
"""simple docstring"""

import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_MAPPING,
          SegformerForImageClassification,
          SegformerForSemanticSegmentation,
          SegformerModel,
      )
      from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import SegformerImageProcessor

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.config_class(**self.inputs_dict   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'hidden_sizes'   )   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'num_attention_heads'   )   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'num_encoder_blocks'   )   )


class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=64			,  __UpperCAmelCase=3			,  __UpperCAmelCase=4			,  __UpperCAmelCase=[2, 2, 2, 2]			,  __UpperCAmelCase=[8, 4, 2, 1]			,  __UpperCAmelCase=[16, 32, 64, 128]			,  __UpperCAmelCase=[1, 4, 8, 16]			,  __UpperCAmelCase=[1, 2, 4, 8]			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= num_encoder_blocks
      __UpperCamelCase       						= sr_ratios
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= downsampling_rates
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= num_labels
      __UpperCamelCase       						= scope





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      __UpperCamelCase       						= None
      if self.use_labels:
       __UpperCamelCase       						= ids_tensor([self.batch_size, self.image_size, self.image_size]			,  self.num_labels   )

      __UpperCamelCase       						= self.get_config()
      return config, pixel_values, labels





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return SegformerConfig(
          image_size=self.image_size			,  num_channels=self.num_channels			,  num_encoder_blocks=self.num_encoder_blocks			,  depths=self.depths			,  hidden_sizes=self.hidden_sizes			,  num_attention_heads=self.num_attention_heads			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  initializer_range=self.initializer_range			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerModel(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      __UpperCamelCase       						= __UpperCamelCase       						= self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape			,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= SegformerForSemanticSegmentation(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape			,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape			,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
      self.parent.assertGreater(result.loss			,  0.0   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= 1
      __UpperCamelCase       						= SegformerForSemanticSegmentation(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= torch.randint(0			,  1			,  (self.batch_size, self.image_size, self.image_size)   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertGreater(result.loss			,  0.0   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.prepare_config_and_inputs()
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= config_and_inputs
      __UpperCamelCase       						= {'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (
             SegformerModel,
             SegformerForSemanticSegmentation,
             SegformerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     lowercase		     =		(
         {
             "feature-extraction": SegformerModel,
             "image-classification": SegformerForImageClassification,
             "image-segmentation": SegformerForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowercase		     =		True
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerModelTester(self   )
      __UpperCamelCase       						= SegformerConfigTester(self			,  config_class=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase   )





     @unittest.skip('SegFormer does not use inputs_embeds'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       __UpperCamelCase       						= inspect.signature(model.forward   )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __UpperCamelCase       						= [*signature.parameters.keys()]

       __UpperCamelCase       						= ['pixel_values']
       self.assertListEqual(arg_names[:1]			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= True

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= True
       __UpperCamelCase       						= False
       __UpperCamelCase       						= True
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()
       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )
       __UpperCamelCase       						= outputs.attentions

       __UpperCamelCase       						= sum(self.model_tester.depths   )
       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __UpperCamelCase       						= True
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()
       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )
       __UpperCamelCase       						= outputs.attentions

       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

       # verify the first attentions (first block, first layer)
       __UpperCamelCase       						= (self.model_tester.image_size // 4) ** 2
       __UpperCamelCase       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]   )			,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,  )

       # verify the last attentions (last block, last layer)
       __UpperCamelCase       						= (self.model_tester.image_size // 32) ** 2
       __UpperCamelCase       						= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]   )			,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,  )
       __UpperCamelCase       						= len(__UpperCAmelCase   )

       # Check attention is always last and order is fine
       __UpperCamelCase       						= True
       __UpperCamelCase       						= True
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()
       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )

       self.assertEqual(out_len + 1			,  len(__UpperCAmelCase   )   )

       __UpperCamelCase       						= outputs.attentions

       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )
       # verify the first attentions (first block, first layer)
       __UpperCamelCase       						= (self.model_tester.image_size // 4) ** 2
       __UpperCamelCase       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]   )			,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()

       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )

       __UpperCamelCase       						= outputs.hidden_states

       __UpperCamelCase       						= self.model_tester.num_encoder_blocks
       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]   )			,  [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]			,  )

      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= True
       check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __UpperCamelCase       						= True

       check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if not self.model_tester.is_training:
       return

      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= True

      for model_class in self.all_model_classes:
       if model_class in get_values(__UpperCAmelCase   ):
        continue

       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.train()
       __UpperCamelCase       						= self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase			,  return_labels=__UpperCAmelCase   )
       __UpperCamelCase       						= model(**__UpperCAmelCase   ).loss
       loss.backward()





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase       						= SegformerModel.from_pretrained(__UpperCAmelCase   )
       self.assertIsNotNone(__UpperCAmelCase   )






def 							A		(       )   ->			Tuple:
 __UpperCamelCase       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image





@require_torch
class __lowerCAmelCase	(   unittest.TestCase    ):





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
          __UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Size((1, model.config.num_labels, 128, 128)   )
      self.assertEqual(outputs.logits.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= torch.tensor(
          [
              [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
              [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
              [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
          ]   ).to(__UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,  __UpperCAmelCase			,  atol=1E-4   )   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained(
          'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'   ).to(__UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Size((1, model.config.num_labels, 128, 128)   )
      self.assertEqual(outputs.logits.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= torch.tensor(
          [
              [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
              [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
              [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
          ]   ).to(__UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,  __UpperCAmelCase			,  atol=1E-1   )   )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
          __UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= outputs.logits.detach().cpu()

      __UpperCamelCase       						= image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase			,  target_sizes=[(500, 300)]   )
      __UpperCamelCase       						= torch.Size((500, 300)   )
      self.assertEqual(segmentation[0].shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase   )
      __UpperCamelCase       						= torch.Size((128, 128)   )
      self.assertEqual(segmentation[0].shape			,  __UpperCAmelCase   )





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

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

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= 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.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

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

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


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

      testmod()



