



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


_snake_case	=						["gpt2"]
_snake_case	=						"gpt2"

if is_tf_available():



						class 						UpperCAmelCase_					( tf.Module):







													def __init__(     self,    __a):

																				'''simple docstring'''

																				super().__init__()
																				_lowerCAmelCase  :							Dict				     =					tokenizer
																				_lowerCAmelCase  :							Union[str, Any]				     =					AutoConfig.from_pretrained(__a)
																				_lowerCAmelCase  :							Union[str, Any]				     =					TFGPTaLMHeadModel.from_config(__a)







													@tf.function(input_signature=(tf.TensorSpec((None,),    tf.string,    name="text"),))
													def      snake_case__   (     self,    __a):

																				'''simple docstring'''

																				_lowerCAmelCase  :							Union[str, Any]				     =					self.tokenizer(__a)
																				_lowerCAmelCase  :							List[Any]				     =					tokenized["input_ids"].to_tensor()

																				_lowerCAmelCase  :							Any				     =					tf.cast(input_ids_dense > 0,    tf.intaa)
																				# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																				_lowerCAmelCase  :							int				     =					self.model(input_ids=__a,    attention_mask=__a)["logits"]

																				return outputs



@require_tf
@require_keras_nlp
class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase  :							List[Any]				     =					[GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)]
														_lowerCAmelCase  :							Any				     =					[TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS]
														assert len(self.tokenizers) == len(self.tf_tokenizers)

														_lowerCAmelCase  :							Optional[Any]				     =					[
														    "This is a straightforward English test sentence.",
														    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
														    "Now we\'re going to add some Chinese: 一 二 三 一二三",
														    "And some much more rare Chinese: 齉 堃 齉堃",
														    "Je vais aussi écrire en français pour tester les accents",
														    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
														]
														_lowerCAmelCase  :							Tuple				     =					list(zip(self.test_sentences,    self.test_sentences[::-1]))







							def      snake_case__   (     self):

														'''simple docstring'''

														for tokenizer, tf_tokenizer in zip(self.tokenizers,    self.tf_tokenizers):
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase  :							Dict				     =					tokenizer([test_inputs],    return_tensors="tf")
																												_lowerCAmelCase  :							Union[str, Any]				     =					tf_tokenizer([test_inputs])

																												for key in python_outputs.keys():
																																			# convert them to numpy to avoid messing with ragged tensors
																																			_lowerCAmelCase  :							str				     =					python_outputs[key].numpy()
																																			_lowerCAmelCase  :							Optional[Any]				     =					tf_outputs[key].numpy()

																																			self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
																																			self.assertTrue(tf.reduce_all(tf.cast(__a,    tf.intaa) == tf_outputs_values))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Tuple				     =					tf.function(__a)
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase  :							Dict				     =					tf.constant(__a)
																												_lowerCAmelCase  :							List[Any]				     =					compiled_tokenizer(__a)
																												_lowerCAmelCase  :							Any				     =					tf_tokenizer(__a)

																												for key in eager_outputs.keys():
																																			self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							List[Any]				     =					ModelToSave(tokenizer=__a)
																					_lowerCAmelCase  :							Optional[int]				     =					tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase  :							Optional[int]				     =					model.serving(__a)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												_lowerCAmelCase  :							Any				     =					Path(__a) / "saved.model"
																												tf.saved_model.save(__a,    __a,    signatures={"serving_default": model.serving})
																												_lowerCAmelCase  :							List[Any]				     =					tf.saved_model.load(__a)
																					_lowerCAmelCase  :							Any				     =					loaded_model.signatures["serving_default"](__a)["output_0"]
																					# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																					self.assertTrue(tf.reduce_all(out == loaded_output))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Any				     =					tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase  :							str				     =					tf_tokenizer(__a)  # Build model with some sample inputs

																					_lowerCAmelCase  :							Tuple				     =					tf_tokenizer.get_config()
																					_lowerCAmelCase  :							Union[str, Any]				     =					TFGPTaTokenizer.from_config(__a)
																					_lowerCAmelCase  :							Any				     =					model_from_config(__a)

																					for key in from_config_output.keys():
																												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					_lowerCAmelCase  :							Tuple				     =					12_3123

																					for max_length in [3, 5, 1024]:
																												_lowerCAmelCase  :							int				     =					tf.convert_to_tensor([self.test_sentences[0]])
																												_lowerCAmelCase  :							Optional[int]				     =					tf_tokenizer(__a,    max_length=__a)

																												_lowerCAmelCase  :							str				     =					out["input_ids"].numpy().shape[1]

																												assert out_length == max_length





import importlib

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					OmegaConf.load(_lowerCamelCase						)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase						)						)						)
							return config






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							if conf_path is None:
														_lowerCAmelCase  :							Union[str, Any]				     =					"./model_checkpoints/vqgan_only.yaml"
							_lowerCAmelCase  :							Tuple				     =					load_config(_lowerCamelCase       ,				display=_lowerCamelCase						)
							_lowerCAmelCase  :							str				     =					VQModel(**config.model.params						)
							if ckpt_path is None:
														_lowerCAmelCase  :							Optional[int]				     =					"./model_checkpoints/vqgan_only.pt"
							_lowerCAmelCase  :							int				     =					torch.load(_lowerCamelCase       ,				map_location=_lowerCamelCase						)
							if ".ckpt" in ckpt_path:
														_lowerCAmelCase  :							List[Any]				     =					sd["state_dict"]
							model.load_state_dict(_lowerCamelCase       ,				strict=_lowerCamelCase						)
							model.to(_lowerCamelCase						)
							del sd
							return model






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					model.encode(_lowerCamelCase						)
							print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"						)
							_lowerCAmelCase  :							int				     =					model.decode(_lowerCamelCase						)
							return xrec






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					string.rsplit("."       ,				1						)
							if reload:
														_lowerCAmelCase  :							Dict				     =					importlib.import_module(_lowerCamelCase						)
														importlib.reload(_lowerCamelCase						)
							return getattr(importlib.import_module(_lowerCamelCase       ,				package=_lowerCamelCase						)       ,				cls						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=True       ,				_lowerCamelCase=True						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					instantiate_from_config(_lowerCamelCase						)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase						)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if ckpt:
														_lowerCAmelCase  :							Optional[int]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)
														_lowerCAmelCase  :							int				     =					pl_sd["global_step"]
														print(F"loaded model from global step {global_step}."						)
							else:
														_lowerCAmelCase  :							Optional[int]				     =					{"state_dict": None}
														_lowerCAmelCase  :							Any				     =					None
							_lowerCAmelCase  :							Optional[int]				     =					load_model_from_config(config.model       ,				pl_sd["state_dict"]       ,				gpu=_lowerCamelCase       ,				eval_mode=_lowerCamelCase						)["model"]
							return model, global_step
