
'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    GPTNeoXForCausalLM,
				    GPTNeoXForQuestionAnswering,
				    GPTNeoXForSequenceClassification,
				    GPTNeoXForTokenClassification,
				    GPTNeoXModel,
				)


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=64  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       str:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  scope
														_snake_case       =  vocab_size - 1

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

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

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

														_snake_case       =  self.get_config()

														return config, input_ids, input_mask, token_labels

							def 	lowercase							(self  )					->       Optional[Any]:
														return GPTNeoXConfig(
														    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  is_decoder=UpperCAmelCase  ,  initializer_range=self.initializer_range  ,  pad_token_id=self.pad_token_id  ,  )

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  self.prepare_config_and_inputs()

														_snake_case       =  True

														return config, input_ids, input_mask, token_labels

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
														_snake_case       =  GPTNeoXModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Tuple:
														_snake_case       =  True
														_snake_case       =  GPTNeoXModel(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )
														self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Tuple:
														_snake_case       =  GPTNeoXForCausalLM(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_snake_case       =  self.num_labels
														_snake_case       =  GPTNeoXForQuestionAnswering(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )
														self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)  )
														self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_snake_case       =  self.num_labels
														_snake_case       =  GPTNeoXForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  ids_tensor([self.batch_size]  ,  self.type_sequence_label_size  )
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  self.num_labels
														_snake_case       =  GPTNeoXForTokenClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  True
														_snake_case       =  GPTNeoXForCausalLM(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()

														# first forward pass
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  use_cache=UpperCAmelCase  )
														_snake_case       =  outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														_snake_case       =  ids_tensor((self.batch_size, 3)  ,  config.vocab_size  )
														_snake_case       =  ids_tensor((self.batch_size, 3)  ,  vocab_size=2  )

														# append to next input_ids and
														_snake_case       =  torch.cat([input_ids, next_tokens]  ,  dim=-1  )
														_snake_case       =  torch.cat([input_mask, next_mask]  ,  dim=-1  )

														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  output_hidden_states=UpperCAmelCase  )
														_snake_case       =  output_from_no_past["""hidden_states"""][0]
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  past_key_values=UpperCAmelCase  ,  output_hidden_states=UpperCAmelCase  ,  )["""hidden_states"""][0]

														# select random slice
														_snake_case       =  ids_tensor((1,)  ,  output_from_past.shape[-1]  ).item()
														_snake_case       =  output_from_no_past[:, -3:, random_slice_idx].detach()
														_snake_case       =  output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(UpperCAmelCase  ,  UpperCAmelCase  ,  atol=1e-3  )  )





							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.prepare_config_and_inputs()
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(GPTNeoXForCausalLM,) if is_torch_available() else ()
							lowerCAmelCase_ 						=						(
							    {
							        "feature-extraction": GPTNeoXModel,
							        "question-answering": GPTNeoXForQuestionAnswering,
							        "text-classification": GPTNeoXForSequenceClassification,
							        "text-generation": GPTNeoXForCausalLM,
							        "token-classification": GPTNeoXForTokenClassification,
							        "zero-shot": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  GPTNeoXModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=64  ,  num_attention_heads=8  )

							def 	lowercase							(self  )					->       Any:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														# This regression test was failing with PyTorch < 1.3
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_decoder()

														_snake_case       =  None

														self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case,							_snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase  )

							@unittest.skip(reason="""Feed forward chunking is not implemented"""  )
							def 	lowercase							(self  )					->       Tuple:
														pass





							@parameterized.expand([("""linear""",), ("""dynamic""",)]  )
							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Dict:
														_snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()
														_snake_case       =  ids_tensor([1, 10]  ,  config.vocab_size  )
														_snake_case       =  ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,  config.vocab_size  )

														set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
														_snake_case       =  GPTNeoXModel(UpperCAmelCase  )
														original_model.to(UpperCAmelCase  )
														original_model.eval()
														_snake_case       =  original_model(UpperCAmelCase  ).last_hidden_state
														_snake_case       =  original_model(UpperCAmelCase  ).last_hidden_state

														set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
														_snake_case       =  {"""type""": scaling_type, """factor""": 10.0}
														_snake_case       =  GPTNeoXModel(UpperCAmelCase  )
														scaled_model.to(UpperCAmelCase  )
														scaled_model.eval()
														_snake_case       =  scaled_model(UpperCAmelCase  ).last_hidden_state
														_snake_case       =  scaled_model(UpperCAmelCase  ).last_hidden_state

														# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
														# maximum sequence length, so the outputs for the short input should match.
														if scaling_type == "dynamic":
																					self.assertTrue(torch.allclose(UpperCAmelCase  ,  UpperCAmelCase  ,  atol=1e-5  )  )
														else:
																					self.assertFalse(torch.allclose(UpperCAmelCase  ,  UpperCAmelCase  ,  atol=1e-5  )  )

														# The output should be different for long inputs
														self.assertFalse(torch.allclose(UpperCAmelCase  ,  UpperCAmelCase  ,  atol=1e-5  )  )


@require_torch
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@slow
							def 	lowercase							(self  )					->       str:
														_snake_case       =  AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""  )
														for checkpointing in [True, False]:
																					_snake_case       =  GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""  )

																					if checkpointing:
																												model.gradient_checkpointing_enable()
																					else:
																												model.gradient_checkpointing_disable()
																					model.to(UpperCAmelCase  )

																					_snake_case       =  tokenizer("""My favorite food is"""  ,  return_tensors="""pt"""  ).to(UpperCAmelCase  )
																					# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																					# See: https://github.com/huggingface/transformers/pull/24193
																					_snake_case       =  """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

																					_snake_case       =  model.generate(**UpperCAmelCase  ,  do_sample=UpperCAmelCase  ,  max_new_tokens=20  )
																					_snake_case       =  tokenizer.batch_decode(UpperCAmelCase  )[0]

																					self.assertEqual(UpperCAmelCase  ,  UpperCAmelCase  )

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


@require_torch_gpu
@slow
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1