





"""simple docstring"""
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:






		class 					_UpperCAmelCase   :


							@staticmethod
							def 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  depth_estimator(
												    [
												        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        # RGBA
												        dataset[0]["file"],
												        # LA
												        dataset[1]["file"],
												        # L
												        dataset[2]["file"],
												    ]		)
												self.assertEqual(
												    [
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


					@slow
					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  "Intel/dpt-large"
												A		=  pipeline("depth-estimation"     ,       model=__UpperCamelCase		)
												A		=  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
												A		=  hashimage(outputs["depth"]		)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)     ,       29.304		)
												self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)     ,       2.662		)


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# This is highly irregular to have no small tests.
												self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)









"""simple docstring"""
import math
import sys






def 					A__				(			UpperCamelCase       ):
							A		=  ""
							try:
														with open(UpperCamelCase					,  "rb"       ) as binary_file:
																					A		=  binary_file.read()
														for dat in data:
																					A		=  F"{dat:08b}"
																					result += curr_byte
														return result
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  {"0": "0", "1": "1"}
							A,	A		=  "", ""
							A		=  len(UpperCamelCase       )

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

														A		=  lexicon[curr_string]
														result += last_match_id
														A		=  last_match_id + "0"

														if math.loga(UpperCamelCase       ).is_integer():
																					A		=  {}
																					for curr_key in list(UpperCamelCase       ):
																												A		=  lexicon.pop(UpperCamelCase       )
																					A		=  new_lex

														A		=  last_match_id + "1"
														index += 1
														A		=  ""
							return result






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  8
							try:
														with open(UpperCamelCase					,  "wb"       ) as opened_file:
																					A		=  [
																					    to_write[i : i + byte_length]
																					    for i in range(0					,  len(UpperCamelCase       )					,  UpperCamelCase       )
																					]

																					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[:-1]:
																												opened_file.write(int(UpperCamelCase					,  2       ).to_bytes(1					,  byteorder="big"       )       )
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A		=  data_bits[counter:]
							A		=  data_bits[counter + 1 :]
							return data_bits






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  read_file_binary(UpperCamelCase       )
							A		=  remove_prefix(UpperCamelCase       )
							A		=  decompress_data(UpperCamelCase       )
							write_file_binary(UpperCamelCase					,  UpperCamelCase       )


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


