





"""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					(      snake_case							):






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.config_class(**self.inputs_dict     )
              self.parent.assertTrue(hasattr(a__    ,  'hidden_sizes'     )     )
              self.parent.assertTrue(hasattr(a__    ,  'num_attention_heads'     )     )
              self.parent.assertTrue(hasattr(a__    ,  'num_encoder_blocks'     )     )



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=64    ,  a__=3    ,  a__=4    ,  a__=[2, 2, 2, 2]    ,  a__=[8, 4, 2, 1]    ,  a__=[16, 32, 64, 1_28]    ,  a__=[1, 4, 8, 16]    ,  a__=[1, 2, 4, 8]    ,  a__=True    ,  a__=True    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=0.02    ,  a__=3    ,  a__=None    ,  ):
              _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 							__A (   self     ):
              _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 							__A (   self     ):
              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 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     SegformerModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              _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 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.num_labels
              _UpperCAmelCase     =     SegformerForSemanticSegmentation(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(
                  result.logits.shape    ,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)     )
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              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 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     SegformerForSemanticSegmentation(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     torch.randint(0    ,  1    ,  (self.batch_size, self.image_size, self.image_size)     ).to(a__     )
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertGreater(result.loss    ,  0.0     )






       def 							__A (   self     ):
              _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					(      snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (
           (
               SegformerModel,
               SegformerForSemanticSegmentation,
               SegformerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__						 =  (
           {
               """feature-extraction""": SegformerModel,
               """image-classification""": SegformerForImageClassification,
               """image-segmentation""": SegformerForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__						 =  True
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     SegformerModelTester(self     )
              _UpperCAmelCase     =     SegformerConfigTester(self    ,  config_class=a__     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_binary_image_segmentation(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_segmentation(*a__     )






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






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






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(a__     )
                     _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]    ,  a__     )






       def 							__A (   self     ):
              _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(a__     )
                     model.to(a__     )
                     model.eval()
                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**self._prepare_for_class(a__    ,  a__     )     )
                     _UpperCAmelCase     =     outputs.attentions

                     _UpperCAmelCase     =     sum(self.model_tester.depths     )
                     self.assertEqual(len(a__     )    ,  a__     )

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

                     self.assertEqual(len(a__     )    ,  a__     )

                     # 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(a__     )

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

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

                     _UpperCAmelCase     =     outputs.attentions

                     self.assertEqual(len(a__     )    ,  a__     )
                     # 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 							__A (   self     ):
              def check_hidden_states_output(a__    ,  a__    ,  a__     ):
                     _UpperCAmelCase     =     model_class(a__     )
                     model.to(a__     )
                     model.eval()

                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**self._prepare_for_class(a__    ,  a__     )     )

                     _UpperCAmelCase     =     outputs.hidden_states

                     _UpperCAmelCase     =     self.model_tester.num_encoder_blocks
                     self.assertEqual(len(a__     )    ,  a__     )

                     # 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(a__    ,  a__    ,  a__     )

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

                     check_hidden_states_output(a__    ,  a__    ,  a__     )






       def 							__A (   self     ):
              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(a__     ):
                            continue

                     _UpperCAmelCase     =     model_class(a__     )
                     model.to(a__     )
                     model.train()
                     _UpperCAmelCase     =     self._prepare_for_class(a__    ,  a__    ,  return_labels=a__     )
                     _UpperCAmelCase     =     model(**a__     ).loss
                     loss.backward()






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






       @slow
       def 							__A (   self     ):
              for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     SegformerModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )


def       __lowerCamelCase    (       )		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       return image



@require_torch
class 	lowerCAmelCase					(      unittest.TestCase							):






       @slow
       def 							__A (   self     ):
              # only resize + normalize
              _UpperCAmelCase     =     SegformerImageProcessor(
                  image_scale=(5_12, 5_12)    ,  keep_ratio=a__    ,  align=a__    ,  do_random_crop=a__     )
              _UpperCAmelCase     =     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'     ).to(
                  a__     )

              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     encoded_inputs.pixel_values.to(a__     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(a__     )

              _UpperCAmelCase     =     torch.Size((1, model.config.num_labels, 1_28, 1_28)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor(
                  [
                      [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                      [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                      [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
                  ]     ).to(a__     )
              self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,  a__    ,  atol=1E-4     )     )






       @slow
       def 							__A (   self     ):
              # only resize + normalize
              _UpperCAmelCase     =     SegformerImageProcessor(
                  image_scale=(5_12, 5_12)    ,  keep_ratio=a__    ,  align=a__    ,  do_random_crop=a__     )
              _UpperCAmelCase     =     SegformerForSemanticSegmentation.from_pretrained(
                  'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'     ).to(a__     )

              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     encoded_inputs.pixel_values.to(a__     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(a__     )

              _UpperCAmelCase     =     torch.Size((1, model.config.num_labels, 1_28, 1_28)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor(
                  [
                      [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                      [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                      [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
                  ]     ).to(a__     )
              self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,  a__    ,  atol=1E-1     )     )






       @slow
       def 							__A (   self     ):
              # only resize + normalize
              _UpperCAmelCase     =     SegformerImageProcessor(
                  image_scale=(5_12, 5_12)    ,  keep_ratio=a__    ,  align=a__    ,  do_random_crop=a__     )
              _UpperCAmelCase     =     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'     ).to(
                  a__     )

              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     encoded_inputs.pixel_values.to(a__     )

              with torch.no_grad():
                     _UpperCAmelCase     =     model(a__     )

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

              _UpperCAmelCase     =     image_processor.post_process_semantic_segmentation(outputs=a__    ,  target_sizes=[(5_00, 3_00)]     )
              _UpperCAmelCase     =     torch.Size((5_00, 3_00)     )
              self.assertEqual(segmentation[0].shape    ,  a__     )

              _UpperCAmelCase     =     image_processor.post_process_semantic_segmentation(outputs=a__     )
              _UpperCAmelCase     =     torch.Size((1_28, 1_28)     )
              self.assertEqual(segmentation[0].shape    ,  a__     )













"""simple docstring"""






from itertools import product

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       list[int]:






       """simple docstring"""




       _UpperCAmelCase     =     sides_number
       _UpperCAmelCase     =     max_face_number * dice_number
       _UpperCAmelCase     =     [0] * (max_total + 1)

       _UpperCAmelCase     =     1
       _UpperCAmelCase     =     range(SCREAMING_SNAKE_CASE,max_face_number + 1							)
       for dice_numbers in product(SCREAMING_SNAKE_CASE,repeat=SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     sum(SCREAMING_SNAKE_CASE							)
              totals_frequencies[total] += 1

       return totals_frequencies

def       __lowerCamelCase    (       )		->       float:






       """simple docstring"""




       _UpperCAmelCase     =     total_frequency_distribution(
           sides_number=4,dice_number=9							)
       _UpperCAmelCase     =     total_frequency_distribution(
           sides_number=6,dice_number=6							)

       _UpperCAmelCase     =     0
       _UpperCAmelCase     =     9
       _UpperCAmelCase     =     4 * 9
       _UpperCAmelCase     =     6
       for peter_total in range(SCREAMING_SNAKE_CASE,max_peter_total + 1							):
              peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                  colin_totals_frequencies[min_colin_total:peter_total]							)

       _UpperCAmelCase     =     (4**9) * (6**6)
       _UpperCAmelCase     =     peter_wins_count / total_games_number

       _UpperCAmelCase     =     round(SCREAMING_SNAKE_CASE,ndigits=7							)

       return rounded_peter_win_probability


if __name__ == "__main__":
    print(F'''{solution() = }''')






