
"""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  :List[str]					,							lowerCamelCase_  :Tuple					,							lowerCamelCase_  :Union[str, Any]=14					,							lowerCamelCase_  :int=7					,							lowerCamelCase_  :List[Any]=True					,							lowerCamelCase_  :Tuple=True					,							lowerCamelCase_  :Optional[int]=True					,							lowerCamelCase_  :Tuple=99					,							lowerCamelCase_  :List[str]=32					,							lowerCamelCase_  :Dict=5					,							lowerCamelCase_  :int=4					,							lowerCamelCase_  :Optional[Any]=37					,							lowerCamelCase_  :Dict="gelu"					,							lowerCamelCase_  :Tuple=0.1					,							lowerCamelCase_  :Optional[int]=0.1					,							lowerCamelCase_  :str=512					,							lowerCamelCase_  :Union[str, Any]=0.02					,							lowerCamelCase_  :int=None					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=parent
										lowerCamelCase__     :					List[Any]		=batch_size
										lowerCamelCase__     :					Dict		=seq_length
										lowerCamelCase__     :					Optional[int]		=is_training
										lowerCamelCase__     :					List[str]		=use_input_mask
										lowerCamelCase__     :					List[str]		=use_labels
										lowerCamelCase__     :					List[Any]		=vocab_size
										lowerCamelCase__     :					Any		=d_model
										lowerCamelCase__     :					str		=num_hidden_layers
										lowerCamelCase__     :					Tuple		=num_attention_heads
										lowerCamelCase__     :					Tuple		=ffn_dim
										lowerCamelCase__     :					Any		=activation_function
										lowerCamelCase__     :					Union[str, Any]		=activation_dropout
										lowerCamelCase__     :					Dict		=attention_dropout
										lowerCamelCase__     :					List[Any]		=max_position_embeddings
										lowerCamelCase__     :					Optional[int]		=initializer_range
										lowerCamelCase__     :					Tuple		=None
										lowerCamelCase__     :					List[str]		=0
										lowerCamelCase__     :					Any		=2
										lowerCamelCase__     :					Any		=1





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					str		=np.clip(ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size	)					,							3					,							self.vocab_size	)

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

										lowerCamelCase__     :					Union[str, 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=lowerCamelCase_					,							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 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=self.prepare_config_and_inputs()
										lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__     :					Any		=config_and_inputs
										lowerCamelCase__     :					List[str]		={'input_ids': input_ids, 'attention_mask': attention_mask}
										return config, inputs_dict





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__     :					List[str]		=self.prepare_config_and_inputs()

										lowerCamelCase__     :					str		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
										lowerCamelCase__     :					str		=ids_tensor([self.batch_size, self.seq_length]					,							vocab_size=2	)

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





							def 				UpperCAmelCase__							(  self  :Optional[int]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :int					,							lowerCamelCase_  :int					,							lowerCamelCase_  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=20
										lowerCamelCase__     :					Union[str, Any]		=model_class_name(lowerCamelCase_	)

										lowerCamelCase__     :					Tuple		=model.init_cache(input_ids.shape[0]					,							lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=jnp.ones((input_ids.shape[0], max_decoder_length)					,							dtype='i4'	)

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

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

										lowerCamelCase__     :					Any		=model(lowerCamelCase_	)

										lowerCamelCase__     :					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 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Tuple					,							lowerCamelCase_  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=20
										lowerCamelCase__     :					Optional[Any]		=model_class_name(lowerCamelCase_	)

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

										lowerCamelCase__     :					Union[str, Any]		=model.init_cache(input_ids.shape[0]					,							lowerCamelCase_	)
										lowerCamelCase__     :					Optional[int]		=jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1	)[None, :]					,							(input_ids.shape[0], input_ids.shape[-1] - 1)	)

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

										lowerCamelCase__     :					Tuple		=model(lowerCamelCase_					,							attention_mask=lowerCamelCase_	)
										lowerCamelCase__     :					Union[str, 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_     (					A__				, A__				, unittest.TestCase       ):



							"""simple docstring"""






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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		=FlaxXGLMModelTester(self	)





							def 				UpperCAmelCase__							(  self  :int	):





										"""simple docstring"""

										for model_class_name in self.all_model_classes:
													lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__     :					Optional[Any]		=self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										for model_class_name in self.all_model_classes:
													lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__     :					Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)





							@slow
							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=XGLMTokenizer.from_pretrained('XGLM'					,							padding_side='left'	)
										lowerCamelCase__     :					Dict		=tokenizer(['Hello this is a long string', 'Hey']					,							return_tensors='np'					,							padding=lowerCamelCase_					,							truncation=lowerCamelCase_	)

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

										lowerCamelCase__     :					Union[str, Any]		=jax.jit(model.generate	)

										lowerCamelCase__     :					Optional[Any]		=jit_generate(inputs['input_ids']					,							attention_mask=inputs['attention_mask']	).sequences

										lowerCamelCase__     :					Tuple		=tokenizer.batch_decode(lowerCamelCase_					,							skip_special_tokens=lowerCamelCase_	)

										lowerCamelCase__     :					Optional[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(lowerCamelCase_					,							lowerCamelCase_	)





							@is_pt_flax_cross_test
							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__	,    lowerCamelCase__     :					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
																lowerCamelCase__     :					Tuple		=self._prepare_for_class(lowerCamelCase_					,							lowerCamelCase_	)
																lowerCamelCase__     :					List[Any]		={k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

																with torch.no_grad():
																			lowerCamelCase__     :					Optional[Any]		=pt_model(**lowerCamelCase_	).to_tuple()

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

																with tempfile.TemporaryDirectory() as tmpdirname:
																			pt_model.save_pretrained(lowerCamelCase_	)
																			lowerCamelCase__     :					int		=model_class.from_pretrained(lowerCamelCase_					,							from_pt=lowerCamelCase_	)

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





							@is_pt_flax_cross_test
							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__	,    lowerCamelCase__     :					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
																lowerCamelCase__     :					Optional[Any]		=self._prepare_for_class(lowerCamelCase_					,							lowerCamelCase_	)
																lowerCamelCase__     :					Union[str, Any]		={k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

																lowerCamelCase__     :					Optional[int]		=pt_model_class(lowerCamelCase_	).eval()
																lowerCamelCase__     :					Any		=False
																lowerCamelCase__     :					List[Any]		=model_class(lowerCamelCase_					,							dtype=jnp.floataa	)

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

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

																with torch.no_grad():
																			lowerCamelCase__     :					Any		=pt_model(**lowerCamelCase_	).to_tuple()

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

																with tempfile.TemporaryDirectory() as tmpdirname:
																			fx_model.save_pretrained(lowerCamelCase_	)
																			lowerCamelCase__     :					List[str]		=pt_model_class.from_pretrained(lowerCamelCase_					,							from_flax=lowerCamelCase_	)

																with torch.no_grad():
																			lowerCamelCase__     :					str		=pt_model_loaded(**lowerCamelCase_	).to_tuple()

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





							@slow
							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										for model_class_name in self.all_model_classes:
													lowerCamelCase__     :					str		=model_class_name.from_pretrained('facebook/xglm-564M'	)
													lowerCamelCase__     :					str		=model(np.ones((1, 1)	)	)
													self.assertIsNotNone(lowerCamelCase_	)

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

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