() as counter:
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''     )
																					self.assertEqual(counter.get_request_count      ,					0     )
																					self.assertEqual(counter.head_request_count      ,					1     )
																					self.assertEqual(counter.other_request_count      ,					0     )

														# With a sharded checkpoint
														lowerCAmelCase__      =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''     )
														with RequestCounter() as counter:
																					lowerCAmelCase__      =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''     )
																					self.assertEqual(counter.get_request_count      ,					0     )
																					self.assertEqual(counter.head_request_count      ,					1     )
																					self.assertEqual(counter.other_request_count      ,					0     )



'''simple docstring'''






import numpy as np
import qiskit


def       _snake_case     (  A = 8				,					A = None )		->					str:
							lowerCAmelCase__      =			np.random.default_rng(seed=A )

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							lowerCAmelCase__      =			6 * key_len
							# Measurement basis for Alice's qubits.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )
							# The set of states Alice will prepare.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )
							# Measurement basis for Bob's qubits.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )

							# Quantum Circuit to simulate BB84
							lowerCAmelCase__      =			qiskit.QuantumCircuit(A				,					name='''BB84''' )

							# Alice prepares her qubits according to rules above.
							for index, _ in enumerate(A ):
														if alice_state[index] == 1:
																					bbaa_circ.x(A )
														if alice_basis[index] == 1:
																					bbaa_circ.h(A )
							bbaa_circ.barrier()

							# Bob measures the received qubits according to rules above.
							for index, _ in enumerate(A ):
														if bob_basis[index] == 1:
																					bbaa_circ.h(A )

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							lowerCAmelCase__      =			qiskit.Aer.get_backend('''aer_simulator''' )
							# We only need to run one shot because the key is unique.
							# Multiple shots will produce the same key.
							lowerCAmelCase__      =			qiskit.execute(A				,					A				,					shots=1				,					seed_simulator=A )
							# Returns the result of measurement.
							lowerCAmelCase__      =			job.result().get_counts(A ).most_frequent()

							# Extracting the generated key from the simulation results.
							# Only keep measurement results where Alice and Bob chose the same basis.
							lowerCAmelCase__      =			''''''.join(
							    [
							        result_bit
							        for alice_basis_bit, bob_basis_bit, result_bit in zip(
							            A				,					A				,					A )
							        if alice_basis_bit == bob_basis_bit
							    ] )

							# Get final key. Pad with 0 if too short, otherwise truncate.
							lowerCAmelCase__      =			gen_key[:key_len] if len(A ) >= key_len else gen_key.ljust(A				,					'''0''' )
							return key


if __name__ == "__main__":
		print(f"""The generated key is : {bbaa(8, seed=0)}""")
		from doctest import testmod

		testmod()