




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      UpperCAmelCase						:






   def __init__(	self   :       List[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Union[str, Any]=14						,					UpperCamelCase_   :       List[str]=7						,					UpperCamelCase_   :       List[str]=True						,					UpperCamelCase_   :       List[str]=True						,					UpperCamelCase_   :       Union[str, Any]=True						,					UpperCamelCase_   :       Optional[Any]=99						,					UpperCamelCase_   :       Optional[Any]=32						,					UpperCamelCase_   :       Any=5						,					UpperCamelCase_   :       Optional[int]=4						,					UpperCamelCase_   :       List[Any]=37						,					UpperCamelCase_   :       int="gelu"						,					UpperCamelCase_   :       Optional[Any]=0.1						,					UpperCamelCase_   :       Union[str, Any]=0.1						,					UpperCamelCase_   :       Any=5_12						,					UpperCamelCase_   :       Tuple=0.02						,					UpperCamelCase_   :       Any=None						,					)->			Dict:
     _lowercase: Tuple						     =    parent
     _lowercase: Dict						     =    batch_size
     _lowercase: Optional[int]						     =    seq_length
     _lowercase: Tuple						     =    is_training
     _lowercase: Tuple						     =    use_input_mask
     _lowercase: Optional[Any]						     =    use_labels
     _lowercase: List[Any]						     =    vocab_size
     _lowercase: Dict						     =    d_model
     _lowercase: Optional[Any]						     =    num_hidden_layers
     _lowercase: Optional[Any]						     =    num_attention_heads
     _lowercase: Tuple						     =    ffn_dim
     _lowercase: List[str]						     =    activation_function
     _lowercase: Union[str, Any]						     =    activation_dropout
     _lowercase: int						     =    attention_dropout
     _lowercase: str						     =    max_position_embeddings
     _lowercase: Any						     =    initializer_range
     _lowercase: str						     =    None
     _lowercase: List[Any]						     =    0
     _lowercase: Any						     =    2
     _lowercase: Optional[Any]						     =    1






   def  _a						(	self   :       List[str]     )->			List[Any]:
     _lowercase: Union[str, Any]						     =    np.clip(ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size     )						,					3						,					self.vocab_size     )

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

     _lowercase: int						     =    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  _a						(	self   :       Optional[Any]     )->			int:
     _lowercase: Optional[Any]						     =    self.prepare_config_and_inputs()
     _lowercase: Union[str, Any]						     =    config_and_inputs
     _lowercase: Optional[int]						     =    {'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict






   def  _a						(	self   :       Optional[Any]     )->			Union[str, Any]:
     _lowercase: Dict						     =    self.prepare_config_and_inputs()

     _lowercase: Union[str, Any]						     =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
     _lowercase: Dict						     =    ids_tensor([self.batch_size, self.seq_length]						,					vocab_size=2     )

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






   def  _a						(	self   :       Dict						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       Optional[int]     )->			Optional[Any]:
     _lowercase: Dict						     =    20
     _lowercase: List[str]						     =    model_class_name(UpperCamelCase_     )

     _lowercase: str						     =    model.init_cache(input_ids.shape[0]						,					UpperCamelCase_     )
     _lowercase: List[str]						     =    jnp.ones((input_ids.shape[0], max_decoder_length)						,					dtype='i4'     )

     _lowercase: str						     =    jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)     )
     _lowercase: int						     =    model(
         input_ids[:, :-1]						,					attention_mask=UpperCamelCase_						,					past_key_values=UpperCamelCase_						,					position_ids=UpperCamelCase_						,					)

     _lowercase: Dict						     =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype='i4'     )
     _lowercase: Union[str, Any]						     =    model(
         input_ids[:, -1:]						,					attention_mask=UpperCamelCase_						,					past_key_values=outputs_cache.past_key_values						,					position_ids=UpperCamelCase_						,					)

     _lowercase: Optional[int]						     =    model(UpperCamelCase_     )

     _lowercase: 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  _a						(	self   :       Dict						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       List[str]     )->			Dict:
     _lowercase: str						     =    20
     _lowercase: Any						     =    model_class_name(UpperCamelCase_     )

     _lowercase: List[str]						     =    jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]						,					axis=-1						,					)

     _lowercase: str						     =    model.init_cache(input_ids.shape[0]						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)     )

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

     _lowercase: Dict						     =    model(UpperCamelCase_						,					attention_mask=UpperCamelCase_     )
     _lowercase: 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      UpperCAmelCase						(			A   ,					A   ,					unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							List[str]							=					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   __SCREAMING_SNAKE_CASE		:							Any							=					(FlaxXGLMForCausalLM,) if is_flax_available() else ()






   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     _lowercase: Tuple						     =    FlaxXGLMModelTester(self     )






   def  _a						(	self   :       str     )->			Optional[Any]:
     for model_class_name in self.all_model_classes:
       _lowercase: List[Any]						     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       Optional[int]     )->			Dict:
     for model_class_name in self.all_model_classes:
       _lowercase: Dict						     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )






   @slow
   def  _a						(	self   :       Optional[Any]     )->			int:
     _lowercase: Dict						     =    XGLMTokenizer.from_pretrained('XGLM'						,					padding_side='left'     )
     _lowercase: Dict						     =    tokenizer(['Hello this is a long string', 'Hey']						,					return_tensors='np'						,					padding=UpperCamelCase_						,					truncation=UpperCamelCase_     )

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

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

     _lowercase: Union[str, Any]						     =    jit_generate(inputs['input_ids']						,					attention_mask=inputs['attention_mask']     ).sequences

     _lowercase: Optional[int]						     =    tokenizer.batch_decode(UpperCamelCase_						,					skip_special_tokens=UpperCamelCase_     )

     _lowercase: Tuple						     =    [
         '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  _a						(	self   :       Optional[int]     )->			Union[str, Any]:
     _lowercase: List[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
         _lowercase: str						     =    self._prepare_for_class(UpperCamelCase_						,					UpperCamelCase_     )
         _lowercase: Optional[int]						     =    {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

         with torch.no_grad():
           _lowercase: Optional[int]						     =    pt_model(**UpperCamelCase_     ).to_tuple()

         _lowercase: Union[str, Any]						     =    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_     )
           _lowercase: Any						     =    model_class.from_pretrained(UpperCamelCase_						,					from_pt=UpperCamelCase_     )

         _lowercase: List[Any]						     =    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  _a						(	self   :       int     )->			Optional[Any]:
     _lowercase: List[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
         _lowercase: List[str]						     =    self._prepare_for_class(UpperCamelCase_						,					UpperCamelCase_     )
         _lowercase: List[Any]						     =    {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

         _lowercase: Dict						     =    pt_model_class(UpperCamelCase_     ).eval()
         _lowercase: Tuple						     =    False
         _lowercase: Optional[int]						     =    model_class(UpperCamelCase_						,					dtype=jnp.floataa     )

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

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

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

         _lowercase: Tuple						     =    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_     )
           _lowercase: Optional[int]						     =    pt_model_class.from_pretrained(UpperCamelCase_						,					from_flax=UpperCamelCase_     )

         with torch.no_grad():
           _lowercase: Optional[Any]						     =    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  _a						(	self   :       Dict     )->			Optional[int]:
     for model_class_name in self.all_model_classes:
       _lowercase: List[Any]						     =    model_class_name.from_pretrained('facebook/xglm-564M'     )
       _lowercase: str						     =    model(np.ones((1, 1)     )     )
       self.assertIsNotNone(UpperCamelCase_     )






from typing import TYPE_CHECKING

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


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
