
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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, GLPNConfig, GLPNForDepthEstimation, GLPNModel
							from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import GLPNImageProcessor




class      __lowercase	(	__snake_case					):






	def 			_a(self							:   Optional[Any]						)   -> List[Any]:
							_lowercase					:  List[str]    			=      self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(snake_case			,  "hidden_sizes"						)						)
							self.parent.assertTrue(hasattr(snake_case			,  "num_attention_heads"						)						)
							self.parent.assertTrue(hasattr(snake_case			,  "num_encoder_blocks"						)						)




class      __lowercase	:






	def __init__(self							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Union[str, Any]=13			,  snake_case							:   Tuple=64			,  snake_case							:   Optional[Any]=3			,  snake_case							:   Tuple=4			,  snake_case							:   Dict=[2, 2, 2, 2]			,  snake_case							:   Union[str, Any]=[8, 4, 2, 1]			,  snake_case							:   Dict=[16, 32, 64, 128]			,  snake_case							:   Optional[int]=[1, 4, 8, 16]			,  snake_case							:   Any=[1, 2, 4, 8]			,  snake_case							:   Optional[Any]=True			,  snake_case							:   str=True			,  snake_case							:   Any="gelu"			,  snake_case							:   int=0.1			,  snake_case							:   List[str]=0.1			,  snake_case							:   str=0.02			,  snake_case							:   List[str]=16			,  snake_case							:   int=3			,  snake_case							:   List[str]=None			,  )   -> Optional[Any]:
							_lowercase					:  List[Any]    			=      parent
							_lowercase					:  Any    			=      batch_size
							_lowercase					:  str    			=      image_size
							_lowercase					:  Any    			=      num_channels
							_lowercase					:  List[Any]    			=      num_encoder_blocks
							_lowercase					:  Any    			=      sr_ratios
							_lowercase					:  List[Any]    			=      depths
							_lowercase					:  Optional[int]    			=      hidden_sizes
							_lowercase					:  Optional[Any]    			=      downsampling_rates
							_lowercase					:  int    			=      num_attention_heads
							_lowercase					:  List[Any]    			=      is_training
							_lowercase					:  List[str]    			=      use_labels
							_lowercase					:  List[str]    			=      hidden_act
							_lowercase					:  Union[str, Any]    			=      hidden_dropout_prob
							_lowercase					:  str    			=      attention_probs_dropout_prob
							_lowercase					:  Dict    			=      initializer_range
							_lowercase					:  Dict    			=      decoder_hidden_size
							_lowercase					:  List[Any]    			=      num_labels
							_lowercase					:  str    			=      scope






	def 			_a(self							:   List[str]						)   -> Optional[int]:
							_lowercase					:  Tuple    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							_lowercase					:  List[Any]    			=      None
							if self.use_labels:
													_lowercase					:  Optional[int]    			=      ids_tensor([self.batch_size, self.image_size, self.image_size]			,  self.num_labels						)

							_lowercase					:  Optional[int]    			=      self.get_config()
							return config, pixel_values, labels






	def 			_a(self							:   Dict						)   -> Union[str, Any]:
							return GLPNConfig(
							    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			,  decoder_hidden_size=self.decoder_hidden_size			,  )






	def 			_a(self							:   Optional[Any]			,  snake_case							:   List[str]			,  snake_case							:   int			,  snake_case							:   List[Any]						)   -> Union[str, Any]:
							_lowercase					:  Any    			=      GLPNModel(config=snake_case						)
							model.to(snake_case						)
							model.eval()
							_lowercase					:  List[str]    			=      model(snake_case						)
							_lowercase					:  List[str]    			=      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							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   int			,  snake_case							:   List[Any]						)   -> Optional[int]:
							_lowercase					:  Any    			=      self.num_labels
							_lowercase					:  Dict    			=      GLPNForDepthEstimation(snake_case						)
							model.to(snake_case						)
							model.eval()
							_lowercase					:  Union[str, Any]    			=      model(snake_case						)
							self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)						)
							_lowercase					:  Tuple    			=      model(snake_case			,  labels=snake_case						)
							self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)						)






	def 			_a(self							:   Optional[Any]						)   -> Optional[int]:
							_lowercase					:  str    			=      self.prepare_config_and_inputs()
							_lowercase			,						_lowercase			,						_lowercase					:  List[Any]    			=      config_and_inputs
							_lowercase					:  Tuple    			=      {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
	_A   			=      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
	_A   			=      (
	    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
	)

	_A   			=      False
	_A   			=      False
	_A   			=      False






	def 			_a(self							:   Tuple						)   -> Dict:
							_lowercase					:  List[str]    			=      GLPNModelTester(self						)
							_lowercase					:  Dict    			=      GLPNConfigTester(self			,  config_class=snake_case						)






	def 			_a(self							:   Optional[Any]						)   -> List[Any]:
							self.config_tester.run_common_tests()






	def 			_a(self							:   Optional[int]						)   -> Any:
							_lowercase					:  List[Any]    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case						)






	def 			_a(self							:   Any						)   -> Optional[int]:
							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*snake_case						)






	@unittest.skip("GLPN does not use inputs_embeds"						)
	def 			_a(self							:   int						)   -> Optional[int]:
							pass






	@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"						)
	def 			_a(self							:   Dict						)   -> int:
							pass






	def 			_a(self							:   int						)   -> Union[str, Any]:
							_lowercase			,						_lowercase					:  Optional[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_lowercase					:  int    			=      model_class(snake_case						)
													_lowercase					:  List[str]    			=      inspect.signature(model.forward						)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_lowercase					:  Union[str, Any]    			=      [*signature.parameters.keys()]

													_lowercase					:  List[str]    			=      ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,  snake_case						)






	def 			_a(self							:   Union[str, Any]						)   -> Optional[int]:
							_lowercase			,						_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
							_lowercase					:  str    			=      True

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

													_lowercase					:  Optional[Any]    			=      sum(self.model_tester.depths						)
													self.assertEqual(len(snake_case						)			,  snake_case						)

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

													self.assertEqual(len(snake_case						)			,  snake_case						)

													# verify the first attentions (first block, first layer)
													_lowercase					:  Optional[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(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					:  Dict    			=      (self.model_tester.image_size // 32) ** 2
													_lowercase					:  Optional[Any]    			=      (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					:  Tuple    			=      len(snake_case						)

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

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

													_lowercase					:  Tuple    			=      outputs.attentions

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

													with torch.no_grad():
																			_lowercase					:  Tuple    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)

													_lowercase					:  List[Any]    			=      outputs.hidden_states

													_lowercase					:  int    			=      self.model_tester.num_encoder_blocks
													self.assertEqual(len(snake_case						)			,  snake_case						)

													# 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			,						_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_lowercase					:  List[str]    			=      True
													check_hidden_states_output(snake_case			,  snake_case			,  snake_case						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_lowercase					:  str    			=      True

													check_hidden_states_output(snake_case			,  snake_case			,  snake_case						)






	def 			_a(self							:   List[Any]						)   -> Any:
							if not self.model_tester.is_training:
													return

							_lowercase			,						_lowercase					:  Union[str, 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(snake_case						):
																			continue
													# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
													# this can then be incorporated into _prepare_for_class in test_modeling_common.py
													if model_class.__name__ == "GLPNForDepthEstimation":
																			_lowercase			,						_lowercase			,						_lowercase			,						_lowercase					:  Any    			=      inputs_dict["pixel_values"].shape
																			_lowercase					:  Optional[Any]    			=      torch.zeros(
																			    [self.model_tester.batch_size, height, width]			,  device=snake_case						).long()
													_lowercase					:  int    			=      model_class(snake_case						)
													model.to(snake_case						)
													model.train()
													_lowercase					:  Tuple    			=      self._prepare_for_class(snake_case			,  snake_case			,  return_labels=snake_case						)
													_lowercase					:  int    			=      model(**snake_case						).loss
													loss.backward()






	@slow
	def 			_a(self							:   Union[str, Any]						)   -> Tuple:
							for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase					:  int    			=      GLPNModel.from_pretrained(snake_case						)
													self.assertIsNotNone(snake_case						)



def 		UpperCamelCase    (					)      -> Any:






						'''simple docstring'''

						_lowercase					:  Any    			=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
						return image




@require_torch
@require_vision
@slow
class      __lowercase	(	unittest.TestCase					):






	@slow
	def 			_a(self							:   Any						)   -> Optional[Any]:
							_lowercase					:  Tuple    			=      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
							_lowercase					:  List[Any]    			=      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)

							_lowercase					:  Dict    			=      prepare_img()
							_lowercase					:  List[str]    			=      image_processor(images=snake_case			,  return_tensors="pt"						).to(snake_case						)

							# forward pass
							with torch.no_grad():
													_lowercase					:  Dict    			=      model(**snake_case						)

							# verify the predicted depth
							_lowercase					:  Tuple    			=      torch.Size([1, 480, 640]						)
							self.assertEqual(outputs.predicted_depth.shape			,  snake_case						)

							_lowercase					:  Tuple    			=      torch.tensor(
							    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						).to(snake_case						)

							self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,  snake_case			,  atol=1e-4						)						)






from dataclasses import dataclass

UpperCamelCase_	:    List[str]  					=       """0.1"""
UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""




@dataclass
class      __lowercase	:
	_A   			=      42
	_A   			=      42
	_A   			=      42






	def __repr__(self							:   List[str]						)   -> str:
							return F"""({self.offset}, {self.length}, {self.indicator})"""




class      __lowercase	:






	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
							_lowercase					:  int    			=      window_size
							_lowercase					:  List[Any]    			=      lookahead_buffer_size
							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size






	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
							_lowercase					:  List[Any]    			=      []
							_lowercase					:  str    			=      ""

							# while there are still characters in text to compress
							while text:
													# find the next encoding phrase
													# - triplet with offset, length, indicator (the next encoding character)
													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  snake_case						)

													# update the search buffer:
													# - add new characters from text into it
													# - check if size exceed the max search buffer size, if so, drop the
													#   oldest elements
													search_buffer += text[: token.length + 1]
													if len(snake_case						) > self.search_buffer_size:
																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]

													# update the text
													_lowercase					:  str    			=      text[token.length + 1 :]

													# append the token to output
													output.append(snake_case						)

							return output






	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
							_lowercase					:  Optional[int]    			=      ""

							for token in tokens:
													for _ in range(token.length						):
																			output += output[-token.offset]
													output += token.indicator

							return output






	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:

							if not text:
													raise ValueError("We need some text to work with."						)

							# Initialise result parameters to default values
							_lowercase			,						_lowercase					:  Tuple    			=      0, 0

							if not search_buffer:
													return Token(snake_case			,  snake_case			,  text[length]						)

							for i, character in enumerate(snake_case						):
													_lowercase					:  Tuple    			=      len(snake_case						) - i
													if character == text[0]:
																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  snake_case						)
																			# if the found length is bigger than the current or if it's equal,
																			# which means it's offset is smaller: update offset and length
																			if found_length >= length:
																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length

							return Token(snake_case			,  snake_case			,  text[length]						)






	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
							if not text or text[text_index] != window[window_index]:
													return 0
							return 1 + self._match_length_from_index(
							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							# Initialize compressor class
							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)

							# Example
							UpperCamelCase_	:    Optional[int]  					=       """cabracadabrarrarrad"""
							UpperCamelCase_	:    Tuple  					=       lzaa_compressor.compress(TEXT)
							print(lzaa_compressor.compress("""ababcbababaa"""))
							UpperCamelCase_	:    Union[str, Any]  					=       lzaa_compressor.decompress(compressed_text)
							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."




