"""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   __snake_case			:



      def __init__(		self       ,     lowercase       ,     lowercase=14       ,     lowercase=7       ,     lowercase=True       ,     lowercase=True       ,     lowercase=True       ,     lowercase=99       ,     lowercase=32       ,     lowercase=5       ,     lowercase=4       ,     lowercase=37       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=0.02       ,     lowercase=None       ,     )      ->     Any:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      np.clip(ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size)       ,     3       ,     self.vocab_size)

        a__:	Union[str, Any]     				=      None
        if self.use_input_mask:
          a__:	Any     				=      random_attention_mask([self.batch_size, self.seq_length])

        a__:	Optional[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=lowercase       ,     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[Any]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self)      ->     Union[str, Any]:





        '''simple docstring'''





        a__				,       a__				,       a__:	Dict     				=      self.prepare_config_and_inputs()

        a__:	int     				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        a__:	int     				=      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       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	str     				=      20
        a__:	Optional[Any]     				=      model_class_name(lowercase)

        a__:	List[Any]     				=      model.init_cache(input_ids.shape[0]       ,     lowercase)
        a__:	List[Any]     				=      jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype='i4')

        a__:	Tuple     				=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1))
        a__:	str     				=      model(
            input_ids[:, :-1]       ,     attention_mask=lowercase       ,     past_key_values=lowercase       ,     position_ids=lowercase       ,     )

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

        a__:	Optional[int]     				=      model(lowercase)

        a__:	Dict     				=      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       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Any     				=      20
        a__:	Any     				=      model_class_name(lowercase)

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

        a__:	Optional[int]     				=      model.init_cache(input_ids.shape[0]       ,     lowercase)
        a__:	Tuple     				=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1))

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

        a__:	Union[str, Any]     				=      model(lowercase       ,     attention_mask=lowercase)
        a__:	Optional[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}')







@require_sentencepiece
@require_flax
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      a__								=						(FlaxXGLMForCausalLM,) if is_flax_available() else ()



      def     lowerCamelCase_  (		self)      ->     Union[str, Any]:





        '''simple docstring'''





        a__:	List[Any]     				=      FlaxXGLMModelTester(self)



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__				,       a__				,       a__:	Dict     				=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowercase       ,     lowercase       ,     lowercase       ,     lowercase)



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__				,       a__				,       a__:	List[str]     				=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowercase       ,     lowercase       ,     lowercase       ,     lowercase)



      @slow
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      XGLMTokenizer.from_pretrained('XGLM'       ,     padding_side='left')
        a__:	int     				=      tokenizer(['Hello this is a long string', 'Hey']       ,     return_tensors='np'       ,     padding=lowercase       ,     truncation=lowercase)

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

        a__:	Tuple     				=      jax.jit(model.generate)

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

        a__:	Optional[Any]     				=      tokenizer.batch_decode(lowercase       ,     skip_special_tokens=lowercase)

        a__:	Dict     				=      [
            '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(lowercase       ,     lowercase)



      @is_pt_flax_cross_test
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__				,       a__:	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
            a__:	str     				=      self._prepare_for_class(lowercase       ,     lowercase)
            a__:	List[str]     				=      {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

            with torch.no_grad():
              a__:	Optional[int]     				=      pt_model(**lowercase).to_tuple()

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

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

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



      @is_pt_flax_cross_test
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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

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

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

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

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

            with torch.no_grad():
              a__:	Tuple     				=      pt_model(**lowercase).to_tuple()

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

            with tempfile.TemporaryDirectory() as tmpdirname:
              fx_model.save_pretrained(lowercase)
              a__:	int     				=      pt_model_class.from_pretrained(lowercase       ,     from_flax=lowercase)

            with torch.no_grad():
              a__:	Dict     				=      pt_model_loaded(**lowercase).to_tuple()

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



      @slow
      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__:	Dict     				=      model_class_name.from_pretrained('facebook/xglm-564M')
          a__:	Tuple     				=      model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)



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

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


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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     '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:
 lowercase__   			=      [
     '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

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


