






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
  import torch







class 						snake_case  :



   '''simple docstring'''






   def __init__(   self							:     List[str]			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     str=14			,						lowerCAmelCase							:     Any=7			,						lowerCAmelCase							:     Optional[int]=True			,						lowerCAmelCase							:     Optional[int]=True			,						lowerCAmelCase							:     Union[str, Any]=False			,						lowerCAmelCase							:     int=True			,						lowerCAmelCase							:     int=99			,						lowerCAmelCase							:     Tuple=32			,						lowerCAmelCase							:     Union[str, Any]=4			,						lowerCAmelCase							:     Dict=4			,						lowerCAmelCase							:     Union[str, Any]=[[["global", "local"], 2]]			,						lowerCAmelCase							:     List[str]=37			,						lowerCAmelCase							:     List[str]="gelu"			,						lowerCAmelCase							:     List[Any]=0.1			,						lowerCAmelCase							:     Optional[Any]=0.1			,						lowerCAmelCase							:     Optional[int]=512			,						lowerCAmelCase							:     str=7			,						lowerCAmelCase							:     Tuple=0.02			,						)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Tuple					=   parent
    _snake_case      :					Tuple					=   batch_size
    _snake_case      :					Dict					=   seq_length
    _snake_case      :					Any					=   is_training
    _snake_case      :					str					=   use_input_mask
    _snake_case      :					Tuple					=   use_token_type_ids
    _snake_case      :					Optional[Any]					=   use_labels
    _snake_case      :					Tuple					=   vocab_size
    _snake_case      :					Dict					=   hidden_size
    _snake_case      :					Tuple					=   num_hidden_layers
    _snake_case      :					str					=   num_attention_heads
    _snake_case      :					Optional[Any]					=   attention_types
    _snake_case      :					Optional[Any]					=   intermediate_size
    _snake_case      :					Optional[Any]					=   hidden_act
    _snake_case      :					int					=   hidden_dropout_prob
    _snake_case      :					Any					=   attention_probs_dropout_prob
    _snake_case      :					List[Any]					=   max_position_embeddings
    _snake_case      :					Union[str, Any]					=   window_size
    _snake_case      :					Any					=   initializer_range
    _snake_case      :					Tuple					=   None
    _snake_case      :					str					=   vocab_size - 1
    _snake_case      :					int					=   vocab_size - 1
    _snake_case      :					str					=   vocab_size - 1

   def        UpperCamelCase_					(   self							:     Optional[Any])			->			List[Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

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

    _snake_case      :					List[Any]					=   GPTNeoConfig(
        vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_layers=self.num_hidden_layers			,						num_heads=self.num_attention_heads			,						max_position_embeddings=self.max_position_embeddings			,						use_cache=lowerCAmelCase			,						bos_token_id=self.bos_token_id			,						eos_token_id=self.eos_token_id			,						pad_token_id=self.pad_token_id			,						window_size=self.window_size			,						attention_types=self.attention_types			,						)

    return (config, input_ids, input_mask)

   def        UpperCamelCase_					(   self							:     str)			->			Dict:

    """simple docstring"""







    _snake_case      :					str					=   self.prepare_config_and_inputs()
    _snake_case		,      _snake_case		,      _snake_case      :					Optional[Any]					=   config_and_inputs
    _snake_case      :					Optional[Any]					=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
    return config, inputs_dict

   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     Optional[int])			->			Any:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   20
    _snake_case      :					Dict					=   model_class_name(lowerCAmelCase)

    _snake_case      :					Optional[int]					=   model.init_cache(input_ids.shape[0]			,						lowerCAmelCase)
    _snake_case      :					int					=   jnp.ones((input_ids.shape[0], max_decoder_length)			,						dtype="""i4""")

    _snake_case      :					Optional[int]					=   jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1)[None, :]			,						(input_ids.shape[0], input_ids.shape[-1] - 1))
    _snake_case      :					List[str]					=   model(
        input_ids[:, :-1]			,						attention_mask=lowerCAmelCase			,						past_key_values=lowerCAmelCase			,						position_ids=lowerCAmelCase			,						)

    _snake_case      :					List[str]					=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,						dtype="""i4""")
    _snake_case      :					Tuple					=   model(
        input_ids[:, -1:]			,						attention_mask=lowerCAmelCase			,						past_key_values=outputs_cache.past_key_values			,						position_ids=lowerCAmelCase			,						)

    _snake_case      :					int					=   model(lowerCAmelCase)

    _snake_case      :					Union[str, Any]					=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
    self.parent.assertTrue(diff < 1E-3			,						msg=F'''Max diff is {diff}''')


   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     Tuple)			->			str:

    """simple docstring"""







    _snake_case      :					Tuple					=   20
    _snake_case      :					Tuple					=   model_class_name(lowerCAmelCase)

    _snake_case      :					List[Any]					=   jnp.concatenate(
        [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]			,						axis=-1			,						)

    _snake_case      :					List[str]					=   model.init_cache(input_ids.shape[0]			,						lowerCAmelCase)
    _snake_case      :					List[str]					=   jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1)[None, :]			,						(input_ids.shape[0], input_ids.shape[-1] - 1))

    _snake_case      :					Dict					=   model(
        input_ids[:, :-1]			,						attention_mask=lowerCAmelCase			,						past_key_values=lowerCAmelCase			,						position_ids=lowerCAmelCase			,						)
    _snake_case      :					str					=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,						dtype="""i4""")
    _snake_case      :					List[str]					=   model(
        input_ids[:, -1:]			,						past_key_values=outputs_cache.past_key_values			,						attention_mask=lowerCAmelCase			,						position_ids=lowerCAmelCase			,						)

    _snake_case      :					List[str]					=   model(lowerCAmelCase			,						attention_mask=lowerCAmelCase)

    _snake_case      :					Optional[int]					=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
    self.parent.assertTrue(diff < 1E-3			,						msg=F'''Max diff is {diff}''')







@require_flax
class 						snake_case  (		SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,unittest.TestCase					):



   '''simple docstring'''






   snake_case_   :	Union[str, Any]       =						(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   snake_case_   :	int       =						(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

   def        UpperCamelCase_					(   self							:     Dict)			->			Tuple:

    """simple docstring"""







    _snake_case      :					Any					=   FlaxGPTNeoModelTester(self)

   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[int]:

    """simple docstring"""







    for model_class_name in self.all_model_classes:
     _snake_case		,      _snake_case		,      _snake_case      :					List[Any]					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     str)			->			Any:

    """simple docstring"""







    for model_class_name in self.all_model_classes:
     _snake_case		,      _snake_case		,      _snake_case      :					Union[str, Any]					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)

   @slow
   def        UpperCamelCase_					(   self							:     List[str])			->			Dict:

    """simple docstring"""







    _snake_case      :					Tuple					=   GPTaTokenizer.from_pretrained("""gpt2"""			,						pad_token="""<|endoftext|>"""			,						padding_side="""left""")
    _snake_case      :					str					=   tokenizer(["""Hello this is a long string""", """Hey"""]			,						return_tensors="""np"""			,						padding=lowerCAmelCase			,						truncation=lowerCAmelCase)

    _snake_case      :					List[Any]					=   FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
    _snake_case      :					Tuple					=   False
    _snake_case      :					List[str]					=   model.config.eos_token_id

    _snake_case      :					Any					=   jax.jit(model.generate)

    _snake_case      :					int					=   jit_generate(
        inputs["""input_ids"""]			,						attention_mask=inputs["""attention_mask"""]			,						pad_token_id=tokenizer.pad_token_id).sequences

    _snake_case      :					Union[str, Any]					=   tokenizer.batch_decode(lowerCAmelCase			,						skip_special_tokens=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   [
        """Hello this is a long string of text.\n\nI\'m trying to get the text of the""",
        """Hey, I\'m a little late to the party. I\'m going to""",
    ]

    self.assertListEqual(lowerCAmelCase			,						lowerCAmelCase)

   @is_pt_flax_cross_test
   def        UpperCamelCase_					(   self							:     Tuple)			->			List[str]:

    """simple docstring"""







    _snake_case		,      _snake_case      :					Any					=   self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
      # prepare inputs
      _snake_case      :					List[Any]					=   self._prepare_for_class(lowerCAmelCase			,						lowerCAmelCase)
      _snake_case      :					Optional[int]					=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

      # load corresponding PyTorch class
      _snake_case      :					Union[str, Any]					=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
      _snake_case      :					Union[str, Any]					=   getattr(lowerCAmelCase			,						lowerCAmelCase)

      _snake_case		,      _snake_case      :					Optional[Any]					=   pt_inputs["""input_ids"""].shape
      _snake_case      :					Optional[Any]					=   np.random.randint(0			,						seq_length - 1			,						size=(batch_size,))
      for batch_idx, start_index in enumerate(lowerCAmelCase):
       _snake_case      :					str					=   0
       _snake_case      :					Any					=   1
       _snake_case      :					Any					=   0
       _snake_case      :					Dict					=   1
      _snake_case      :					Dict					=   pt_model_class(lowerCAmelCase).eval()
      _snake_case      :					int					=   model_class(lowerCAmelCase			,						dtype=jnp.floataa)

      _snake_case      :					Union[str, Any]					=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,						lowerCAmelCase)
      _snake_case      :					List[str]					=   fx_state

      with torch.no_grad():
       _snake_case      :					str					=   pt_model(**lowerCAmelCase).to_tuple()

      _snake_case      :					List[Any]					=   fx_model(**lowerCAmelCase).to_tuple()
      self.assertEqual(len(lowerCAmelCase)			,						len(lowerCAmelCase)			,						"""Output lengths differ between Flax and PyTorch""")
      for fx_output, pt_output in zip(lowerCAmelCase			,						lowerCAmelCase):
       self.assert_almost_equals(fx_output[:, -1]			,						pt_output[:, -1].numpy()			,						4E-2)

      with tempfile.TemporaryDirectory() as tmpdirname:
       pt_model.save_pretrained(lowerCAmelCase)
       _snake_case      :					List[str]					=   model_class.from_pretrained(lowerCAmelCase			,						from_pt=lowerCAmelCase)

      _snake_case      :					Optional[Any]					=   fx_model_loaded(**lowerCAmelCase).to_tuple()
      self.assertEqual(
          len(lowerCAmelCase)			,						len(lowerCAmelCase)			,						"""Output lengths differ between Flax and PyTorch""")
      for fx_output_loaded, pt_output in zip(lowerCAmelCase			,						lowerCAmelCase):
       self.assert_almost_equals(fx_output_loaded[:, -1]			,						pt_output[:, -1].numpy()			,						4E-2)

   @is_pt_flax_cross_test
   def        UpperCamelCase_					(   self							:     Tuple)			->			Dict:

    """simple docstring"""







    _snake_case		,      _snake_case      :					Dict					=   self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
      # prepare inputs
      _snake_case      :					List[str]					=   self._prepare_for_class(lowerCAmelCase			,						lowerCAmelCase)
      _snake_case      :					Any					=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

      # load corresponding PyTorch class
      _snake_case      :					Optional[Any]					=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
      _snake_case      :					str					=   getattr(lowerCAmelCase			,						lowerCAmelCase)

      _snake_case      :					Union[str, Any]					=   pt_model_class(lowerCAmelCase).eval()
      _snake_case      :					str					=   model_class(lowerCAmelCase			,						dtype=jnp.floataa)

      _snake_case      :					Any					=   load_flax_weights_in_pytorch_model(lowerCAmelCase			,						fx_model.params)
      _snake_case		,      _snake_case      :					List[str]					=   pt_inputs["""input_ids"""].shape
      _snake_case      :					Optional[int]					=   np.random.randint(0			,						seq_length - 1			,						size=(batch_size,))
      for batch_idx, start_index in enumerate(lowerCAmelCase):
       _snake_case      :					Optional[int]					=   0
       _snake_case      :					int					=   1
       _snake_case      :					Optional[Any]					=   0
       _snake_case      :					List[str]					=   1

      # make sure weights are tied in PyTorch
      pt_model.tie_weights()

      with torch.no_grad():
       _snake_case      :					Any					=   pt_model(**lowerCAmelCase).to_tuple()

      _snake_case      :					str					=   fx_model(**lowerCAmelCase).to_tuple()
      self.assertEqual(len(lowerCAmelCase)			,						len(lowerCAmelCase)			,						"""Output lengths differ between Flax and PyTorch""")
      for fx_output, pt_output in zip(lowerCAmelCase			,						lowerCAmelCase):
       self.assert_almost_equals(fx_output[:, -1]			,						pt_output[:, -1].numpy()			,						4E-2)

      with tempfile.TemporaryDirectory() as tmpdirname:
       fx_model.save_pretrained(lowerCAmelCase)
       _snake_case      :					Optional[Any]					=   pt_model_class.from_pretrained(lowerCAmelCase			,						from_flax=lowerCAmelCase)

      with torch.no_grad():
       _snake_case      :					Any					=   pt_model_loaded(**lowerCAmelCase).to_tuple()

      self.assertEqual(
          len(lowerCAmelCase)			,						len(lowerCAmelCase)			,						"""Output lengths differ between Flax and PyTorch""")
      for fx_output, pt_output in zip(lowerCAmelCase			,						lowerCAmelCase):
       self.assert_almost_equals(fx_output[:, -1]			,						pt_output[:, -1].numpy()			,						4E-2)


   @slow
   def        UpperCamelCase_					(   self							:     List[str])			->			List[str]:

    """simple docstring"""







    for model_class_name in self.all_model_classes:
     _snake_case      :					Union[str, Any]					=   model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
     _snake_case      :					str					=   model(np.ones((1, 1)))
     self.assertIsNotNone(lowerCAmelCase)





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


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

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







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



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
