





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							,    snake_case_							,    snake_case_=14							,    snake_case_=7							,    snake_case_=True							,    snake_case_=True							,    snake_case_=True							,    snake_case_=99							,    snake_case_=32							,    snake_case_=5							,    snake_case_=4							,    snake_case_=37							,    snake_case_="gelu"							,    snake_case_=0.1							,    snake_case_=0.1							,    snake_case_=512							,    snake_case_=0.02							,    snake_case_=None							,    ):
         _A    =       parent
         _A    =       batch_size
         _A    =       seq_length
         _A    =       is_training
         _A    =       use_input_mask
         _A    =       use_labels
         _A    =       vocab_size
         _A    =       d_model
         _A    =       num_hidden_layers
         _A    =       num_attention_heads
         _A    =       ffn_dim
         _A    =       activation_function
         _A    =       activation_dropout
         _A    =       attention_dropout
         _A    =       max_position_embeddings
         _A    =       initializer_range
         _A    =       None
         _A    =       0
         _A    =       2
         _A    =       1




     def 					lowerCAmelCase__		(						self       ):
         _A    =       np.clip(ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size       )							,    3							,    self.vocab_size       )

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

         _A    =       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 					lowerCAmelCase__		(						self       ):
         _A    =       self.prepare_config_and_inputs()
         _A, _A, _A    =       config_and_inputs
         _A    =       {'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict




     def 					lowerCAmelCase__		(						self       ):
         _A, _A, _A    =       self.prepare_config_and_inputs()

         _A    =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         _A    =       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							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       20
         _A    =       model_class_name(__UpperCamelCase       )

         _A    =       model.init_cache(input_ids.shape[0]							,    __UpperCamelCase       )
         _A    =       jnp.ones((input_ids.shape[0], max_decoder_length)							,    dtype='i4'       )

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

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

         _A    =       model(__UpperCamelCase       )

         _A    =       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							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       20
         _A    =       model_class_name(__UpperCamelCase       )

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

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

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

         _A    =       model(__UpperCamelCase							,    attention_mask=__UpperCamelCase       )
         _A    =       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(	UpperCAmelCase_ ,   UpperCAmelCase_ ,   unittest.TestCase  ):
     '''simple docstring'''


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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       FlaxXGLMModelTester(self       )




     def 					lowerCAmelCase__		(						self       ):
         for model_class_name in self.all_model_classes:
             _A, _A, _A    =       self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )




     def 					lowerCAmelCase__		(						self       ):
         for model_class_name in self.all_model_classes:
             _A, _A, _A    =       self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       XGLMTokenizer.from_pretrained('XGLM'							,    padding_side='left'       )
         _A    =       tokenizer(['Hello this is a long string', 'Hey']							,    return_tensors='np'							,    padding=__UpperCamelCase							,    truncation=__UpperCamelCase       )

         _A    =       FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
         _A    =       1
         _A    =       False

         _A    =       jax.jit(model.generate       )

         _A    =       jit_generate(inputs['input_ids']							,    attention_mask=inputs['attention_mask']       ).sequences

         _A    =       tokenizer.batch_decode(__UpperCamelCase							,    skip_special_tokens=__UpperCamelCase       )

         _A    =       [
             '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 					lowerCAmelCase__		(						self       ):
         _A, _A    =       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    =       self._prepare_for_class(__UpperCamelCase							,    __UpperCamelCase       )
                 _A    =       {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

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

                 with torch.no_grad():
                     _A    =       pt_model(**__UpperCamelCase       ).to_tuple()

                 _A    =       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       )
                     _A    =       model_class.from_pretrained(__UpperCamelCase							,    from_pt=__UpperCamelCase       )

                 _A    =       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 					lowerCAmelCase__		(						self       ):
         _A, _A    =       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    =       self._prepare_for_class(__UpperCamelCase							,    __UpperCamelCase       )
                 _A    =       {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

                 _A    =       pt_model_class(__UpperCamelCase       ).eval()
                 _A    =       False
                 _A    =       model_class(__UpperCamelCase							,    dtype=jnp.floataa       )

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

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

                 with torch.no_grad():
                     _A    =       pt_model(**__UpperCamelCase       ).to_tuple()

                 _A    =       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       )
                     _A    =       pt_model_class.from_pretrained(__UpperCamelCase							,    from_flax=__UpperCamelCase       )

                 with torch.no_grad():
                     _A    =       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 					lowerCAmelCase__		(						self       ):
         for model_class_name in self.all_model_classes:
             _A    =       model_class_name.from_pretrained('facebook/xglm-564M'       )
             _A    =       model(np.ones((1, 1)       )       )
             self.assertIsNotNone(__UpperCamelCase       )












from typing import TYPE_CHECKING

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


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : List[str]						    = [
     "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "HubertForCTC",
     "HubertForSequenceClassification",
     "HubertModel",
     "HubertPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFHubertForCTC",
     "TFHubertModel",
     "TFHubertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





