


'''simple docstring'''
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__					:





  def __init__(	self   :			Union[str, Any]							,      _a   :			Any							,      _a   :			Tuple=14							,      _a   :			str=7							,      _a   :			Optional[Any]=True							,      _a   :			Any=True							,      _a   :			Optional[Any]=True							,      _a   :			Union[str, Any]=99							,      _a   :			int=32							,      _a   :			Union[str, Any]=5							,      _a   :			Any=4							,      _a   :			Optional[int]=37							,      _a   :			Union[str, Any]="gelu"							,      _a   :			Dict=0.1							,      _a   :			str=0.1							,      _a   :			Any=512							,      _a   :			Tuple=0.02							,      _a   :			Optional[int]=None							,      )			->					int:





       '''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 		A						(	self   :			List[str]				)			->					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 		A						(	self   :			Optional[int]				)			->					Optional[int]:





       '''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 		A						(	self   :			int				)			->					Tuple:





       '''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 		A						(	self   :			int							,      _a   :			Optional[Any]							,      _a   :			List[Any]							,      _a   :			Tuple							,      _a   :			Union[str, Any]				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=20
       _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 		A						(	self   :			Optional[int]							,      _a   :			Union[str, Any]							,      _a   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			str				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=20
       _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__					( A__  ,     A__  ,     unittest.TestCase						):
  A__					=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  A__					=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()





  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=FlaxXGLMModelTester(self				)





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





       '''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 		A						(	self   :			int				)			->					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_with_attn_mask(
                _a							,      _a							,      _a							,      _a				)





  @slow
  def 		A						(	self   :			Dict				)			->					Optional[int]:





       '''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 		A						(	self   :			Optional[Any]				)			->					List[str]:





       '''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 		A						(	self   :			Union[str, Any]				)			->					int:





       '''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 		A						(	self   :			Tuple				)			->					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'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
