

'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.openai.modeling_tf_openai import (
           TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFOpenAIGPTDoubleHeadsModel,
           TFOpenAIGPTForSequenceClassification,
           TFOpenAIGPTLMHeadModel,
           TFOpenAIGPTModel,
       )


class 					lowerCAmelCase       :







       def __init__(							self     :	Dict  , __lowercase     :	str  , ):







            """simple docstring"""







            __lowercase     						=parent
            __lowercase     						=13
            __lowercase     						=7
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=99
            __lowercase     						=32
            __lowercase     						=2
            __lowercase     						=4
            __lowercase     						=37
            __lowercase     						='gelu'
            __lowercase     						=0.1
            __lowercase     						=0.1
            __lowercase     						=512
            __lowercase     						=16
            __lowercase     						=2
            __lowercase     						=0.0_2
            __lowercase     						=3
            __lowercase     						=4
            __lowercase     						=None
            __lowercase     						=self.vocab_size - 1







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size )

            __lowercase     						=None
            if self.use_input_mask:
                 __lowercase     						=random_attention_mask([self.batch_size, self.seq_length] )

            __lowercase     						=None
            if self.use_token_type_ids:
                 __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.type_vocab_size )

            __lowercase     						=None
            if self.use_mc_token_ids:
                 __lowercase     						=ids_tensor([self.batch_size, self.num_choices]  , self.seq_length )

            __lowercase     						=None
            __lowercase     						=None
            __lowercase     						=None
            if self.use_labels:
                 __lowercase     						=ids_tensor([self.batch_size]  , self.type_sequence_label_size )
                 __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.num_labels )
                 __lowercase     						=ids_tensor([self.batch_size]  , self.num_choices )

            __lowercase     						=OpenAIGPTConfig(
                vocab_size=self.vocab_size  , n_embd=self.hidden_size  , n_layer=self.num_hidden_layers  , n_head=self.num_attention_heads  , n_positions=self.max_position_embeddings  , pad_token_id=self.pad_token_id  , )

            __lowercase     						=ids_tensor([self.num_hidden_layers, self.num_attention_heads]  , 2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
                token_type_ids,
                mc_token_ids,
                sequence_labels,
                token_labels,
                choice_labels,
            )







       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Optional[int]  , __lowercase     :	Optional[Any]  , __lowercase     :	Optional[Any]  , __lowercase     :	Optional[int]  , __lowercase     :	Dict  , *__lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=TFOpenAIGPTModel(config=__lowercase )
            __lowercase     						={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __lowercase     						=model(__lowercase )

            __lowercase     						=[input_ids, input_mask]
            __lowercase     						=model(__lowercase )

            __lowercase     						=model(__lowercase )

            self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size) )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Tuple  , __lowercase     :	str  , __lowercase     :	Dict  , __lowercase     :	Optional[Any]  , __lowercase     :	Union[str, Any]  , *__lowercase     :	Dict ):







            """simple docstring"""







            __lowercase     						=TFOpenAIGPTLMHeadModel(config=__lowercase )
            __lowercase     						={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __lowercase     						=model(__lowercase )
            self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.vocab_size) )







       def      snake_case				(							self     :	int  , __lowercase     :	Dict  , __lowercase     :	List[Any]  , __lowercase     :	List[Any]  , __lowercase     :	List[str]  , __lowercase     :	List[Any]  , __lowercase     :	List[Any]  , *__lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=TFOpenAIGPTDoubleHeadsModel(config=__lowercase )

            __lowercase     						=tf.tile(tf.expand_dims(__lowercase  , 1 )  , (1, self.num_choices, 1) )
            __lowercase     						=tf.tile(tf.expand_dims(__lowercase  , 1 )  , (1, self.num_choices, 1) )
            __lowercase     						=tf.tile(tf.expand_dims(__lowercase  , 1 )  , (1, self.num_choices, 1) )

            __lowercase     						={
                'input_ids': multiple_choice_inputs_ids,
                'mc_token_ids': mc_token_ids,
                'attention_mask': multiple_choice_input_mask,
                'token_type_ids': multiple_choice_token_type_ids,
            }
            __lowercase     						=model(__lowercase )
            self.parent.assertEqual(
                result.logits.shape  , (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) )
            self.parent.assertEqual(result.mc_logits.shape  , (self.batch_size, self.num_choices) )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	List[Any]  , __lowercase     :	int  , __lowercase     :	List[str]  , __lowercase     :	Tuple  , __lowercase     :	int  , *__lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.num_labels
            __lowercase     						=ids_tensor([self.batch_size]  , self.type_sequence_label_size )
            __lowercase     						={
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
                'labels': sequence_labels,
            }
            __lowercase     						=TFOpenAIGPTForSequenceClassification(__lowercase )
            __lowercase     						=model(__lowercase )
            self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_labels) )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.prepare_config_and_inputs()

            (
                (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,       (
                __lowercase       
            )						,              
            )     						=config_and_inputs

            __lowercase     						={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict






@require_tf
class 					lowerCAmelCase       ( A				,     A				,     unittest.TestCase				):
       lowerCAmelCase_				=	(
           (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
           if is_tf_available()
           else ()
       )
       lowerCAmelCase_				=	(
           (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       lowerCAmelCase_				=	(
           {
               "feature-extraction": TFOpenAIGPTModel,
               "text-classification": TFOpenAIGPTForSequenceClassification,
               "text-generation": TFOpenAIGPTLMHeadModel,
               "zero-shot": TFOpenAIGPTForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase_				=	False
       lowerCAmelCase_				=	False







       def      snake_case				(							self     :	str  , __lowercase     :	List[str]  , __lowercase     :	Union[str, Any]  , __lowercase     :	List[Any]  , __lowercase     :	List[str]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                 # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                 # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                 # tiny config could not be created.
                 return True

            return False







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=TFOpenAIGPTModelTester(self )
            __lowercase     						=ConfigTester(self  , config_class=__lowercase  , n_embd=37 )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_model(*__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_lm_head(*__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_double_head(*__lowercase )







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowercase )







       @slow
       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase     						=TFOpenAIGPTModel.from_pretrained(__lowercase )
                 self.assertIsNotNone(__lowercase )






@require_tf
class 					lowerCAmelCase       ( unittest.TestCase				):







       @slow
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
            __lowercase     						=tf.convert_to_tensor([[481, 4735, 544]]  , dtype=tf.intaa )  # the president is
            __lowercase     						=[
                481,
                4735,
                544,
                246,
                963,
                870,
                762,
                239,
                244,
                40477,
                244,
                249,
                719,
                881,
                487,
                544,
                240,
                244,
                603,
                481,
            ]  # the president is a very good man. " \n " i\'m sure he is, " said the

            __lowercase     						=model.generate(__lowercase  , do_sample=__lowercase )
            self.assertListEqual(output_ids[0].numpy().tolist()  , __lowercase )





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


