


'''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 _lowerCamelCase :



		'''simple docstring'''







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







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

			__magic_name__ :					Dict  			=  None
			if self.use_input_mask:
				__magic_name__ :					List[str]  			=  random_attention_mask([self.batch_size, self.seq_length]    )

			__magic_name__ :					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=_A						,  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	:		Any    )						->				Dict:
			__magic_name__ :					List[Any]  			=  self.prepare_config_and_inputs()
			__magic_name__			,       __magic_name__			,       __magic_name__ :					int  			=  config_and_inputs
			__magic_name__ :					Dict  			=  {'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict







		def      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
			__magic_name__			,       __magic_name__			,       __magic_name__ :					Optional[Any]  			=  self.prepare_config_and_inputs()

			__magic_name__ :					Optional[Any]  			=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
			__magic_name__ :					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      __lowerCAmelCase	(      self	:		str						,  _A	:		Tuple						,  _A	:		Tuple						,  _A	:		Union[str, Any]						,  _A	:		Tuple    )						->				Optional[Any]:
			__magic_name__ :					Tuple  			=  20
			__magic_name__ :					Any  			=  model_class_name(_A    )

			__magic_name__ :					Union[str, Any]  			=  model.init_cache(input_ids.shape[0]						,  _A    )
			__magic_name__ :					int  			=  jnp.ones((input_ids.shape[0], max_decoder_length)						,  dtype='i4'    )

			__magic_name__ :					Union[str, Any]  			=  jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1    )[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)    )
			__magic_name__ :					Any  			=  model(
			    input_ids[:, :-1]						,  attention_mask=_A						,  past_key_values=_A						,  position_ids=_A						,  )

			__magic_name__ :					Optional[Any]  			=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,  dtype='i4'    )
			__magic_name__ :					Optional[int]  			=  model(
			    input_ids[:, -1:]						,  attention_mask=_A						,  past_key_values=outputs_cache.past_key_values						,  position_ids=_A						,  )

			__magic_name__ :					Optional[int]  			=  model(_A    )

			__magic_name__ :					List[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}'    )







		def      __lowerCAmelCase	(      self	:		int						,  _A	:		Any						,  _A	:		Tuple						,  _A	:		Optional[Any]						,  _A	:		str    )						->				Optional[int]:
			__magic_name__ :					List[Any]  			=  20
			__magic_name__ :					Optional[int]  			=  model_class_name(_A    )

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

			__magic_name__ :					Any  			=  model.init_cache(input_ids.shape[0]						,  _A    )
			__magic_name__ :					List[Any]  			=  jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1    )[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)    )

			__magic_name__ :					str  			=  model(
			    input_ids[:, :-1]						,  attention_mask=_A						,  past_key_values=_A						,  position_ids=_A						,  )
			__magic_name__ :					Any  			=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,  dtype='i4'    )
			__magic_name__ :					Any  			=  model(
			    input_ids[:, -1:]						,  past_key_values=outputs_cache.past_key_values						,  attention_mask=_A						,  position_ids=_A						,  )

			__magic_name__ :					int  			=  model(_A						,  attention_mask=_A    )
			__magic_name__ :					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_sentencepiece
@require_flax
class _lowerCamelCase (		lowercase__	,  lowercase__	,  unittest.TestCase							):



		'''simple docstring'''







		A_			:    List[Any]   					=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
		A_			:    Tuple   					=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()







		def      __lowerCAmelCase	(      self	:		int    )						->				Tuple:
			__magic_name__ :					List[str]  			=  FlaxXGLMModelTester(self    )







		def      __lowerCAmelCase	(      self	:		int    )						->				Any:
			for model_class_name in self.all_model_classes:
				__magic_name__			,       __magic_name__			,       __magic_name__ :					Optional[int]  			=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.check_use_cache_forward(_A						,  _A						,  _A						,  _A    )







		def      __lowerCAmelCase	(      self	:		Any    )						->				Any:
			for model_class_name in self.all_model_classes:
				__magic_name__			,       __magic_name__			,       __magic_name__ :					Union[str, Any]  			=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.check_use_cache_forward_with_attn_mask(
				    _A						,  _A						,  _A						,  _A    )







		@slow
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[Any]:
			__magic_name__ :					str  			=  XGLMTokenizer.from_pretrained('XGLM'						,  padding_side='left'    )
			__magic_name__ :					Optional[int]  			=  tokenizer(['Hello this is a long string', 'Hey']						,  return_tensors='np'						,  padding=_A						,  truncation=_A    )

			__magic_name__ :					List[Any]  			=  FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
			__magic_name__ :					Union[str, Any]  			=  1
			__magic_name__ :					List[str]  			=  False

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

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

			__magic_name__ :					Any  			=  tokenizer.batch_decode(_A						,  skip_special_tokens=_A    )

			__magic_name__ :					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(_A						,  _A    )







		@is_pt_flax_cross_test
		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			__magic_name__			,       __magic_name__ :					Optional[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
					__magic_name__ :					Any  			=  self._prepare_for_class(_A						,  _A    )
					__magic_name__ :					Union[str, Any]  			=  {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

					with torch.no_grad():
						__magic_name__ :					Optional[Any]  			=  pt_model(**_A    ).to_tuple()

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

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

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







		@is_pt_flax_cross_test
		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Any:
			__magic_name__			,       __magic_name__ :					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
					__magic_name__ :					Optional[Any]  			=  self._prepare_for_class(_A						,  _A    )
					__magic_name__ :					int  			=  {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

					# load corresponding PyTorch class
					__magic_name__ :					Tuple  			=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
					__magic_name__ :					Union[str, Any]  			=  getattr(_A						,  _A    )

					__magic_name__ :					int  			=  pt_model_class(_A    ).eval()
					__magic_name__ :					Union[str, Any]  			=  False
					__magic_name__ :					Dict  			=  model_class(_A						,  dtype=jnp.floataa    )

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

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

					with torch.no_grad():
						__magic_name__ :					Optional[Any]  			=  pt_model(**_A    ).to_tuple()

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

					with tempfile.TemporaryDirectory() as tmpdirname:
						fx_model.save_pretrained(_A    )
						__magic_name__ :					Union[str, Any]  			=  pt_model_class.from_pretrained(_A						,  from_flax=_A    )

					with torch.no_grad():
						__magic_name__ :					List[str]  			=  pt_model_loaded(**_A    ).to_tuple()

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







		@slow
		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
			for model_class_name in self.all_model_classes:
				__magic_name__ :					Tuple  			=  model_class_name.from_pretrained('facebook/xglm-564M'    )
				__magic_name__ :					Optional[Any]  			=  model(np.ones((1, 1)    )    )
				self.assertIsNotNone(_A    )



'''simple docstring'''

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


lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)

lowerCAmelCase   :Dict								=    {
    '''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 _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    Optional[Any]   					=       """yoso"""







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

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