





"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							PriorTransformer
					UpperCamelCase      =							'''hidden_states'''


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												A		=  4
												A		=  8
												A		=  7

												A		=  floats_tensor((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  floats_tensor((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:List[Any]=0		):
												torch.manual_seed(__UpperCamelCase		)
												A		=  4
												A		=  8
												A		=  7

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return (4, 8)


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return (4, 8)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  {
												    "num_attention_heads": 2,
												    "attention_head_dim": 4,
												    "num_layers": 2,
												    "embedding_dim": 8,
												    "num_embeddings": 7,
												    "additional_embeddings": 4,
												}
												A		=  self.dummy_input
												return init_dict, inputs_dict


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A,	A		=  PriorTransformer.from_pretrained(
												    "hf-internal-testing/prior-dummy"     ,       output_loading_info=__UpperCamelCase		)
												self.assertIsNotNone(__UpperCamelCase		)
												self.assertEqual(len(loading_info["missing_keys"]		)     ,       0		)

												model.to(__UpperCamelCase		)
												A		=  model(**self.dummy_input		)[0]

												assert hidden_states is not None, "Make sure output is not None"


					def 	lowerCamelCase       (					self			:str		):
												A,	A		=  self.prepare_init_args_and_inputs_for_common()

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

												A		=  ["hidden_states", "timestep"]
												self.assertListEqual(arg_names[:2]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"		)
												A		=  model.to(__UpperCamelCase		)

												if hasattr(__UpperCamelCase     ,       "set_default_attn_processor"		):
																			model.set_default_attn_processor()

												A		=  self.get_dummy_seed_input()

												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)[0]

												A		=  output[0, :5].flatten().cpu()
												print(__UpperCamelCase		)

												# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
												# the expected output slices are not the same for CPU and GPU.
												A		=  torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]		)
												self.assertTrue(torch_all_close(__UpperCamelCase     ,       __UpperCamelCase     ,       rtol=1e-2		)		)







@slow
class 					_UpperCAmelCase   (	unittest.TestCase ):


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:int=1     ,       __UpperCamelCase			:Tuple=7_68     ,       __UpperCamelCase			:List[Any]=77     ,       __UpperCamelCase			:str=0		):
												torch.manual_seed(__UpperCamelCase		)
												A		=  batch_size
												A		=  embedding_dim
												A		=  num_embeddings

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
					        [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
					        # fmt: on
					    ]		)
					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:List[Any]		):
												A		=  PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"     ,       subfolder="prior"		)
												model.to(__UpperCamelCase		)
												A		=  self.get_dummy_seed_input(seed=__UpperCamelCase		)

												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)[0]

												assert list(sample.shape		) == [1, 7_68]

												A		=  sample[0, :8].flatten().cpu()
												print(__UpperCamelCase		)
												A		=  torch.tensor(__UpperCamelCase		)

												assert torch_all_close(__UpperCamelCase     ,       __UpperCamelCase     ,       atol=1e-3		)









"""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])


