


import importlib

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







def        _lowerCamelCase(	lowercase__   , lowercase__=False	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			OmegaConf.load(lowercase__	)
		if display:
				print(yaml.dump(OmegaConf.to_container(lowercase__	)	)	)
		return config







def        _lowerCamelCase(	lowercase__   , lowercase__=None   , lowercase__=None	)						->	Any:

		'''simple docstring'''







		if conf_path is None:
				__lowercase=			'./model_checkpoints/vqgan_only.yaml'
		__lowercase=			load_config(lowercase__   , display=lowercase__	)
		__lowercase=			VQModel(**config.model.params	)
		if ckpt_path is None:
				__lowercase=			'./model_checkpoints/vqgan_only.pt'
		__lowercase=			torch.load(lowercase__   , map_location=lowercase__	)
		if ".ckpt" in ckpt_path:
				__lowercase=			sd['state_dict']
		model.load_state_dict(lowercase__   , strict=lowercase__	)
		model.to(lowercase__	)
		del sd
		return model







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		__lowercase,							__lowercase,							__lowercase=			model.encode(lowercase__	)
		print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'	)
		__lowercase=			model.decode(lowercase__	)
		return xrec







def        _lowerCamelCase(	lowercase__   , lowercase__=False	)						->	str:

		'''simple docstring'''







		__lowercase,							__lowercase=			string.rsplit('.'   , 1	)
		if reload:
				__lowercase=			importlib.import_module(lowercase__	)
				importlib.reload(lowercase__	)
		return getattr(importlib.import_module(lowercase__   , package=lowercase__	)   , cls	)







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''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        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__=True   , lowercase__=True	)						->	Tuple:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		if ckpt:
				__lowercase=			torch.load(lowercase__   , map_location='cpu'	)
				__lowercase=			pl_sd['global_step']
				print(F'loaded model from global step {global_step}.'	)
		else:
				__lowercase=			{'state_dict': None}
				__lowercase=			None
		__lowercase=			load_model_from_config(config.model   , pl_sd['state_dict']   , gpu=lowercase__   , eval_mode=lowercase__	)['model']
		return model, global_step










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      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=5      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=5_1_2      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=2      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=3      ,				lowerCAmelCase=4      ,				lowerCAmelCase=None      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			seq_length
									__lowercase=			is_training
									__lowercase=			use_token_type_ids
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			intermediate_size
									__lowercase=			hidden_act
									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			type_vocab_size
									__lowercase=			type_sequence_label_size
									__lowercase=			initializer_range
									__lowercase=			num_labels
									__lowercase=			num_choices
									__lowercase=			scope
									__lowercase=			self.vocab_size - 1


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

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

									__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,
									    head_mask,
									    token_type_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTModel(config=lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase      ,				head_mask=lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase					)

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


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTLMHeadModel(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTDoubleHeadsModel(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			self.num_labels
									__lowercase=			OpenAIGPTForSequenceClassification(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

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







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									(
									    (
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							
									)=			config_and_inputs
									__lowercase=			{
									    'input_ids': input_ids,
									    'token_type_ids': token_type_ids,
									    'head_mask': head_mask,
									}

									return config, inputs_dict





@require_torch
class       A    (   A_					,				A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Optional[Any]       =(
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase_   :							Tuple       =(
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							UpperCamelCase_   :							List[str]       =(
							    {
							        '''feature-extraction''': OpenAIGPTModel,
							        '''text-classification''': OpenAIGPTForSequenceClassification,
							        '''text-generation''': OpenAIGPTLMHeadModel,
							        '''zero-shot''': OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									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      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=False					):
									__lowercase=			super()._prepare_for_class(lowerCAmelCase      ,				lowerCAmelCase      ,				return_labels=lowerCAmelCase					)

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


							def 			_A       (self					):
									__lowercase=			OpenAIGPTModelTester(self					)
									__lowercase=			ConfigTester(self      ,				config_class=lowerCAmelCase      ,				n_embd=3_7					)


							def 			_A       (self					):
									self.config_tester.run_common_tests()


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase					)







							@slow
							def 			_A       (self					):
									for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase=			OpenAIGPTModel.from_pretrained(lowerCAmelCase					)
											self.assertIsNotNone(lowerCAmelCase					)





@require_torch
class       A    (   unittest.TestCase ):


							@slow
							def 			_A       (self					):
									__lowercase=			OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'					)
									model.to(lowerCAmelCase					)
									__lowercase=			torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]      ,				dtype=torch.long      ,				device=lowerCAmelCase					)  # the president is
									__lowercase=			[
									    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

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






