
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				lowercase__				:				List[Any]						=      False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' ,		)
@require_vision
@require_torch
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      tempfile.mkdtemp()

													__UpperCamelCase  	=      PixaStructImageProcessor()
													__UpperCamelCase  	=      TaTokenizer.from_pretrained('''t5-small'''      )

													__UpperCamelCase  	=      PixaStructProcessor(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													processor.save_pretrained(self.tmpdirname      )



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													return AutoProcessor.from_pretrained(self.tmpdirname  ,						**SCREAMING_SNAKE_CASE_      ).tokenizer



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													return AutoProcessor.from_pretrained(self.tmpdirname  ,						**SCREAMING_SNAKE_CASE_      ).image_processor



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta      )]

													__UpperCamelCase  	=      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_  ,						0  ,						-1      )      ) for x in image_inputs]

													return image_inputs



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()      )
													processor.save_pretrained(self.tmpdirname      )

													__UpperCamelCase  	=      self.get_tokenizer(bos_token='''(BOS)'''  ,						eos_token='''(EOS)'''      )
													__UpperCamelCase  	=      self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_  ,						padding_value=1.0      )

													__UpperCamelCase  	=      PixaStructProcessor.from_pretrained(
													    self.tmpdirname  ,						bos_token='''(BOS)'''  ,						eos_token='''(EOS)'''  ,						do_normalize=SCREAMING_SNAKE_CASE_  ,						padding_value=1.0      )

													self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab()      )
													self.assertIsInstance(processor.tokenizer  ,						SCREAMING_SNAKE_CASE_      )

													self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor_add_kwargs.to_json_string()      )
													self.assertIsInstance(processor.image_processor  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.prepare_image_inputs()

													__UpperCamelCase  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors='''np'''      )
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''np'''      )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''lower newer'''

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      tokenizer(SCREAMING_SNAKE_CASE_  ,						return_token_type_ids=SCREAMING_SNAKE_CASE_  ,						add_special_tokens=SCREAMING_SNAKE_CASE_      )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''lower newer'''
													__UpperCamelCase  	=      self.prepare_image_inputs()

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_      )

													self.assertListEqual(
													    list(inputs.keys()      )  ,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']      )

													# test if it raises when no input is passed
													with pytest.raises(SCREAMING_SNAKE_CASE_      ):
																			processor()



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''lower newer'''
													__UpperCamelCase  	=      self.prepare_image_inputs()

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      [512, 1024, 2048, 4096]
													__UpperCamelCase  	=      [770, 770, 770, 770]
													# with text
													for i, max_patch in enumerate(SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_  ,						max_patches=SCREAMING_SNAKE_CASE_      )
																			self.assertEqual(inputs['''flattened_patches'''][0].shape[0]  ,						SCREAMING_SNAKE_CASE_      )
																			self.assertEqual(inputs['''flattened_patches'''][0].shape[1]  ,						expected_hidden_size[i]      )

													# without text input
													for i, max_patch in enumerate(SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						max_patches=SCREAMING_SNAKE_CASE_      )
																			self.assertEqual(inputs['''flattened_patches'''][0].shape[0]  ,						SCREAMING_SNAKE_CASE_      )
																			self.assertEqual(inputs['''flattened_patches'''][0].shape[1]  ,						expected_hidden_size[i]      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__UpperCamelCase  	=      processor.batch_decode(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

													self.assertListEqual(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''lower newer'''
													__UpperCamelCase  	=      self.prepare_image_inputs()

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_      )

													# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
													self.assertListEqual(
													    list(inputs.keys()      )  ,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']      )

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_      )

													# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
													self.assertListEqual(list(inputs.keys()      )  ,						['''input_ids''', '''attention_mask''']      )










"""simple docstring"""



import numpy as np
import qiskit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1		)

 # First, let's deal with the case in which oracle is balanced
 if case == "balanced":
  # First generate a random number that tells us which CNOTs to
  # wrap in X-gates:
  A__			=							np.random.randint(1     ,					2**num_qubits		)
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  A__			=							format(lowercase_     ,					f"""0{num_qubits}b"""		)
  # Next, we place the first X-gates. Each digit in our binary string
  # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
  # we apply an X-gate to that qubit:
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(lowercase_		):
   oracle_qc.cx(lowercase_     ,					lowercase_		)
  # Next, place the final X-gates
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)

    # Case in which oracle is constant
 if case == "constant":
  # First decide what the fixed output of the oracle will be
  # (either always 0 or always 1)
  A__			=							np.random.randint(2		)
  if output == 1:
   oracle_qc.x(lowercase_		)

 A__			=							oracle_qc.to_gate()
 A__			=							"Oracle"  # To show when we display the circuit
 return oracle_gate







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1     ,					lowercase_		)
 # Set up the output qubit:
 dj_circuit.x(lowercase_		)
 dj_circuit.h(lowercase_		)
 # And set up the input register:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)
 # Let's append the oracle gate to our circuit:
 dj_circuit.append(lowercase_     ,					range(num_qubits + 1		)		)
 # Finally, perform the H-gates again and measure:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)

 for i in range(lowercase_		):
  dj_circuit.measure(lowercase_     ,					lowercase_		)

 return dj_circuit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.result.counts.Counts:
 A__			=							qiskit.Aer.get_backend("aer_simulator"		)

 A__			=							dj_oracle(lowercase_     ,					lowercase_		)
 A__			=							dj_algorithm(lowercase_     ,					lowercase_		)

 # Execute the circuit on the simulator
 A__			=							qiskit.execute(lowercase_     ,					lowercase_     ,					shots=10_00		)

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


if __name__ == "__main__":
      print(f'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
      print(f'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')





