

"""simple docstring"""





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      __a :

			"""simple docstring"""

			def __init__(				self  :       Union[str, Any]    ,	lowercase_  :       Optional[int]    ,	lowercase_  :       Tuple=14    ,	lowercase_  :       List[Any]=7    ,	lowercase_  :       int=True    ,	lowercase_  :       List[Any]=True    ,	lowercase_  :       Optional[int]=True    ,	lowercase_  :       int=99    ,	lowercase_  :       Tuple=32    ,	lowercase_  :       Optional[Any]=5    ,	lowercase_  :       str=4    ,	lowercase_  :       Any=37    ,	lowercase_  :       Union[str, Any]="gelu"    ,	lowercase_  :       int=0.1    ,	lowercase_  :       Union[str, Any]=0.1    ,	lowercase_  :       Optional[Any]=512    ,	lowercase_  :       List[str]=0.0_2    ,	lowercase_  :       str=None    ,	):
								UpperCamelCase__		:     Tuple								=parent
								UpperCamelCase__		:     Optional[int]								=batch_size
								UpperCamelCase__		:     Any								=seq_length
								UpperCamelCase__		:     Dict								=is_training
								UpperCamelCase__		:     List[Any]								=use_input_mask
								UpperCamelCase__		:     int								=use_labels
								UpperCamelCase__		:     List[str]								=vocab_size
								UpperCamelCase__		:     str								=d_model
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Union[str, Any]								=num_attention_heads
								UpperCamelCase__		:     Optional[Any]								=ffn_dim
								UpperCamelCase__		:     Optional[int]								=activation_function
								UpperCamelCase__		:     List[str]								=activation_dropout
								UpperCamelCase__		:     Union[str, Any]								=attention_dropout
								UpperCamelCase__		:     str								=max_position_embeddings
								UpperCamelCase__		:     Dict								=initializer_range
								UpperCamelCase__		:     Optional[int]								=None
								UpperCamelCase__		:     str								=0
								UpperCamelCase__		:     Dict								=2
								UpperCamelCase__		:     str								=1






			def  _lowerCAmelCase      (				self  :       List[str]    ):
								UpperCamelCase__		:     Dict								=np.clip(ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size    )    ,	3    ,	self.vocab_size    )

								UpperCamelCase__		:     str								=None
								if self.use_input_mask:
													UpperCamelCase__		:     Tuple								=random_attention_mask([self.batch_size, self.seq_length]    )

								UpperCamelCase__		:     Dict								=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=lowercase_    ,	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  _lowerCAmelCase      (				self  :       Optional[Any]    ):
								UpperCamelCase__		:     Tuple								=self.prepare_config_and_inputs()
								UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Any								=config_and_inputs
								UpperCamelCase__		:     Optional[Any]								={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict






			def  _lowerCAmelCase      (				self  :       Tuple    ):
								UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Optional[Any]								=self.prepare_config_and_inputs()

								UpperCamelCase__		:     Any								=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								UpperCamelCase__		:     int								=ids_tensor([self.batch_size, self.seq_length]    ,	vocab_size=2    )

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






			def  _lowerCAmelCase      (				self  :       List[Any]    ,	lowercase_  :       Union[str, Any]    ,	lowercase_  :       List[str]    ,	lowercase_  :       Any    ,	lowercase_  :       Optional[Any]    ):
								UpperCamelCase__		:     Any								=20
								UpperCamelCase__		:     str								=model_class_name(lowercase_    )

								UpperCamelCase__		:     Any								=model.init_cache(input_ids.shape[0]    ,	lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=jnp.ones((input_ids.shape[0], max_decoder_length)    ,	dtype='''i4'''    )

								UpperCamelCase__		:     List[Any]								=jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :]    ,	(input_ids.shape[0], input_ids.shape[-1] - 1)    )
								UpperCamelCase__		:     str								=model(
								    input_ids[:, :-1]    ,	attention_mask=lowercase_    ,	past_key_values=lowercase_    ,	position_ids=lowercase_    ,	)

								UpperCamelCase__		:     List[str]								=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,	dtype='''i4'''    )
								UpperCamelCase__		:     int								=model(
								    input_ids[:, -1:]    ,	attention_mask=lowercase_    ,	past_key_values=outputs_cache.past_key_values    ,	position_ids=lowercase_    ,	)

								UpperCamelCase__		:     Optional[int]								=model(lowercase_    )

								UpperCamelCase__		:     List[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  _lowerCAmelCase      (				self  :       Dict    ,	lowercase_  :       Tuple    ,	lowercase_  :       str    ,	lowercase_  :       List[str]    ,	lowercase_  :       List[str]    ):
								UpperCamelCase__		:     List[str]								=20
								UpperCamelCase__		:     List[Any]								=model_class_name(lowercase_    )

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

								UpperCamelCase__		:     Optional[Any]								=model.init_cache(input_ids.shape[0]    ,	lowercase_    )
								UpperCamelCase__		:     Tuple								=jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :]    ,	(input_ids.shape[0], input_ids.shape[-1] - 1)    )

								UpperCamelCase__		:     Tuple								=model(
								    input_ids[:, :-1]    ,	attention_mask=lowercase_    ,	past_key_values=lowercase_    ,	position_ids=lowercase_    ,	)
								UpperCamelCase__		:     Union[str, Any]								=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,	dtype='''i4'''    )
								UpperCamelCase__		:     Optional[int]								=model(
								    input_ids[:, -1:]    ,	past_key_values=outputs_cache.past_key_values    ,	attention_mask=lowercase_    ,	position_ids=lowercase_    ,	)

								UpperCamelCase__		:     Dict								=model(lowercase_    ,	attention_mask=lowercase_    )
								UpperCamelCase__		:     Dict								=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      __a (   snake_case__,     snake_case__,     unittest.TestCase     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			SCREAMING_SNAKE_CASE_      =    (FlaxXGLMForCausalLM,) if is_flax_available() else ()






			def  _lowerCAmelCase      (				self  :       Union[str, Any]    ):
								UpperCamelCase__		:     Optional[int]								=FlaxXGLMModelTester(self    )






			def  _lowerCAmelCase      (				self  :       Optional[int]    ):
								for model_class_name in self.all_model_classes:
													UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Tuple								=self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(lowercase_    ,	lowercase_    ,	lowercase_    ,	lowercase_    )






			def  _lowerCAmelCase      (				self  :       Tuple    ):
								for model_class_name in self.all_model_classes:
													UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Tuple								=self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    lowercase_    ,	lowercase_    ,	lowercase_    ,	lowercase_    )






			@slow
			def  _lowerCAmelCase      (				self  :       List[str]    ):
								UpperCamelCase__		:     Dict								=XGLMTokenizer.from_pretrained('''XGLM'''    ,	padding_side='''left'''    )
								UpperCamelCase__		:     List[Any]								=tokenizer(['''Hello this is a long string''', '''Hey''']    ,	return_tensors='''np'''    ,	padding=lowercase_    ,	truncation=lowercase_    )

								UpperCamelCase__		:     Any								=FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
								UpperCamelCase__		:     Dict								=1
								UpperCamelCase__		:     Optional[Any]								=False

								UpperCamelCase__		:     int								=jax.jit(model.generate    )

								UpperCamelCase__		:     Union[str, Any]								=jit_generate(inputs['''input_ids''']    ,	attention_mask=inputs['''attention_mask''']    ).sequences

								UpperCamelCase__		:     Tuple								=tokenizer.batch_decode(lowercase_    ,	skip_special_tokens=lowercase_    )

								UpperCamelCase__		:     Union[str, Any]								=[
								    '''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(lowercase_    ,	lowercase_    )






			@is_pt_flax_cross_test
			def  _lowerCAmelCase      (				self  :       str    ):
								UpperCamelCase__	,      UpperCamelCase__		:     Optional[int]								=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
																		UpperCamelCase__		:     List[str]								=self._prepare_for_class(lowercase_    ,	lowercase_    )
																		UpperCamelCase__		:     Any								={k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

																		UpperCamelCase__	,      UpperCamelCase__		:     List[str]								=pt_inputs['''input_ids'''].shape
																		UpperCamelCase__		:     Optional[int]								=np.random.randint(0    ,	seq_length - 1    ,	size=(batch_size,)    )
																		for batch_idx, start_index in enumerate(lowercase_    ):
																							UpperCamelCase__		:     List[Any]								=0
																							UpperCamelCase__		:     Dict								=1
																							UpperCamelCase__		:     Union[str, Any]								=0
																							UpperCamelCase__		:     str								=1
																		UpperCamelCase__		:     Any								=pt_model_class(lowercase_    ).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.
																		UpperCamelCase__		:     Union[str, Any]								=False
																		UpperCamelCase__		:     int								=model_class(lowercase_    ,	dtype=jnp.floataa    )
																		UpperCamelCase__		:     List[str]								=convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,	lowercase_    )
																		UpperCamelCase__		:     Dict								=fx_state

																		with torch.no_grad():
																							UpperCamelCase__		:     int								=pt_model(**lowercase_    ).to_tuple()

																		UpperCamelCase__		:     Optional[int]								=fx_model(**lowercase_    ).to_tuple()
																		self.assertEqual(len(lowercase_    )    ,	len(lowercase_    )    ,	'''Output lengths differ between Flax and PyTorch'''    )
																		for fx_output, pt_output in zip(lowercase_    ,	lowercase_    ):
																							self.assert_almost_equals(fx_output[:, -1]    ,	pt_output[:, -1].numpy()    ,	4e-2    )

																		with tempfile.TemporaryDirectory() as tmpdirname:
																							pt_model.save_pretrained(lowercase_    )
																							UpperCamelCase__		:     Any								=model_class.from_pretrained(lowercase_    ,	from_pt=lowercase_    )

																		UpperCamelCase__		:     Any								=fx_model_loaded(**lowercase_    ).to_tuple()
																		self.assertEqual(
																		    len(lowercase_    )    ,	len(lowercase_    )    ,	'''Output lengths differ between Flax and PyTorch'''    )
																		for fx_output_loaded, pt_output in zip(lowercase_    ,	lowercase_    ):
																							self.assert_almost_equals(fx_output_loaded[:, -1]    ,	pt_output[:, -1].numpy()    ,	4e-2    )






			@is_pt_flax_cross_test
			def  _lowerCAmelCase      (				self  :       List[str]    ):
								UpperCamelCase__	,      UpperCamelCase__		:     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
																		UpperCamelCase__		:     Any								=self._prepare_for_class(lowercase_    ,	lowercase_    )
																		UpperCamelCase__		:     Any								={k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		UpperCamelCase__		:     int								=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		UpperCamelCase__		:     str								=getattr(lowercase_    ,	lowercase_    )

																		UpperCamelCase__		:     int								=pt_model_class(lowercase_    ).eval()
																		UpperCamelCase__		:     Optional[int]								=False
																		UpperCamelCase__		:     int								=model_class(lowercase_    ,	dtype=jnp.floataa    )

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

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

																		with torch.no_grad():
																							UpperCamelCase__		:     Any								=pt_model(**lowercase_    ).to_tuple()

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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																							fx_model.save_pretrained(lowercase_    )
																							UpperCamelCase__		:     Tuple								=pt_model_class.from_pretrained(lowercase_    ,	from_flax=lowercase_    )

																		with torch.no_grad():
																							UpperCamelCase__		:     Optional[int]								=pt_model_loaded(**lowercase_    ).to_tuple()

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






			@slow
			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								for model_class_name in self.all_model_classes:
													UpperCamelCase__		:     Any								=model_class_name.from_pretrained('''facebook/xglm-564M'''    )
													UpperCamelCase__		:     Union[str, Any]								=model(np.ones((1, 1)    )    )
													self.assertIsNotNone(lowercase_    )





"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	**lowercase_    ,	)


