

import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch





@require_flax
class 				lowerCamelCase  :

			def __init__(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_4      ,			__UpperCAmelCase			:  Union[str, Any]=7      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Optional[int]=9_9      ,			__UpperCAmelCase			:  Any=3_2      ,			__UpperCAmelCase			:  Union[str, Any]=5      ,			__UpperCAmelCase			:  str=4      ,			__UpperCAmelCase			:  List[str]=3_7      ,			__UpperCAmelCase			:  Tuple="gelu"      ,			__UpperCAmelCase			:  Optional[int]=0.1      ,			__UpperCAmelCase			:  List[Any]=0.1      ,			__UpperCAmelCase			:  Optional[Any]=5_1_2      ,			__UpperCAmelCase			:  Optional[Any]=0.02      ,			__UpperCAmelCase			:  Any=None      ,			)				->      Dict:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= seq_length
						SCREAMING_SNAKE_CASE__									= is_training
						SCREAMING_SNAKE_CASE__									= use_input_mask
						SCREAMING_SNAKE_CASE__									= use_labels
						SCREAMING_SNAKE_CASE__									= vocab_size
						SCREAMING_SNAKE_CASE__									= d_model
						SCREAMING_SNAKE_CASE__									= num_hidden_layers
						SCREAMING_SNAKE_CASE__									= num_attention_heads
						SCREAMING_SNAKE_CASE__									= ffn_dim
						SCREAMING_SNAKE_CASE__									= activation_function
						SCREAMING_SNAKE_CASE__									= activation_dropout
						SCREAMING_SNAKE_CASE__									= attention_dropout
						SCREAMING_SNAKE_CASE__									= max_position_embeddings
						SCREAMING_SNAKE_CASE__									= initializer_range
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= 0
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 1

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= np.clip(ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size    )      ,			3      ,			self.vocab_size    )

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

						SCREAMING_SNAKE_CASE__									= XGLMConfig(
						    vocab_size=self.vocab_size      ,			d_model=self.hidden_size      ,			num_layers=self.num_hidden_layers      ,			attention_heads=self.num_attention_heads      ,			ffn_dim=self.ffn_dim      ,			activation_function=self.activation_function      ,			activation_dropout=self.activation_dropout      ,			attention_dropout=self.attention_dropout      ,			max_position_embeddings=self.max_position_embeddings      ,			initializer_range=self.initializer_range      ,			use_cache=lowerCAmelCase__      ,			bos_token_id=self.bos_token_id      ,			eos_token_id=self.eos_token_id      ,			pad_token_id=self.pad_token_id      ,			)

						return (config, input_ids, input_mask)

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= config_and_inputs
						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
						return config, inputs_dict

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      int:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()

						SCREAMING_SNAKE_CASE__									= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
						SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			vocab_size=2    )

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Tuple    )				->      int:
						SCREAMING_SNAKE_CASE__									= 2_0
						SCREAMING_SNAKE_CASE__									= model_class_name(lowerCAmelCase__    )

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

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

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

						SCREAMING_SNAKE_CASE__									= model(lowerCAmelCase__    )

						SCREAMING_SNAKE_CASE__									= 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			:  List[str]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Optional[int]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= 2_0
						SCREAMING_SNAKE_CASE__									= model_class_name(lowerCAmelCase__    )

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

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

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

						SCREAMING_SNAKE_CASE__									= model(lowerCAmelCase__      ,			attention_mask=lowerCAmelCase__    )
						SCREAMING_SNAKE_CASE__									= 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_sentencepiece
@require_flax
class 				lowerCamelCase  (_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase     ):
			lowerCamelCase__				:						Union[str, Any]   						=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			lowerCamelCase__				:						str   						=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= FlaxXGLMModelTester(self    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[str]:


						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(lowerCAmelCase__      ,			lowerCAmelCase__      ,			lowerCAmelCase__      ,			lowerCAmelCase__    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[Any]:


						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    lowerCAmelCase__      ,			lowerCAmelCase__      ,			lowerCAmelCase__      ,			lowerCAmelCase__    )

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= XGLMTokenizer.from_pretrained("""XGLM"""      ,			padding_side="""left"""    )
						SCREAMING_SNAKE_CASE__									= tokenizer(["""Hello this is a long string""", """Hey"""]      ,			return_tensors="""np"""      ,			padding=lowerCAmelCase__      ,			truncation=lowerCAmelCase__    )

						SCREAMING_SNAKE_CASE__									= FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= False

						SCREAMING_SNAKE_CASE__									= jax.jit(model.generate    )

						SCREAMING_SNAKE_CASE__									= jit_generate(inputs["""input_ids"""]      ,			attention_mask=inputs["""attention_mask"""]    ).sequences

						SCREAMING_SNAKE_CASE__									= tokenizer.batch_decode(lowerCAmelCase__      ,			skip_special_tokens=lowerCAmelCase__    )

						SCREAMING_SNAKE_CASE__									= [
						    """Hello this is a long string of questions, but I\'m not sure if I\'m""",
						    """Hey, I\'m a newbie to the forum and I\'""",
						]

						self.assertListEqual(lowerCAmelCase__      ,			lowerCAmelCase__    )

			@is_pt_flax_cross_test
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      str:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= 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
												SCREAMING_SNAKE_CASE__									= self._prepare_for_class(lowerCAmelCase__      ,			lowerCAmelCase__    )
												SCREAMING_SNAKE_CASE__									= {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= pt_inputs["""input_ids"""].shape
												SCREAMING_SNAKE_CASE__									= np.random.randint(0      ,			seq_length - 1      ,			size=(batch_size,)    )
												for batch_idx, start_index in enumerate(lowerCAmelCase__    ):
															SCREAMING_SNAKE_CASE__									= 0
															SCREAMING_SNAKE_CASE__									= 1
															SCREAMING_SNAKE_CASE__									= 0
															SCREAMING_SNAKE_CASE__									= 1
												SCREAMING_SNAKE_CASE__									= pt_model_class(lowerCAmelCase__    ).eval()
												# Flax models don't use the `use_cache` option and cache is not returned as a default.
												# So we disable `use_cache` here for PyTorch model.
												SCREAMING_SNAKE_CASE__									= False
												SCREAMING_SNAKE_CASE__									= model_class(lowerCAmelCase__      ,			dtype=jnp.floataa    )
												SCREAMING_SNAKE_CASE__									= convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,			lowerCAmelCase__    )
												SCREAMING_SNAKE_CASE__									= fx_state

												with torch.no_grad():
															SCREAMING_SNAKE_CASE__									= pt_model(**lowerCAmelCase__    ).to_tuple()

												SCREAMING_SNAKE_CASE__									= 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__    )
															SCREAMING_SNAKE_CASE__									= model_class.from_pretrained(lowerCAmelCase__      ,			from_pt=lowerCAmelCase__    )

												SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= 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
												SCREAMING_SNAKE_CASE__									= self._prepare_for_class(lowerCAmelCase__      ,			lowerCAmelCase__    )
												SCREAMING_SNAKE_CASE__									= {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

												SCREAMING_SNAKE_CASE__									= pt_model_class(lowerCAmelCase__    ).eval()
												SCREAMING_SNAKE_CASE__									= False
												SCREAMING_SNAKE_CASE__									= model_class(lowerCAmelCase__      ,			dtype=jnp.floataa    )

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

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

												with torch.no_grad():
															SCREAMING_SNAKE_CASE__									= pt_model(**lowerCAmelCase__    ).to_tuple()

												SCREAMING_SNAKE_CASE__									= 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__    )
															SCREAMING_SNAKE_CASE__									= pt_model_class.from_pretrained(lowerCAmelCase__      ,			from_flax=lowerCAmelCase__    )

												with torch.no_grad():
															SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Optional[int]:


						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__									= model_class_name.from_pretrained("""facebook/xglm-564M"""    )
									SCREAMING_SNAKE_CASE__									= model(np.ones((1, 1)    )    )
									self.assertIsNotNone(lowerCAmelCase__    )





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


