



import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      AudioLDMPipeline
     _A      =      TEXT_TO_AUDIO_PARAMS
     _A      =      TEXT_TO_AUDIO_BATCH_PARAMS
     _A      =      frozenset(
         [
             'num_inference_steps',
             'num_waveforms_per_prompt',
             'generator',
             'latents',
             'output_type',
             'return_dict',
             'callback',
             'callback_steps',
         ])







     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		UNetaDConditionModel(
               block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=4					,      out_channels=4					,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,      cross_attention_dim=(32, 64)					,      class_embed_type="simple_projection"					,      projection_class_embeddings_input_dim=32					,      class_embeddings_concat=__a					,      )
           lowerCamelCase             =		DDIMScheduler(
               beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule="scaled_linear"					,      clip_sample=__a					,      set_alpha_to_one=__a					,      )
           torch.manual_seed(0     )
           lowerCamelCase             =		AutoencoderKL(
               block_out_channels=[32, 64]					,      in_channels=1					,      out_channels=1					,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,      latent_channels=4					,      )
           torch.manual_seed(0     )
           lowerCamelCase             =		ClapTextConfig(
               bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1E-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=10_00					,      projection_dim=32					,      )
           lowerCamelCase             =		ClapTextModelWithProjection(__a     )
           lowerCamelCase             =		RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"					,      model_max_length=77     )

           lowerCamelCase             =		SpeechTaHifiGanConfig(
               model_in_dim=8					,      sampling_rate=1_60_00					,      upsample_initial_channel=16					,      upsample_rates=[2, 2]					,      upsample_kernel_sizes=[4, 4]					,      resblock_kernel_sizes=[3, 7]					,      resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]					,      normalize_before=__a					,      )

           lowerCamelCase             =		SpeechTaHifiGan(__a     )

           lowerCamelCase             =		{
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "vocoder": vocoder,
           }
           return components







     def     _a   (self					,      __a					,      __a=0     ):






           '''simple docstring'''



           if str(__a     ).startswith("mps"     ):
                 lowerCamelCase             =		torch.manual_seed(__a     )
           else:
                 lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		{
               "prompt": "A hammer hitting a wooden surface",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		np.array(
               [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]     )

           assert np.abs(audio_slice - expected_slice     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

           lowerCamelCase             =		audioldm_pipe.tokenizer(
               __a					,      padding="max_length"					,      max_length=audioldm_pipe.tokenizer.model_max_length					,      truncation=__a					,      return_tensors="pt"					,      )
           lowerCamelCase             =		text_inputs["input_ids"].to(__a     )

           lowerCamelCase             =		audioldm_pipe.text_encoder(
               __a					,      )
           lowerCamelCase             =		prompt_embeds.text_embeds
           # additional L_2 normalization over each hidden-state
           lowerCamelCase             =		F.normalize(__a					,      dim=-1     )

           lowerCamelCase             =		prompt_embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert np.abs(audio_a - audio_a     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * ["this is a negative prompt"]
           lowerCamelCase             =		negative_prompt
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

           lowerCamelCase             =		[]
           for p in [prompt, negative_prompt]:
                 lowerCamelCase             =		audioldm_pipe.tokenizer(
                     __a					,      padding="max_length"					,      max_length=audioldm_pipe.tokenizer.model_max_length					,      truncation=__a					,      return_tensors="pt"					,      )
                 lowerCamelCase             =		text_inputs["input_ids"].to(__a     )

                 lowerCamelCase             =		audioldm_pipe.text_encoder(
                     __a					,      )
                 lowerCamelCase             =		text_embeds.text_embeds
                 # additional L_2 normalization over each hidden-state
                 lowerCamelCase             =		F.normalize(__a					,      dim=-1     )

                 embeds.append(__a     )

           lowerCamelCase      ,				lowerCamelCase             =		embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert np.abs(audio_a - audio_a     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		"egg cracking"
           lowerCamelCase             =		audioldm_pipe(**__a					,      negative_prompt=__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		np.array(
               [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]     )

           assert np.abs(audio_slice - expected_slice     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A hammer hitting a wooden surface"

           # test num_waveforms_per_prompt=1 (default)
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2     ).audios

           assert audios.shape == (1, 2_56)

           # test num_waveforms_per_prompt=1 (default) for batch of prompts
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe([prompt] * batch_size					,      num_inference_steps=2     ).audios

           assert audios.shape == (batch_size, 2_56)

           # test num_waveforms_per_prompt for single prompt
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2					,      num_waveforms_per_prompt=__a     ).audios

           assert audios.shape == (num_waveforms_per_prompt, 2_56)

           # test num_waveforms_per_prompt for batch of prompts
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe(
               [prompt] * batch_size					,      num_inference_steps=2					,      num_waveforms_per_prompt=__a     ).audios

           assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )
           lowerCamelCase             =		audioldm_pipe.vocoder.config.sampling_rate

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(audio_length_in_s=0.016					,      **__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) / vocoder_sampling_rate == 0.016

           lowerCamelCase             =		audioldm_pipe(audio_length_in_s=0.032					,      **__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) / vocoder_sampling_rate == 0.032







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		["hey"]

           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           assert audio_shape == (1, 2_56)

           lowerCamelCase             =		audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           lowerCamelCase             =		SpeechTaHifiGan(__a     ).to(__a     )
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
           assert audio_shape == (1, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           self._test_inference_batch_single_identical(test_mean_pixel_difference=__a     )







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()					,      reason="XFormers attention is only available with CUDA and `xformers` installed"					,      )
     def     _a   (self     ):






           '''simple docstring'''



           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a     )





@slow
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def     _a   (self					,      __a					,      __a="cpu"					,      __a=torch.floataa					,      __a=0     ):






           '''simple docstring'''



           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		np.random.RandomState(__a     ).standard_normal((1, 8, 1_28, 16)     )
           lowerCamelCase             =		torch.from_numpy(__a     ).to(device=__a					,      dtype=__a     )
           lowerCamelCase             =		{
               "prompt": "A hammer hitting a wooden surface",
               "latents": latents,
               "generator": generator,
               "num_inference_steps": 3,
               "guidance_scale": 2.5,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		25
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[7_72_30:7_72_40]
           lowerCamelCase             =		np.array(
               [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[2_77_80:2_77_90]
           lowerCamelCase             =		np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 3E-2




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=7					,      __a=True					,      __a=True					,      __a=True					,      __a=99					,      __a=32					,      __a=5					,      __a=4					,      __a=37					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=5_12					,      __a=16					,      __a=2					,      __a=0.02					,      __a=3					,      __a=4					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		seq_length
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_token_type_ids
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		vocab_size
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		max_position_embeddings
           lowerCamelCase             =		type_vocab_size
           lowerCamelCase             =		type_sequence_label_size
           lowerCamelCase             =		initializer_range
           lowerCamelCase             =		num_labels
           lowerCamelCase             =		num_choices
           lowerCamelCase             =		scope
           lowerCamelCase             =		self.vocab_size - 1







     def     _a   (self     ):






           '''simple docstring'''



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

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

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

           lowerCamelCase             =		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					,      )

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

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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModel(config=__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      head_mask=__a     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a     )
           lowerCamelCase             =		model(__a     )

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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTDoubleHeadsModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.num_labels
           lowerCamelCase             =		OpenAIGPTForSequenceClassification(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				
           )             =		config_and_inputs
           lowerCamelCase             =		{
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "head_mask": head_mask,
           }

           return config, inputs_dict





@require_torch
class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      (
         (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
         if is_torch_available()
         else ()
     )
     _A      =      (
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
     _A      =      (
         {
             'feature-extraction': OpenAIGPTModel,
             'text-classification': OpenAIGPTForSequenceClassification,
             'text-generation': OpenAIGPTLMHeadModel,
             'zero-shot': OpenAIGPTForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a     ):






           '''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     _a   (self					,      __a					,      __a					,      __a=False     ):






           '''simple docstring'''



           lowerCamelCase             =		super()._prepare_for_class(__a					,      __a					,      return_labels=__a     )

           if return_labels:
                 if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		torch.zeros(
                           self.model_tester.batch_size					,      dtype=torch.long					,      device=__a     )
           return inputs_dict







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModelTester(self     )
           lowerCamelCase             =		ConfigTester(self					,      config_class=__a					,      n_embd=37     )







     def     _a   (self     ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase             =		OpenAIGPTModel.from_pretrained(__a     )
                 self.assertIsNotNone(__a     )





@require_torch
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"     )
           model.to(__a     )
           lowerCamelCase             =		torch.tensor([[4_81, 47_35, 5_44]]					,      dtype=torch.long					,      device=__a     )  # the president is
           lowerCamelCase             =		[
               4_81,
               47_35,
               5_44,
               2_46,
               9_63,
               8_70,
               7_62,
               2_39,
               2_44,
               4_04_77,
               2_44,
               2_49,
               7_19,
               8_81,
               4_87,
               5_44,
               2_40,
               2_44,
               6_03,
               4_81,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           lowerCamelCase             =		model.generate(__a					,      do_sample=__a     )
           self.assertListEqual(output_ids[0].tolist()					,      __a     )