






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						,    _lowerCamelCase						,    _lowerCamelCase=1_4						,    _lowerCamelCase=7						,    _lowerCamelCase=True						,    _lowerCamelCase=True						,    _lowerCamelCase=True						,    _lowerCamelCase=9_9						,    _lowerCamelCase=3_2						,    _lowerCamelCase=5						,    _lowerCamelCase=4						,    _lowerCamelCase=3_7						,    _lowerCamelCase="gelu"						,    _lowerCamelCase=0.1						,    _lowerCamelCase=0.1						,    _lowerCamelCase=5_1_2						,    _lowerCamelCase=0.0_2						,    _lowerCamelCase=None						,    ):
         UpperCamelCase_:      Tuple					   =       parent
         UpperCamelCase_:      Dict					   =       batch_size
         UpperCamelCase_:      Any					   =       seq_length
         UpperCamelCase_:      int					   =       is_training
         UpperCamelCase_:      Optional[Any]					   =       use_input_mask
         UpperCamelCase_:      Tuple					   =       use_labels
         UpperCamelCase_:      Any					   =       vocab_size
         UpperCamelCase_:      str					   =       d_model
         UpperCamelCase_:      Dict					   =       num_hidden_layers
         UpperCamelCase_:      List[Any]					   =       num_attention_heads
         UpperCamelCase_:      Optional[Any]					   =       ffn_dim
         UpperCamelCase_:      int					   =       activation_function
         UpperCamelCase_:      Any					   =       activation_dropout
         UpperCamelCase_:      List[str]					   =       attention_dropout
         UpperCamelCase_:      Optional[Any]					   =       max_position_embeddings
         UpperCamelCase_:      int					   =       initializer_range
         UpperCamelCase_:      int					   =       None
         UpperCamelCase_:      Union[str, Any]					   =       0
         UpperCamelCase_:      Any					   =       2
         UpperCamelCase_:      Dict					   =       1





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

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

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





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      List[Any]					   =       self.prepare_config_and_inputs()

         UpperCamelCase_:      Optional[int]					   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
         UpperCamelCase_:      Any					   =       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						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[str]					   =       2_0
         UpperCamelCase_:      List[str]					   =       model_class_name(__UpperCamelCase							)

         UpperCamelCase_:      Optional[Any]					   =       model.init_cache(input_ids.shape[0]						,    __UpperCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype='i4'							)

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

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

         UpperCamelCase_:      Any					   =       model(__UpperCamelCase							)

         UpperCamelCase_:      Optional[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}'''							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[Any]					   =       2_0
         UpperCamelCase_:      List[str]					   =       model_class_name(__UpperCamelCase							)

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

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

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

         UpperCamelCase_:      List[Any]					   =       model(__UpperCamelCase						,    attention_mask=__UpperCamelCase							)
         UpperCamelCase_:      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        _lowerCAmelCase( UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase      ):





       """simple docstring"""







       a       :	int								=(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       a       :	Optional[Any]								=(FlaxXGLMForCausalLM,) if is_flax_available() else ()





       def     _a					(      self							):
         UpperCamelCase_:      str					   =       FlaxXGLMModelTester(self							)





       def     _a					(      self							):
         for model_class_name in self.all_model_classes:
           UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Any					   =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase							)





       def     _a					(      self							):
         for model_class_name in self.all_model_classes:
           UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      int					   =       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							):
         UpperCamelCase_:      Dict					   =       XGLMTokenizer.from_pretrained('XGLM'						,    padding_side='left'							)
         UpperCamelCase_:      List[str]					   =       tokenizer(['Hello this is a long string', 'Hey']						,    return_tensors='np'						,    padding=__UpperCamelCase						,    truncation=__UpperCamelCase							)

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

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

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

         UpperCamelCase_:      int					   =       tokenizer.batch_decode(__UpperCamelCase						,    skip_special_tokens=__UpperCamelCase							)

         UpperCamelCase_:      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(__UpperCamelCase						,    __UpperCamelCase							)





       @is_pt_flax_cross_test
       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      int					   =       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
             UpperCamelCase_:      List[str]					   =       self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase							)
             UpperCamelCase_:      Tuple					   =       {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

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

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

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

             UpperCamelCase_:      int					   =       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							):
         UpperCamelCase_   ,UpperCamelCase_:      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
             UpperCamelCase_:      Any					   =       self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase							)
             UpperCamelCase_:      Dict					   =       {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

             UpperCamelCase_:      Any					   =       pt_model_class(__UpperCamelCase							).eval()
             UpperCamelCase_:      Dict					   =       False
             UpperCamelCase_:      Tuple					   =       model_class(__UpperCamelCase						,    dtype=jnp.floataa							)

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

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

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

             UpperCamelCase_:      Optional[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:
               fx_model.save_pretrained(__UpperCamelCase							)
               UpperCamelCase_:      str					   =       pt_model_class.from_pretrained(__UpperCamelCase						,    from_flax=__UpperCamelCase							)

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







# Lint as: python3

import hashlib
from typing import Union

def 						snake_case      (UpperCAmelCase__						) ->       bytes:
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # Data already in bytes, returns as it as
    return hash_data
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # We keep the data as it as for it ot be later encoded to UTF-8
    # However replace `\\` with `/` for Windows compatibility
    UpperCamelCase_:      List[str]					   =       hash_data.replace('\\'			,					'/'						)
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       str(UpperCAmelCase__						)
  else:
    # If data is not of the required type, raise error
    raise InvalidKeyError(UpperCAmelCase__						)

  return hash_data.encode('utf-8'						)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
         UpperCamelCase_:      Dict					   =       f'''\nFound Key {hash_data} of type {type(_lowerCamelCase							)}'''
         UpperCamelCase_:      Any					   =       '\nKeys should be either str, int or bytes type'
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=""							):
         UpperCamelCase_:      int					   =       key
         UpperCamelCase_:      Dict					   =       duplicate_key_indices
         UpperCamelCase_:      int					   =       fix_msg
         UpperCamelCase_:      List[Any]					   =       'Found multiple examples generated with the same key'
         if len(_lowerCamelCase							) <= 2_0:
           UpperCamelCase_:      Tuple					   =       f'''\nThe examples at index {', '.join(_lowerCamelCase							)} have the key {key}'''
         else:
           UpperCamelCase_:      Optional[int]					   =       f'''\nThe examples at index {', '.join(duplicate_key_indices[:2_0]							)}... ({len(_lowerCamelCase							) - 2_0} more) have the key {key}'''
         UpperCamelCase_:      List[Any]					   =       '\n' + fix_msg if fix_msg else ''
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       hashlib.mda(_as_bytes(_lowerCamelCase							)							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self._split_mda.copy()
         UpperCamelCase_:      str					   =       _as_bytes(_lowerCamelCase							)
         mda.update(_lowerCamelCase							)
         # Convert to integer with hexadecimal conversion
         return int(mda.hexdigest()						,    1_6							)