'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__snake_case				   =  {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case				   =  ["""RealmTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case				   =  [
						    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """RealmEmbedder""",
						    """RealmForOpenQA""",
						    """RealmKnowledgeAugEncoder""",
						    """RealmPreTrainedModel""",
						    """RealmReader""",
						    """RealmScorer""",
						    """load_tf_weights_in_realm""",
						]
						__snake_case				   =  ["""RealmRetriever"""]


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

						__snake_case				   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''




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     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=14	,   __lowerCAmelCase=7	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=99	,   __lowerCAmelCase=32	,   __lowerCAmelCase=4	,   __lowerCAmelCase=4	,   __lowerCAmelCase=[[["global", "local"], 2]]	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=7	,   __lowerCAmelCase=0.02	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  seq_length
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_input_mask
					lowerCAmelCase        =  use_token_type_ids
					lowerCAmelCase        =  use_labels
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  hidden_size
					lowerCAmelCase        =  num_hidden_layers
					lowerCAmelCase        =  num_attention_heads
					lowerCAmelCase        =  attention_types
					lowerCAmelCase        =  intermediate_size
					lowerCAmelCase        =  hidden_act
					lowerCAmelCase        =  hidden_dropout_prob
					lowerCAmelCase        =  attention_probs_dropout_prob
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  window_size
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  None
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size)

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

					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  config_and_inputs
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.ones((input_ids.shape[0], max_decoder_length)	,   dtype="""i4""")

					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))
					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

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

					lowerCAmelCase        =  model(__lowerCAmelCase)

					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

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

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

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

					lowerCAmelCase        =  model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase)

					lowerCAmelCase        =  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     a__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			UpperCAmelCase_      :					Optional[Any]								=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  FlaxGPTNeoModelTester(self)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


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

					lowerCAmelCase        =  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
					lowerCAmelCase        =  False
					lowerCAmelCase        =  model.config.eos_token_id

					lowerCAmelCase        =  jax.jit(model.generate)

					lowerCAmelCase        =  jit_generate(
					    inputs["""input_ids"""]	,   attention_mask=inputs["""attention_mask"""]	,   pad_token_id=tokenizer.pad_token_id).sequences

					lowerCAmelCase        =  tokenizer.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase)

					lowerCAmelCase        =  [
					    """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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  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
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

									lowerCAmelCase        =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   __lowerCAmelCase)
									lowerCAmelCase        =  fx_state

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

									lowerCAmelCase        =  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)
											lowerCAmelCase        =  model_class.from_pretrained(__lowerCAmelCase	,   from_pt=__lowerCAmelCase)

									lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  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
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

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

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

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

									lowerCAmelCase        =  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)
											lowerCAmelCase        =  pt_model_class.from_pretrained(__lowerCAmelCase	,   from_flax=__lowerCAmelCase)

									with torch.no_grad():
											lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase        =  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
							lowerCAmelCase        =  model(np.ones((1, 1)))
							self.assertIsNotNone(__lowerCAmelCase)




