



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






					def        lowerCAmelCase_    (	self:							List[str]						)       ->     Tuple:
											snake_case_			:int						=      np.clip(ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size						)						,					3						,					self.vocab_size						)

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

											snake_case_			:List[Any]						=      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=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        lowerCAmelCase_    (	self:							List[str]						)       ->     Any:
											snake_case_			:Union[str, Any]						=      self.prepare_config_and_inputs()
											snake_case_,	snake_case_,	snake_case_			:Optional[Any]						=      config_and_inputs
											snake_case_			:Any						=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[int]:
											snake_case_,	snake_case_,	snake_case_			:Any						=      self.prepare_config_and_inputs()

											snake_case_			:Dict						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											snake_case_			: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:							Optional[Any]						,					snake_case:							List[str]						,					snake_case:							List[str]						,					snake_case:							List[str]						,					snake_case:							str						)       ->     Optional[int]:
											snake_case_			:str						=      20
											snake_case_			:Union[str, Any]						=      model_class_name(snake_case						)

											snake_case_			:Dict						=      model.init_cache(input_ids.shape[0]						,					snake_case						)
											snake_case_			:Tuple						=      jnp.ones((input_ids.shape[0], max_decoder_length)						,					dtype="""i4"""						)

											snake_case_			:Optional[int]						=      jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
											snake_case_			:Any						=      model(
											    input_ids[:, :-1]						,					attention_mask=snake_case						,					past_key_values=snake_case						,					position_ids=snake_case						,					)

											snake_case_			:Optional[int]						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype="""i4"""						)
											snake_case_			:Tuple						=      model(
											    input_ids[:, -1:]						,					attention_mask=snake_case						,					past_key_values=outputs_cache.past_key_values						,					position_ids=snake_case						,					)

											snake_case_			:List[str]						=      model(snake_case						)

											snake_case_			: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						,					snake_case:							str						,					snake_case:							Optional[Any]						,					snake_case:							Optional[Any]						,					snake_case:							int						)       ->     Tuple:
											snake_case_			:Optional[Any]						=      20
											snake_case_			:List[str]						=      model_class_name(snake_case						)

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

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

											snake_case_			:Any						=      model(snake_case						,					attention_mask=snake_case						)
											snake_case_			: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     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					_A		:      Any						=					(FlaxXGLMForCausalLM,) if is_flax_available() else ()






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[str]:
											snake_case_			:List[Any]						=      FlaxXGLMModelTester(self						)






					def        lowerCAmelCase_    (	self:							Union[str, 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(snake_case						,					snake_case						,					snake_case						,					snake_case						)






					def        lowerCAmelCase_    (	self:							int						)       ->     Dict:
											for model_class_name in self.all_model_classes:
																	snake_case_,	snake_case_,	snake_case_			:Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    snake_case						,					snake_case						,					snake_case						,					snake_case						)






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

											snake_case_			:Tuple						=      FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
											snake_case_			:List[Any]						=      1
											snake_case_			:Any						=      False

											snake_case_			:Dict						=      jax.jit(model.generate						)

											snake_case_			:List[Any]						=      jit_generate(inputs["""input_ids"""]						,					attention_mask=inputs["""attention_mask"""]						).sequences

											snake_case_			:List[str]						=      tokenizer.batch_decode(snake_case						,					skip_special_tokens=snake_case						)

											snake_case_			: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(snake_case						,					snake_case						)






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							List[str]						)       ->     Optional[Any]:
											snake_case_,	snake_case_			:Tuple						=      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_			:int						=      self._prepare_for_class(snake_case						,					snake_case						)
																							snake_case_			:Dict						=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

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

																							with torch.no_grad():
																													snake_case_			:Union[str, Any]						=      pt_model(**snake_case						).to_tuple()

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

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

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






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_,	snake_case_			: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_			:Dict						=      self._prepare_for_class(snake_case						,					snake_case						)
																							snake_case_			:int						=      {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_			:str						=      getattr(snake_case						,					snake_case						)

																							snake_case_			:Optional[int]						=      pt_model_class(snake_case						).eval()
																							snake_case_			:Union[str, Any]						=      False
																							snake_case_			:Optional[Any]						=      model_class(snake_case						,					dtype=jnp.floataa						)

																							snake_case_			:List[str]						=      load_flax_weights_in_pytorch_model(snake_case						,					fx_model.params						)
																							snake_case_,	snake_case_			: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(snake_case						):
																													snake_case_			:Dict						=      0
																													snake_case_			:Union[str, Any]						=      1
																													snake_case_			:Dict						=      0
																													snake_case_			:List[str]						=      1

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

																							with torch.no_grad():
																													snake_case_			:str						=      pt_model(**snake_case						).to_tuple()

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

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

																							with torch.no_grad():
																													snake_case_			:Union[str, Any]						=      pt_model_loaded(**snake_case						).to_tuple()

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






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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    ["XLMProphetNetTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

						__a     =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
