'''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       UpperCAmelCase__  (       _snake_case			):
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[Any]     				=					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       UpperCAmelCase__  :
   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, 128]       ,				_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       ,				)    ->     List[Any]:
        lowercase_					:       Dict     				=					parent
        lowercase_					:       int     				=					batch_size
        lowercase_					:       Any     				=					image_size
        lowercase_					:       List[str]     				=					num_channels
        lowercase_					:       str     				=					num_encoder_blocks
        lowercase_					:       Tuple     				=					sr_ratios
        lowercase_					:       Tuple     				=					depths
        lowercase_					:       Dict     				=					hidden_sizes
        lowercase_					:       Optional[int]     				=					downsampling_rates
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[Any]     				=					is_training
        lowercase_					:       Dict     				=					use_labels
        lowercase_					:       Optional[Any]     				=					hidden_act
        lowercase_					:       Any     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					num_labels
        lowercase_					:       str     				=					scope
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Union[str, Any]     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       str     				=					None
        if self.use_labels:
             lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.image_size, self.image_size]       ,				self.num_labels				)

        lowercase_					:       Tuple     				=					self.get_config()
        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Dict:
        lowercase_					:       Union[str, Any]     				=					SegformerModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       int     				=					model(_a				)
        lowercase_					:       Any     				=					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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					self.num_labels
        lowercase_					:       Union[str, Any]     				=					SegformerForSemanticSegmentation(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Tuple     				=					model(_a				)
        self.parent.assertEqual(
            result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
        lowercase_					:       str     				=					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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     int:
        lowercase_					:       Dict     				=					1
        lowercase_					:       Dict     				=					SegformerForSemanticSegmentation(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       List[str]     				=					torch.randint(0       ,				1       ,				(self.batch_size, self.image_size, self.image_size)				).to(_a				)
        lowercase_					:       Any     				=					model(_a       ,				labels=_a				)
        self.parent.assertGreater(result.loss       ,				0.0				)





   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Any     				=					self.prepare_config_and_inputs()
        lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       int     				=					{'pixel_values': pixel_values}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):
   A							:       int     =    (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   A							:       Tuple     =    (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   A							:       str     =    True
   A							:       int     =    False
   A							:       int     =    False
   A							:       Optional[Any]     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[str]     				=					SegformerModelTester(self				)
        lowercase_					:       str     				=					SegformerConfigTester(self       ,				config_class=_a				)
   def 		_lowerCamelCase (self				)    ->     str:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Any     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*_a				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					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 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_					:       Dict     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_					:       Optional[Any]     				=					True

        for model_class in self.all_model_classes:
             lowercase_					:       Any     				=					True
             lowercase_					:       int     				=					False
             lowercase_					:       int     				=					True
             lowercase_					:       Tuple     				=					model_class(_a				)
             model.to(_a				)
             model.eval()
             with torch.no_grad():
                  lowercase_					:       int     				=					model(**self._prepare_for_class(_a       ,				_a				)				)
             lowercase_					:       Union[str, Any]     				=					outputs.attentions

             lowercase_					:       Tuple     				=					sum(self.model_tester.depths				)
             self.assertEqual(len(_a				)       ,				_a				)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             lowercase_					:       List[str]     				=					True
             lowercase_					:       Dict     				=					model_class(_a				)
             model.to(_a				)
             model.eval()
             with torch.no_grad():
                  lowercase_					:       Optional[Any]     				=					model(**self._prepare_for_class(_a       ,				_a				)				)
             lowercase_					:       List[Any]     				=					outputs.attentions

             self.assertEqual(len(_a				)       ,				_a				)

             # verify the first attentions (first block, first layer)
             lowercase_					:       Any     				=					(self.model_tester.image_size // 4) ** 2
             lowercase_					:       Dict     				=					(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)
             lowercase_					:       Optional[int]     				=					(self.model_tester.image_size // 32) ** 2
             lowercase_					:       Optional[int]     				=					(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]       ,				)
             lowercase_					:       Optional[Any]     				=					len(_a				)

             # Check attention is always last and order is fine
             lowercase_					:       List[Any]     				=					True
             lowercase_					:       Union[str, Any]     				=					True
             lowercase_					:       Optional[int]     				=					model_class(_a				)
             model.to(_a				)
             model.eval()
             with torch.no_grad():
                  lowercase_					:       Any     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

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

             lowercase_					:       List[Any]     				=					outputs.attentions

             self.assertEqual(len(_a				)       ,				_a				)
             # verify the first attentions (first block, first layer)
             lowercase_					:       Any     				=					(self.model_tester.image_size // 4) ** 2
             lowercase_					:       List[str]     				=					(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 		_lowerCamelCase (self				)    ->     List[str]:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       Dict     				=					model_class(_a				)
             model.to(_a				)
             model.eval()

             with torch.no_grad():
                  lowercase_					:       List[Any]     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

             lowercase_					:       Any     				=					outputs.hidden_states

             lowercase_					:       str     				=					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,
                 ]       ,				)

        lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       str     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_					:       Optional[Any]     				=					True

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        if not self.model_tester.is_training:
             return

        lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_					:       List[Any]     				=					True

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

             lowercase_					:       Optional[int]     				=					model_class(_a				)
             model.to(_a				)
             model.train()
             lowercase_					:       List[Any]     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       str     				=					model(**_a				).loss
             loss.backward()
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
   def 		_lowerCamelCase (self				)    ->     str:
        pass





   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[Any]     				=					SegformerModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






@require_torch
class       UpperCAmelCase__  (       unittest.TestCase			):
   @slow
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # only resize + normalize
        lowercase_					:       Union[str, Any]     				=					SegformerImageProcessor(
            image_scale=(512, 512)       ,				keep_ratio=_a       ,				align=_a       ,				do_random_crop=_a				)
        lowercase_					:       List[str]     				=					SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'				).to(
            _a				)

        lowercase_					:       int     				=					prepare_img()
        lowercase_					:       int     				=					image_processor(images=_a       ,				return_tensors='pt'				)
        lowercase_					:       int     				=					encoded_inputs.pixel_values.to(_a				)

        with torch.no_grad():
             lowercase_					:       List[Any]     				=					model(_a				)

        lowercase_					:       Union[str, Any]     				=					torch.Size((1, model.config.num_labels, 128, 128)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       int     				=					torch.tensor(
            [
                [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
            ]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,				_a       ,				atol=1e-4				)				)
   @slow
   def 		_lowerCamelCase (self				)    ->     Dict:
        # only resize + normalize
        lowercase_					:       Optional[Any]     				=					SegformerImageProcessor(
            image_scale=(512, 512)       ,				keep_ratio=_a       ,				align=_a       ,				do_random_crop=_a				)
        lowercase_					:       Union[str, Any]     				=					SegformerForSemanticSegmentation.from_pretrained(
            'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'				).to(_a				)

        lowercase_					:       Optional[Any]     				=					prepare_img()
        lowercase_					:       Tuple     				=					image_processor(images=_a       ,				return_tensors='pt'				)
        lowercase_					:       Any     				=					encoded_inputs.pixel_values.to(_a				)

        with torch.no_grad():
             lowercase_					:       Union[str, Any]     				=					model(_a				)

        lowercase_					:       str     				=					torch.Size((1, model.config.num_labels, 128, 128)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       List[Any]     				=					torch.tensor(
            [
                [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
            ]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,				_a       ,				atol=1e-1				)				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        # only resize + normalize
        lowercase_					:       List[Any]     				=					SegformerImageProcessor(
            image_scale=(512, 512)       ,				keep_ratio=_a       ,				align=_a       ,				do_random_crop=_a				)
        lowercase_					:       List[Any]     				=					SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'				).to(
            _a				)

        lowercase_					:       List[str]     				=					prepare_img()
        lowercase_					:       List[str]     				=					image_processor(images=_a       ,				return_tensors='pt'				)
        lowercase_					:       Optional[Any]     				=					encoded_inputs.pixel_values.to(_a				)

        with torch.no_grad():
             lowercase_					:       Dict     				=					model(_a				)

        lowercase_					:       Optional[int]     				=					outputs.logits.detach().cpu()

        lowercase_					:       Tuple     				=					image_processor.post_process_semantic_segmentation(outputs=_a       ,				target_sizes=[(500, 300)]				)
        lowercase_					:       Any     				=					torch.Size((500, 300)				)
        self.assertEqual(segmentation[0].shape       ,				_a				)

        lowercase_					:       List[str]     				=					image_processor.post_process_semantic_segmentation(outputs=_a				)
        lowercase_					:       Optional[int]     				=					torch.Size((128, 128)				)
        self.assertEqual(segmentation[0].shape       ,				_a				)





'''simple docstring'''




import math
import os
import sys

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					''
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as binary_file:
               lowercase_					:       Dict     				=					binary_file.read()
          for dat in data:
               lowercase_					:       Union[str, Any]     				=					f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lexicon.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					last_match_id

     if math.loga(SCREAMING_SNAKE_CASE_							).is_integer():
          for curr_key in lexicon:
               lowercase_					:       Tuple     				=					'0' + lexicon[curr_key]

     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					{'0': '0', '1': '1'}
     lowercase_				,lowercase_					:       Any     				=					'', ''
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     for i in range(len(SCREAMING_SNAKE_CASE_							)							):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          lowercase_					:       str     				=					lexicon[curr_string]
          result += last_match_id
          add_key_to_lexicon(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          index += 1
          lowercase_					:       List[Any]     				=					''

     while curr_string != "" and curr_string not in lexicon:
          curr_string += "0"

     if curr_string != "":
          lowercase_					:       Optional[Any]     				=					lexicon[curr_string]
          result += last_match_id

     return result

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					os.path.getsize(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]
     lowercase_					:       Optional[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     return "0" * (length_length - 1) + file_length_binary + compressed

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					8
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'wb'							) as opened_file:
               lowercase_					:       List[Any]     				=					[
                   to_write[i : i + byte_length]
                   for i in range(0  ,       len(SCREAMING_SNAKE_CASE_							)  ,       SCREAMING_SNAKE_CASE_							)
               ]

               if len(result_byte_array[-1]							) % byte_length == 0:
                    result_byte_array.append('10000000'							)
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1]							) - 1
                    )

               for elem in result_byte_array:
                    opened_file.write(int(SCREAMING_SNAKE_CASE_  ,       2							).to_bytes(1  ,       byteorder='big'							)							)
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					read_file_binary(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					compress_data(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					add_file_length(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     write_file_binary(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])




