





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    LongformerTokenizer
					A__				 =    True
					A__				 =    LongformerTokenizerFast
					A__				 =    True





					def 					__magic_name__ ( self	):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase__       =       [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										lowerCamelCase__       =       dict(zip(_lowerCAmelCase	,  range(len(_lowerCAmelCase	)	)	)	)
										lowerCamelCase__       =       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										lowerCamelCase__       =       {"unk_token": "<unk>"}

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["merges_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)
										with open(self.merges_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write("\n".join(_lowerCAmelCase	)	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       "lower newer"
										return input_text, output_text





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tokenizer_class(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map	)
										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										lowerCamelCase__       =       tokenizer.tokenize(_lowerCAmelCase	)  # , add_prefix_space=True)
										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       tokens + [tokenizer.unk_token]
										lowerCamelCase__       =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase	)	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("Hello world!"	,  add_special_tokens=_lowerCAmelCase	)	,  [0, 3_1414, 232, 328, 2]	)
										self.assertListEqual(
										    tokenizer.encode("Hello world! cécé herlolip 418"	,  add_special_tokens=_lowerCAmelCase	)	,  [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]	,  )





					@slow
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"	)

										lowerCamelCase__       =       tokenizer.encode("sequence builders"	,  add_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.encode("multi-sequence build"	,  add_special_tokens=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.encode(
										    "sequence builders"	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.encode(
										    "sequence builders"	,  "multi-sequence build"	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	,  _lowerCAmelCase	)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       "Encode this sequence."
										lowerCamelCase__       =       tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

										# Testing encoder arguments
										lowerCamelCase__       =       tokenizer.encode(_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
										self.assertNotEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.encode(_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
										self.assertEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

										tokenizer.add_special_tokens({"bos_token": "<s>"}	)
										lowerCamelCase__       =       tokenizer.encode(_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
										self.assertNotEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

										# Testing spaces after special tokens
										lowerCamelCase__       =       "<mask>"
										tokenizer.add_special_tokens(
										    {"mask_token": AddedToken(_lowerCAmelCase	,  lstrip=_lowerCAmelCase	,  rstrip=_lowerCAmelCase	)}	)  # mask token has a left space
										lowerCamelCase__       =       tokenizer.convert_tokens_to_ids(_lowerCAmelCase	)

										lowerCamelCase__       =       "Encode <mask> sequence"
										lowerCamelCase__       =       "Encode <mask>sequence"

										lowerCamelCase__       =       tokenizer.encode(_lowerCAmelCase	)
										lowerCamelCase__       =       encoded.index(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
										self.assertEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.encode(_lowerCAmelCase	)
										lowerCamelCase__       =       encoded.index(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
										self.assertNotEqual(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										pass





					def 					__magic_name__ ( self	):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,  **_lowerCAmelCase	)
																				lowerCamelCase__       =       self.tokenizer_class.from_pretrained(_lowerCAmelCase	,  **_lowerCAmelCase	)
																				lowerCamelCase__       =       "A, <mask> AllenNLP sentence."
																				lowerCamelCase__       =       tokenizer_r.encode_plus(_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	,  return_token_type_ids=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_p.encode_plus(_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	,  return_token_type_ids=_lowerCAmelCase	)

																				# token_type_ids should put 0 everywhere
																				self.assertEqual(sum(tokens_r["token_type_ids"]	)	,  sum(tokens_p["token_type_ids"]	)	)

																				# attention_mask should put 1 everywhere, so sum over length should be 1
																				self.assertEqual(
																				    sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)	,  sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)	,  )

																				lowerCamelCase__       =       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																				lowerCamelCase__       =       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

																				# Rust correctly handles the space before the mask while python doesnt
																				self.assertSequenceEqual(tokens_p["input_ids"]	,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)
																				self.assertSequenceEqual(tokens_r["input_ids"]	,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)

																				self.assertSequenceEqual(
																				    _lowerCAmelCase	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
																				self.assertSequenceEqual(
																				    _lowerCAmelCase	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





					def 					__magic_name__ ( self	):
										for trim_offsets, add_prefix_space in itertools.product([True, False]	,  repeat=2	):
															lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)

															lowerCamelCase__       =       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
															lowerCamelCase__       =       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

															self.assertEqual(pre_tokenizer_state["add_prefix_space"]	,  _lowerCAmelCase	)

															self.assertEqual(post_processor_state["add_prefix_space"]	,  _lowerCAmelCase	)
															self.assertEqual(post_processor_state["trim_offsets"]	,  _lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
										# `trim_offsets`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																				lowerCamelCase__       =       "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																				lowerCamelCase__       =       F"{text_of_1_token} {text_of_1_token}"

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (len(_lowerCAmelCase	) + 1, len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (len(_lowerCAmelCase	) + 1, len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (len(_lowerCAmelCase	), len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (len(_lowerCAmelCase	), len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       F" {text}"

																				# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																				#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																				# )
																				# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																				# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																				# self.assertEqual(
																				#     encoding.offset_mapping[1],
																				#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																				# )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (1, 1 + len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (1 + len(_lowerCAmelCase	) + 1, 1 + len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, 1 + len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (1 + len(_lowerCAmelCase	), 1 + len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )

																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase	,  use_fast=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	,  trim_offsets=_lowerCAmelCase	)
																				lowerCamelCase__       =       tokenizer_r(_lowerCAmelCase	,  return_offsets_mapping=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
																				self.assertEqual(encoding.offset_mapping[0]	,  (0, 1 + len(_lowerCAmelCase	))	)
																				self.assertEqual(
																				    encoding.offset_mapping[1]	,  (1 + len(_lowerCAmelCase	), 1 + len(_lowerCAmelCase	) + 1 + len(_lowerCAmelCase	))	,  )








import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





def    __UpperCamelCase      (    a) ->int:
					if "target" not in config:
										raise KeyError("Expected key `target` to instantiate.")
					return get_obj_from_str(config["target"])(**config.get("params",     {}))





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

