
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch






class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   List[str]=14       ,		snake_case__						:   str=7       ,		snake_case__						:   Dict=True       ,		snake_case__						:   Tuple=True       ,		snake_case__						:   Optional[Any]=False       ,		snake_case__						:   int=True       ,		snake_case__						:   Optional[Any]=99       ,		snake_case__						:   Union[str, Any]=32       ,		snake_case__						:   List[Any]=4       ,		snake_case__						:   Optional[Any]=4       ,		snake_case__						:   Any=4       ,		snake_case__						:   Any=37       ,		snake_case__						:   Dict="gelu"       ,		snake_case__						:   Dict=0.1       ,		snake_case__						:   List[str]=0.1       ,		snake_case__						:   Tuple=5_12       ,		snake_case__						:   Tuple=0.02       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


          snake_case			:  Tuple     							= ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size					)

          snake_case			:  Optional[int]     							= None
          if self.use_input_mask:
                 snake_case			:  List[Any]     							= random_attention_mask([self.batch_size, self.seq_length]					)

          snake_case			:  str     							= GPTJConfig(
              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       ,		use_cache=snake_case__       ,		bos_token_id=self.bos_token_id       ,		eos_token_id=self.eos_token_id       ,		pad_token_id=self.pad_token_id       ,		rotary_dim=self.rotary_dim       ,		)

          return (config, input_ids, input_mask)



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


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   List[str]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= 20
          snake_case			:  Optional[Any]     							= model_class_name(snake_case__					)

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

          snake_case			:  Dict     							= jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)
          snake_case			:  Tuple     							= model(
              input_ids[:, :-1]       ,		attention_mask=snake_case__       ,		past_key_values=snake_case__       ,		position_ids=snake_case__       ,		)

          snake_case			:  Union[str, Any]     							= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype="i4"					)
          snake_case			:  Any     							= model(
              input_ids[:, -1:]       ,		attention_mask=snake_case__       ,		past_key_values=outputs_cache.past_key_values       ,		position_ids=snake_case__       ,		)

          snake_case			:  Union[str, Any]     							= model(snake_case__					)

          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}"""					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   List[Any]       ,		snake_case__						:   str       ,		snake_case__						:   int       ,		snake_case__						:   Optional[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  List[Any]     							= 20
          snake_case			:  Optional[int]     							= model_class_name(snake_case__					)

          snake_case			:  int     							= jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]       ,		axis=-1       ,		)

          snake_case			:  str     							= model.init_cache(input_ids.shape[0]       ,		snake_case__					)
          snake_case			:  Tuple     							= jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)

          snake_case			:  Union[str, Any]     							= model(
              input_ids[:, :-1]       ,		attention_mask=snake_case__       ,		past_key_values=snake_case__       ,		position_ids=snake_case__       ,		)
          snake_case			:  List[Any]     							= 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=snake_case__       ,		position_ids=snake_case__       ,		)

          snake_case			:  Optional[int]     							= model(snake_case__       ,		attention_mask=snake_case__					)

          snake_case			:  str     							= 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     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Any			=	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   A__   :						Optional[Any]			=	(FlaxGPTJForCausalLM,) if is_flax_available() else ()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Tuple     							= FlaxGPTJModelTester(self					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[int]:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case							,   snake_case							,   snake_case			:  Dict     							= self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''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_with_attn_mask(
                     snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__					)



   @tooslow
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


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

          snake_case			:  Optional[int]     							= FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"					)
          snake_case			:  List[Any]     							= False
          snake_case			:  Union[str, Any]     							= model.config.eos_token_id

          snake_case			:  List[Any]     							= jax.jit(model.generate					)

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

          snake_case			:  Optional[int]     							= tokenizer.batch_decode(snake_case__       ,		skip_special_tokens=snake_case__					)

          snake_case			:  List[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(snake_case__       ,		snake_case__					)



   @is_pt_flax_cross_test
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case							,   snake_case			:  List[str]     							= 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			:  Tuple     							= self._prepare_for_class(snake_case__       ,		snake_case__					)
                        snake_case			:  Optional[Any]     							= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                        # load corresponding PyTorch class
                        snake_case			:  List[Any]     							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
                        snake_case			:  int     							= getattr(snake_case__       ,		snake_case__					)

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

                        snake_case			:  Optional[int]     							= convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,		snake_case__					)
                        snake_case			:  List[str]     							= fx_state

                        with torch.no_grad():
                               snake_case			:  Tuple     							= pt_model(**snake_case__					).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                               pt_model.save_pretrained(snake_case__					)
                               snake_case			:  Optional[Any]     							= model_class.from_pretrained(snake_case__       ,		from_pt=snake_case__					)

                        snake_case			:  List[str]     							= fx_model_loaded(**snake_case__					).to_tuple()
                        self.assertEqual(
                            len(snake_case__					)       ,		len(snake_case__					)       ,		"Output lengths differ between Flax and PyTorch"					)
                        for fx_output_loaded, pt_output in zip(snake_case__       ,		snake_case__					):
                               self.assert_almost_equals(fx_output_loaded[:, -1]       ,		pt_output[:, -1].numpy()       ,		4e-2					)



   @is_pt_flax_cross_test
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      int:


          '''simple docstring'''


          snake_case							,   snake_case			:  List[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[str]     							= self._prepare_for_class(snake_case__       ,		snake_case__					)
                        snake_case			:  Optional[Any]     							= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                        # load corresponding PyTorch class
                        snake_case			:  List[Any]     							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
                        snake_case			:  Any     							= getattr(snake_case__       ,		snake_case__					)

                        snake_case			:  List[Any]     							= pt_model_class(snake_case__					).eval()
                        snake_case			:  Union[str, Any]     							= model_class(snake_case__       ,		dtype=jnp.floataa					)

                        snake_case			:  Tuple     							= load_flax_weights_in_pytorch_model(snake_case__       ,		fx_model.params					)
                        snake_case							,   snake_case			:  Union[str, 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(snake_case__					):
                               snake_case			:  Optional[int]     							= 0
                               snake_case			:  Union[str, Any]     							= 1
                               snake_case			:  Optional[Any]     							= 0
                               snake_case			:  Dict     							= 1

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

                        with torch.no_grad():
                               snake_case			:  Optional[int]     							= pt_model(**snake_case__					).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                               fx_model.save_pretrained(snake_case__					)
                               snake_case			:  str     							= pt_model_class.from_pretrained(snake_case__       ,		from_flax=snake_case__					)

                        with torch.no_grad():
                               snake_case			:  str     							= pt_model_loaded(**snake_case__					).to_tuple()

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



   @tooslow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case			:  List[str]     							= model_class_name.from_pretrained("EleutherAI/gpt-j-6B"					)
                 snake_case			:  Tuple     							= model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(snake_case__					)







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler





