






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 	a_:



     """simple docstring"""



     def __init__(		self					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Any=1_4  ,  lowerCAmelCase__					:	Union[str, Any]=7  ,  lowerCAmelCase__					:	Optional[int]=True  ,  lowerCAmelCase__					:	List[str]=True  ,  lowerCAmelCase__					:	int=True  ,  lowerCAmelCase__					:	Any=9_9  ,  lowerCAmelCase__					:	Optional[int]=3_2  ,  lowerCAmelCase__					:	List[str]=5  ,  lowerCAmelCase__					:	Optional[Any]=4  ,  lowerCAmelCase__					:	Union[str, Any]=3_7  ,  lowerCAmelCase__					:	List[Any]="gelu"  ,  lowerCAmelCase__					:	int=0.1  ,  lowerCAmelCase__					:	Union[str, Any]=0.1  ,  lowerCAmelCase__					:	str=5_1_2  ,  lowerCAmelCase__					:	Optional[Any]=0.02  ,  lowerCAmelCase__					:	Tuple=None  ,  )							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		seq_length
      SCREAMING_SNAKE_CASE				    =		is_training
      SCREAMING_SNAKE_CASE				    =		use_input_mask
      SCREAMING_SNAKE_CASE				    =		use_labels
      SCREAMING_SNAKE_CASE				    =		vocab_size
      SCREAMING_SNAKE_CASE				    =		d_model
      SCREAMING_SNAKE_CASE				    =		num_hidden_layers
      SCREAMING_SNAKE_CASE				    =		num_attention_heads
      SCREAMING_SNAKE_CASE				    =		ffn_dim
      SCREAMING_SNAKE_CASE				    =		activation_function
      SCREAMING_SNAKE_CASE				    =		activation_dropout
      SCREAMING_SNAKE_CASE				    =		attention_dropout
      SCREAMING_SNAKE_CASE				    =		max_position_embeddings
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		None
      SCREAMING_SNAKE_CASE				    =		0
      SCREAMING_SNAKE_CASE				    =		2
      SCREAMING_SNAKE_CASE				    =		1





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		np.clip(ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size)  ,  3  ,  self.vocab_size)

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

      SCREAMING_SNAKE_CASE				    =		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=_A  ,  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     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		config_and_inputs
      SCREAMING_SNAKE_CASE				    =		{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict





     def     __UpperCamelCase						(		self					:	str)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE				    =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2)

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





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(_A)

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

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

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

      SCREAMING_SNAKE_CASE				    =		model(_A)

      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Tuple)							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(_A)

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

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

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

      SCREAMING_SNAKE_CASE				    =		model(_A  ,  attention_mask=_A)
      SCREAMING_SNAKE_CASE				    =		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 	a_(		lowercase__			,							lowercase__			,							unittest.TestCase	):



     """simple docstring"""



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





     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		FlaxXGLMModelTester(self)





     def     __UpperCamelCase						(		self					:	Dict)							->	List[str]:

      """simple docstring"""



      for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(_A  ,  _A  ,  _A  ,  _A)





     def     __UpperCamelCase						(		self					:	str)							->	int:

      """simple docstring"""



      for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           _A  ,  _A  ,  _A  ,  _A)





     @slow
     def     __UpperCamelCase						(		self					:	Tuple)							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		XGLMTokenizer.from_pretrained('XGLM'  ,  padding_side='left')
      SCREAMING_SNAKE_CASE				    =		tokenizer(['Hello this is a long string', 'Hey']  ,  return_tensors='np'  ,  padding=_A  ,  truncation=_A)

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

      SCREAMING_SNAKE_CASE				    =		jax.jit(model.generate)

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

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

      SCREAMING_SNAKE_CASE				    =		[
          '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(_A  ,  _A)





     @is_pt_flax_cross_test
     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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
        SCREAMING_SNAKE_CASE				    =		self._prepare_for_class(_A  ,  _A)
        SCREAMING_SNAKE_CASE				    =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

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

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(_A)
         SCREAMING_SNAKE_CASE				    =		model_class.from_pretrained(_A  ,  from_pt=_A)

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





     @is_pt_flax_cross_test
     def     __UpperCamelCase						(		self					:	List[str])							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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
        SCREAMING_SNAKE_CASE				    =		self._prepare_for_class(_A  ,  _A)
        SCREAMING_SNAKE_CASE				    =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(_A)
         SCREAMING_SNAKE_CASE				    =		pt_model_class.from_pretrained(_A  ,  from_flax=_A)

        with torch.no_grad():
         SCREAMING_SNAKE_CASE				    =		pt_model_loaded(**_A).to_tuple()

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





     @slow
     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Union[str, Any]:

      """simple docstring"""



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






'''simple docstring'''





class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:int      )					->      None:







       '''simple docstring'''



       __A													=      data
       __A													=      None
       __A													=      None





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      BinaryTreeNode | None:







      """simple docstring"""
      if node is None:
            __A													=      BinaryTreeNode(UpperCAmelCase						)
            return node

      # binary search tree is not empty,
      # so we will insert it into the tree
      # if new_value is less than value of data in node,
      #  add it to left subtree and proceed recursively
      if new_value < node.data:
            __A													=      insert(node.left_child   ,	UpperCAmelCase						)
      else:
            # if new_value is greater than value of data in node,
            #  add it to right subtree and proceed recursively
            __A													=      insert(node.right_child   ,	UpperCAmelCase						)
      return node





def 							snake_case				(		UpperCAmelCase						)->      list[int]:  # if node is None,return







      """simple docstring"""
      if node:
            __A													=      inorder(node.left_child						)
            __A													=      [*inorder_array, node.data]
            __A													=      inorder_array + inorder(node.right_child						)
      else:
            __A													=      []
      return inorder_array





def 							snake_case				(		)->      BinaryTreeNode | None:







      """simple docstring"""
      __A													=      insert(UpperCAmelCase   ,	1_5						)
      insert(UpperCAmelCase   ,	1_0						)
      insert(UpperCAmelCase   ,	2_5						)
      insert(UpperCAmelCase   ,	6						)
      insert(UpperCAmelCase   ,	1_4						)
      insert(UpperCAmelCase   ,	2_0						)
      insert(UpperCAmelCase   ,	6_0						)
      return root





def 							snake_case				(		)->      None:







      """simple docstring"""
      # main function
      __A													=      make_tree()
      print('Printing values of binary search tree in Inorder Traversal.'						)
      inorder(UpperCAmelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






