



"""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     :



			def __init__( self       :							Dict      ,  _lowercase       :							Any      ,  _lowercase       :							List[Any]=14      ,  _lowercase       :							Dict=7      ,  _lowercase       :							List[Any]=True      ,  _lowercase       :							List[str]=True      ,  _lowercase       :							List[Any]=True      ,  _lowercase       :							int=99      ,  _lowercase       :							Dict=32      ,  _lowercase       :							str=5      ,  _lowercase       :							List[Any]=4      ,  _lowercase       :							List[Any]=37      ,  _lowercase       :							List[Any]="gelu"      ,  _lowercase       :							str=0.1      ,  _lowercase       :							List[str]=0.1      ,  _lowercase       :							Dict=512      ,  _lowercase       :							Optional[Any]=0.02      ,  _lowercase       :							int=None      ,  )       ->				Union[str, Any]:
									snake_case      : Optional[Any]							=   parent
									snake_case      : Optional[int]							=   batch_size
									snake_case      : List[Any]							=   seq_length
									snake_case      : Any							=   is_training
									snake_case      : Optional[Any]							=   use_input_mask
									snake_case      : Any							=   use_labels
									snake_case      : Union[str, Any]							=   vocab_size
									snake_case      : str							=   d_model
									snake_case      : Union[str, Any]							=   num_hidden_layers
									snake_case      : List[Any]							=   num_attention_heads
									snake_case      : List[str]							=   ffn_dim
									snake_case      : Any							=   activation_function
									snake_case      : List[str]							=   activation_dropout
									snake_case      : Optional[Any]							=   attention_dropout
									snake_case      : Union[str, Any]							=   max_position_embeddings
									snake_case      : Dict							=   initializer_range
									snake_case      : str							=   None
									snake_case      : Optional[Any]							=   0
									snake_case      : Dict							=   2
									snake_case      : List[str]							=   1



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

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

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



			def 							__lowercase				( self       :							str		)       ->				List[str]:
									snake_case						, snake_case						, snake_case      : List[Any]							=   self.prepare_config_and_inputs()

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

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



			def 							__lowercase				( self       :							List[Any]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							Any      ,  _lowercase       :							Dict      ,  _lowercase       :							Optional[int]		)       ->				str:
									snake_case      : Dict							=   20
									snake_case      : Dict							=   model_class_name(SCREAMING_SNAKE_CASE_		)

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

									snake_case      : Any							=   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=SCREAMING_SNAKE_CASE_      ,  past_key_values=SCREAMING_SNAKE_CASE_      ,  position_ids=SCREAMING_SNAKE_CASE_      ,  )

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

									snake_case      : Any							=   model(SCREAMING_SNAKE_CASE_		)

									snake_case      : 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 							__lowercase				( self       :							int      ,  _lowercase       :							Tuple      ,  _lowercase       :							str      ,  _lowercase       :							Optional[Any]      ,  _lowercase       :							Any		)       ->				int:
									snake_case      : str							=   20
									snake_case      : Optional[int]							=   model_class_name(SCREAMING_SNAKE_CASE_		)

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

									snake_case      : int							=   model.init_cache(input_ids.shape[0]      ,  SCREAMING_SNAKE_CASE_		)
									snake_case      : int							=   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=SCREAMING_SNAKE_CASE_      ,  past_key_values=SCREAMING_SNAKE_CASE_      ,  position_ids=SCREAMING_SNAKE_CASE_      ,  )
									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=SCREAMING_SNAKE_CASE_      ,  position_ids=SCREAMING_SNAKE_CASE_      ,  )

									snake_case      : Optional[Any]							=   model(SCREAMING_SNAKE_CASE_      ,  attention_mask=SCREAMING_SNAKE_CASE_		)
									snake_case      : Optional[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}'''		)



@require_sentencepiece
@require_flax
class 			_a     (   lowerCamelCase__    ,  lowerCamelCase__    ,  unittest.TestCase):
			__magic_name__    				=    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			__magic_name__    				=    (FlaxXGLMForCausalLM,) if is_flax_available() else ()



			def 							__lowercase				( self       :							Optional[int]		)       ->				Dict:
									snake_case      : Optional[Any]							=   FlaxXGLMModelTester(self		)



			def 							__lowercase				( self       :							List[Any]		)       ->				int:
									for model_class_name in self.all_model_classes:
															snake_case						, snake_case						, snake_case      : Tuple							=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_		)



			def 							__lowercase				( self       :							Any		)       ->				int:
									for model_class_name in self.all_model_classes:
															snake_case						, snake_case						, snake_case      : List[str]							=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_		)



			@slow
			def 							__lowercase				( self       :							Union[str, Any]		)       ->				Any:
									snake_case      : Optional[Any]							=   XGLMTokenizer.from_pretrained("XGLM"      ,  padding_side="left"		)
									snake_case      : Tuple							=   tokenizer(["Hello this is a long string", "Hey"]      ,  return_tensors="np"      ,  padding=SCREAMING_SNAKE_CASE_      ,  truncation=SCREAMING_SNAKE_CASE_		)

									snake_case      : int							=   FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
									snake_case      : Any							=   1
									snake_case      : Dict							=   False

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

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

									snake_case      : Tuple							=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      ,  skip_special_tokens=SCREAMING_SNAKE_CASE_		)

									snake_case      : int							=   [
									    "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(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_		)



			@is_pt_flax_cross_test
			def 							__lowercase				( self       :							int		)       ->				List[str]:
									snake_case						, snake_case      : 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
																					snake_case      : Tuple							=   self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,  SCREAMING_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      : Optional[int]							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					snake_case      : Tuple							=   getattr(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_		)

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

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

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

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

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



			@is_pt_flax_cross_test
			def 							__lowercase				( self       :							List[str]		)       ->				Optional[Any]:
									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      : str							=   self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_		)
																					snake_case      : Union[str, Any]							=   {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

																					snake_case      : Optional[Any]							=   pt_model_class(SCREAMING_SNAKE_CASE_		).eval()
																					snake_case      : Dict							=   False
																					snake_case      : List[str]							=   model_class(SCREAMING_SNAKE_CASE_      ,  dtype=jnp.floataa		)

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

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

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

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(SCREAMING_SNAKE_CASE_		)
																											snake_case      : List[Any]							=   pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_      ,  from_flax=SCREAMING_SNAKE_CASE_		)

																					with torch.no_grad():
																											snake_case      : Optional[Any]							=   pt_model_loaded(**SCREAMING_SNAKE_CASE_		).to_tuple()

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



			@slow
			def 							__lowercase				( self       :							int		)       ->				Any:
									for model_class_name in self.all_model_classes:
															snake_case      : Union[str, Any]							=   model_class_name.from_pretrained("facebook/xglm-564M"		)
															snake_case      : str							=   model(np.ones((1, 1)		)		)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)








"""simple docstring"""







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


A    =	logging.get_logger(__name__)

A    =	{
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """yoso"""



			def __init__( self       :							List[str]      ,  _lowercase       :							List[str]=50265      ,  _lowercase       :							List[str]=768      ,  _lowercase       :							Optional[int]=12      ,  _lowercase       :							str=12      ,  _lowercase       :							List[Any]=3072      ,  _lowercase       :							Dict="gelu"      ,  _lowercase       :							int=0.1      ,  _lowercase       :							Optional[Any]=0.1      ,  _lowercase       :							Dict=4096      ,  _lowercase       :							Optional[Any]=1      ,  _lowercase       :							Any=0.02      ,  _lowercase       :							Union[str, Any]=1E-12      ,  _lowercase       :							int="absolute"      ,  _lowercase       :							Any=True      ,  _lowercase       :							Dict=9      ,  _lowercase       :							int=64      ,  _lowercase       :							Any=None      ,  _lowercase       :							List[str]=True      ,  _lowercase       :							Optional[Any]=True      ,  _lowercase       :							Any=1      ,  _lowercase       :							int=0      ,  _lowercase       :							List[str]=2      ,  **_lowercase       :							List[Any]      ,  )       ->				Union[str, Any]:
									super().__init__(pad_token_id=_lowercase      ,  bos_token_id=_lowercase      ,  eos_token_id=_lowercase      ,  **_lowercase		)

									snake_case      : List[str]							=   vocab_size
									snake_case      : Tuple							=   max_position_embeddings
									snake_case      : Optional[Any]							=   hidden_size
									snake_case      : Tuple							=   num_hidden_layers
									snake_case      : Tuple							=   num_attention_heads
									snake_case      : Any							=   intermediate_size
									snake_case      : Union[str, Any]							=   hidden_act
									snake_case      : int							=   hidden_dropout_prob
									snake_case      : Dict							=   attention_probs_dropout_prob
									snake_case      : Any							=   initializer_range
									snake_case      : Optional[Any]							=   type_vocab_size
									snake_case      : List[str]							=   layer_norm_eps
									snake_case      : Dict							=   position_embedding_type
									snake_case      : Tuple							=   use_expectation
									snake_case      : Dict							=   hash_code_len
									snake_case      : Optional[Any]							=   num_hash
									snake_case      : List[str]							=   conv_window
									snake_case      : Optional[Any]							=   use_fast_hash
									snake_case      : Tuple							=   lsh_backward



