





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    ,							UpperCAmelCase    ,							UpperCAmelCase=14    ,							UpperCAmelCase=7    ,							UpperCAmelCase=True    ,							UpperCAmelCase=True    ,							UpperCAmelCase=True    ,							UpperCAmelCase=99    ,							UpperCAmelCase=32    ,							UpperCAmelCase=5    ,							UpperCAmelCase=4    ,							UpperCAmelCase=37    ,							UpperCAmelCase="gelu"    ,							UpperCAmelCase=0.1    ,							UpperCAmelCase=0.1    ,							UpperCAmelCase=5_12    ,							UpperCAmelCase=0.02    ,							UpperCAmelCase=None    ,							)->   Dict:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   Any:
     __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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   Optional[int]:
     __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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   int:
     __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(		_a						,       _a						,       unittest.TestCase):

  """simple docstring"""







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

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     __A    =  FlaxXGLMModelTester(self      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[Any]:
     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 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     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 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   Any:
     __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 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     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      )








import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_UpperCamelCase       :				Tuple           =			logging.get_logger(__name__)







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def __init__(   self    ,							*UpperCAmelCase    ,							**UpperCAmelCase      )->   None:
     warnings.warn(
         '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use DetrImageProcessor instead.'''    ,							UpperCAmelCase    ,							)
     super().__init__(*UpperCAmelCase    ,							**UpperCAmelCase      )

