



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 		lowercase						:






	"""simple docstring"""


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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Tuple:
							UpperCAmelCase_=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     )						,  3						,  self.vocab_size     )

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

							UpperCAmelCase_=   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=__UpperCAmelCase						,  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	:						List[str]     )			-> int:
							UpperCAmelCase_=   self.prepare_config_and_inputs()
							UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   config_and_inputs
							UpperCAmelCase_=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
							return config, inputs_dict




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_config_and_inputs()

							UpperCAmelCase_=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
							UpperCAmelCase_=   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	:						Tuple						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						Optional[int]						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						Tuple     )			-> List[Any]:
							UpperCAmelCase_=   20
							UpperCAmelCase_=   model_class_name(__UpperCAmelCase     )

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

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

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

							UpperCAmelCase_=   model(__UpperCAmelCase     )

							UpperCAmelCase_=   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	:						Dict						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						List[Any]						,  __UpperCAmelCase	:						int     )			-> Tuple:
							UpperCAmelCase_=   20
							UpperCAmelCase_=   model_class_name(__UpperCAmelCase     )

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

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

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

							UpperCAmelCase_=   model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase     )
							UpperCAmelCase_=   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 		lowercase						( snake_case__ ,    snake_case__ ,    unittest.TestCase):






	"""simple docstring"""


	a__      :					Dict		   =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
	a__      :					Dict		   =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Optional[Any]:
							UpperCAmelCase_=   FlaxXGLMModelTester(self     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Optional[Any]:
							for model_class_name in self.all_model_classes:
													UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> int:
							for model_class_name in self.all_model_classes:
													UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Tuple:
							UpperCAmelCase_=   XGLMTokenizer.from_pretrained("""XGLM"""						,  padding_side="""left"""     )
							UpperCAmelCase_=   tokenizer(["""Hello this is a long string""", """Hey"""]						,  return_tensors="""np"""						,  padding=__UpperCAmelCase						,  truncation=__UpperCAmelCase     )

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

							UpperCAmelCase_=   jax.jit(model.generate     )

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

							UpperCAmelCase_=   tokenizer.batch_decode(__UpperCAmelCase						,  skip_special_tokens=__UpperCAmelCase     )

							UpperCAmelCase_=   [
							    """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(__UpperCAmelCase						,  __UpperCAmelCase     )




	@is_pt_flax_cross_test
	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Union[str, Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   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_=   self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )
																			UpperCAmelCase_=   {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																			UpperCAmelCase_,   UpperCAmelCase_=   pt_inputs["""input_ids"""].shape
																			UpperCAmelCase_=   np.random.randint(0						,  seq_length - 1						,  size=(batch_size,)     )
																			for batch_idx, start_index in enumerate(__UpperCAmelCase     ):
																									UpperCAmelCase_=   0
																									UpperCAmelCase_=   1
																									UpperCAmelCase_=   0
																									UpperCAmelCase_=   1
																			UpperCAmelCase_=   pt_model_class(__UpperCAmelCase     ).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_=   False
																			UpperCAmelCase_=   model_class(__UpperCAmelCase						,  dtype=jnp.floataa     )
																			UpperCAmelCase_=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,  __UpperCAmelCase     )
																			UpperCAmelCase_=   fx_state

																			with torch.no_grad():
																									UpperCAmelCase_=   pt_model(**__UpperCAmelCase     ).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(__UpperCAmelCase     )
																									UpperCAmelCase_=   model_class.from_pretrained(__UpperCAmelCase						,  from_pt=__UpperCAmelCase     )

																			UpperCAmelCase_=   fx_model_loaded(**__UpperCAmelCase     ).to_tuple()
																			self.assertEqual(
																			    len(__UpperCAmelCase     )						,  len(__UpperCAmelCase     )						,  """Output lengths differ between Flax and PyTorch"""     )
																			for fx_output_loaded, pt_output in zip(__UpperCAmelCase						,  __UpperCAmelCase     ):
																									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     )			-> Any:
							UpperCAmelCase_,   UpperCAmelCase_=   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_=   self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )
																			UpperCAmelCase_=   {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																			UpperCAmelCase_=   pt_model_class(__UpperCAmelCase     ).eval()
																			UpperCAmelCase_=   False
																			UpperCAmelCase_=   model_class(__UpperCAmelCase						,  dtype=jnp.floataa     )

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

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

																			with torch.no_grad():
																									UpperCAmelCase_=   pt_model(**__UpperCAmelCase     ).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(__UpperCAmelCase     )
																									UpperCAmelCase_=   pt_model_class.from_pretrained(__UpperCAmelCase						,  from_flax=__UpperCAmelCase     )

																			with torch.no_grad():
																									UpperCAmelCase_=   pt_model_loaded(**__UpperCAmelCase     ).to_tuple()

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




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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						['''MPNetTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

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




