





"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   42


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .pipeline_text_to_video_synth import TextToVideoSDPipeline
						from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
						from .pipeline_text_to_video_zero import TextToVideoZeroPipeline










"""simple docstring"""




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 			_a						:






				def __init__(			self		:   Dict,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Optional[int]=1_3,			lowerCAmelCase__		:   Optional[Any]=7,			lowerCAmelCase__		:   Optional[Any]=True,			lowerCAmelCase__		:   Any=True,			lowerCAmelCase__		:   str=True,			lowerCAmelCase__		:   Any=9_9,			lowerCAmelCase__		:   Dict=3_2,			lowerCAmelCase__		:   List[Any]=5,			lowerCAmelCase__		:   Tuple=4,			lowerCAmelCase__		:   List[Any]=3_7,			lowerCAmelCase__		:   Tuple="gelu",			lowerCAmelCase__		:   Any=0.1,			lowerCAmelCase__		:   Optional[Any]=0.1,			lowerCAmelCase__		:   Dict=5_1_2,			lowerCAmelCase__		:   List[str]=1_6,			lowerCAmelCase__		:   Tuple=2,			lowerCAmelCase__		:   int=0.02,			lowerCAmelCase__		:   int=3,			lowerCAmelCase__		:   Optional[Any]=4,			lowerCAmelCase__		:   Dict=None,			)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   parent
										_UpperCamelCase      : Union[str, Any]         =   batch_size
										_UpperCamelCase      : Union[str, Any]         =   seq_length
										_UpperCamelCase      : Tuple         =   is_training
										_UpperCamelCase      : Tuple         =   use_token_type_ids
										_UpperCamelCase      : Optional[int]         =   use_labels
										_UpperCamelCase      : Dict         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : Optional[Any]         =   num_hidden_layers
										_UpperCamelCase      : str         =   num_attention_heads
										_UpperCamelCase      : Union[str, Any]         =   intermediate_size
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Optional[Any]         =   hidden_dropout_prob
										_UpperCamelCase      : int         =   attention_probs_dropout_prob
										_UpperCamelCase      : Union[str, Any]         =   max_position_embeddings
										_UpperCamelCase      : int         =   type_vocab_size
										_UpperCamelCase      : List[str]         =   type_sequence_label_size
										_UpperCamelCase      : List[str]         =   initializer_range
										_UpperCamelCase      : int         =   num_labels
										_UpperCamelCase      : List[str]         =   num_choices
										_UpperCamelCase      : str         =   scope
										_UpperCamelCase      : Optional[int]         =   self.vocab_size - 1






				def     snake_case     (			self		:   int							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   ids_tensor([self.batch_size, self.seq_length],			self.vocab_size							)

										_UpperCamelCase      : List[str]         =   None
										if self.use_token_type_ids:
																_UpperCamelCase      : Tuple         =   ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size							)

										_UpperCamelCase      : Optional[int]         =   None
										_UpperCamelCase      : str         =   None
										_UpperCamelCase      : List[str]         =   None
										if self.use_labels:
																_UpperCamelCase      : int         =   ids_tensor([self.batch_size],			self.type_sequence_label_size							)
																_UpperCamelCase      : Dict         =   ids_tensor([self.batch_size, self.seq_length],			self.num_labels							)
																_UpperCamelCase      : Dict         =   ids_tensor([self.batch_size],			self.num_choices							)

										_UpperCamelCase      : str         =   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,			)

										_UpperCamelCase      : List[Any]         =   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     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   List[str],			*lowerCAmelCase__		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   OpenAIGPTModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										_UpperCamelCase      : List[str]         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			head_mask=lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   model(lowerCAmelCase__							)

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






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Optional[Any],			*lowerCAmelCase__		:   Union[str, Any]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   OpenAIGPTLMHeadModel(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										_UpperCamelCase      : Tuple         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.loss.shape,			()							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   List[Any],			*lowerCAmelCase__		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   OpenAIGPTDoubleHeadsModel(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										_UpperCamelCase      : Optional[int]         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.loss.shape,			()							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)							)






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[Any],			*lowerCAmelCase__		:   List[str]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.num_labels
										_UpperCamelCase      : Optional[int]         =   OpenAIGPTForSequenceClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										_UpperCamelCase      : str         =   ids_tensor([self.batch_size],			self.type_sequence_label_size							)
										_UpperCamelCase      : Union[str, Any]         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)							)






				def     snake_case     (			self		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							
										)      : Tuple         =   config_and_inputs
										_UpperCamelCase      : Tuple         =   {
										    '''input_ids''': input_ids,
										    '''token_type_ids''': token_type_ids,
										    '''head_mask''': head_mask,
										}

										return config, inputs_dict




@require_torch
class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   (
				    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase								=   (
				    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				UpperCamelCase								=   (
				    {
				        '''feature-extraction''': OpenAIGPTModel,
				        '''text-classification''': OpenAIGPTForSequenceClassification,
				        '''text-generation''': OpenAIGPTLMHeadModel,
				        '''zero-shot''': OpenAIGPTForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   List[str]							)     ->				List[str]:




										'''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		:   str,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[int]=False							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   super()._prepare_for_class(lowerCAmelCase__,			lowerCAmelCase__,			return_labels=lowerCAmelCase__							)

										if return_labels:
																if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						_UpperCamelCase      : Union[str, Any]         =   torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),			dtype=torch.long,			device=lowerCAmelCase__,			)
																						_UpperCamelCase      : Tuple         =   inputs_dict['''labels''']
																						_UpperCamelCase      : List[str]         =   inputs_dict['''labels''']
																						_UpperCamelCase      : Optional[Any]         =   torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices),			dtype=torch.long,			device=lowerCAmelCase__,			)
																						_UpperCamelCase      : Dict         =   torch.zeros(
																						    self.model_tester.batch_size,			dtype=torch.long,			device=lowerCAmelCase__							)
										return inputs_dict






				def     snake_case     (			self		:   List[str]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   OpenAIGPTModelTester(self							)
										_UpperCamelCase      : int         =   ConfigTester(self,			config_class=lowerCAmelCase__,			n_embd=3_7							)






				def     snake_case     (			self		:   Optional[int]							)     ->				str:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def     snake_case     (			self		:   Optional[int]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Any							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase__							)






				@slow
				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : int         =   OpenAIGPTModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)




@require_torch
class 			_a						(				unittest.TestCase    ):






				@slow
				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''							)
										model.to(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]],			dtype=torch.long,			device=lowerCAmelCase__							)  # the president is
										_UpperCamelCase      : Optional[int]         =   [
										    4_8_1,
										    4_7_3_5,
										    5_4_4,
										    2_4_6,
										    9_6_3,
										    8_7_0,
										    7_6_2,
										    2_3_9,
										    2_4_4,
										    4_0_4_7_7,
										    2_4_4,
										    2_4_9,
										    7_1_9,
										    8_8_1,
										    4_8_7,
										    5_4_4,
										    2_4_0,
										    2_4_4,
										    6_0_3,
										    4_8_1,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										_UpperCamelCase      : Union[str, Any]         =   model.generate(lowerCAmelCase__,			do_sample=lowerCAmelCase__							)
										self.assertListEqual(output_ids[0].tolist(),			lowerCAmelCase__							)



