


'''simple docstring'''
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      lowercase			(							tf.Module      ):

				"""simple docstring"""

				def __init__(	self					,     UpperCamelCase_							):

									'''simple docstring'''


									super().__init__()
									UpperCamelCase__     :Optional[int]							      =				tokenizer
									UpperCamelCase__     :Union[str, Any]							      =				AutoConfig.from_pretrained(UpperCamelCase_							)
									UpperCamelCase__     :Union[str, Any]							      =				TFGPTaLMHeadModel.from_config(UpperCamelCase_							)




				@tf.function(input_signature=(tf.TensorSpec((None,)					,     tf.string					,     name='''text'''							),)							)
				def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

									'''simple docstring'''


									UpperCamelCase__     :Tuple							      =				self.tokenizer(UpperCamelCase_							)
									UpperCamelCase__     :Union[str, Any]							      =				tokenized['''input_ids'''].to_tensor()

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

									UpperCamelCase__     :str							      =				self.model(input_ids=UpperCamelCase_					,     attention_mask=UpperCamelCase_							)['''logits''']

									return outputs
@require_tf
@require_keras_nlp
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().setUp()

							UpperCamelCase__     :Optional[int]							      =				[GPTaTokenizer.from_pretrained(UpperCamelCase_							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
							UpperCamelCase__     :List[Any]							      =				[TFGPTaTokenizer.from_pretrained(UpperCamelCase_							) for checkpoint in TOKENIZER_CHECKPOINTS]
							assert len(self.tokenizers							) == len(self.tf_tokenizers							)

							UpperCamelCase__     :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''',
							]
							UpperCamelCase__     :List[Any]							      =				list(zip(self.test_sentences					,     self.test_sentences[::-1]							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tokenizer, tf_tokenizer in zip(self.tokenizers					,     self.tf_tokenizers							):
												for test_inputs in self.test_sentences:
																	UpperCamelCase__     :int							      =				tokenizer([test_inputs]					,     return_tensors='''tf'''							)
																	UpperCamelCase__     :List[Any]							      =				tf_tokenizer([test_inputs]							)

																	for key in python_outputs.keys():
																						# convert them to numpy to avoid messing with ragged tensors
																						UpperCamelCase__     :Optional[Any]							      =				python_outputs[key].numpy()
																						UpperCamelCase__     :List[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(UpperCamelCase_					,     tf.intaa							) == tf_outputs_values							)							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tf_tokenizer in self.tf_tokenizers:
												UpperCamelCase__     :List[str]							      =				tf.function(UpperCamelCase_							)
												for test_inputs in self.test_sentences:
																	UpperCamelCase__     :int							      =				tf.constant(UpperCamelCase_							)
																	UpperCamelCase__     :str							      =				compiled_tokenizer(UpperCamelCase_							)
																	UpperCamelCase__     :int							      =				tf_tokenizer(UpperCamelCase_							)

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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tf_tokenizer in self.tf_tokenizers:
												UpperCamelCase__     :List[Any]							      =				ModelToSave(tokenizer=UpperCamelCase_							)
												UpperCamelCase__     :Union[str, Any]							      =				tf.convert_to_tensor([self.test_sentences[0]]							)
												UpperCamelCase__     :str							      =				model.serving(UpperCamelCase_							)  # Build model with some sample inputs
												with TemporaryDirectory() as tempdir:
																	UpperCamelCase__     :Tuple							      =				Path(UpperCamelCase_							) / '''saved.model'''
																	tf.saved_model.save(UpperCamelCase_					,     UpperCamelCase_					,     signatures={'''serving_default''': model.serving}							)
																	UpperCamelCase__     :List[Any]							      =				tf.saved_model.load(UpperCamelCase_							)
												UpperCamelCase__     :Any							      =				loaded_model.signatures['''serving_default'''](UpperCamelCase_							)['''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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tf_tokenizer in self.tf_tokenizers:
												UpperCamelCase__     :Union[str, Any]							      =				tf.convert_to_tensor([self.test_sentences[0]]							)
												UpperCamelCase__     :Tuple							      =				tf_tokenizer(UpperCamelCase_							)  # Build model with some sample inputs

												UpperCamelCase__     :int							      =				tf_tokenizer.get_config()
												UpperCamelCase__     :Dict							      =				TFGPTaTokenizer.from_config(UpperCamelCase_							)
												UpperCamelCase__     :List[str]							      =				model_from_config(UpperCamelCase_							)

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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tf_tokenizer in self.tf_tokenizers:
												# for the test to run
												UpperCamelCase__     :Dict							      =				123123

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

																	UpperCamelCase__     :Tuple							      =				out['''input_ids'''].numpy().shape[1]

																	assert out_length == max_length




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps