





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
   import torch




class  __UpperCamelCase						:





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase=14							,       _UpperCamelCase=7							,       _UpperCamelCase=True							,       _UpperCamelCase=True							,       _UpperCamelCase=False							,       _UpperCamelCase=True							,       _UpperCamelCase=99							,       _UpperCamelCase=32							,       _UpperCamelCase=4							,       _UpperCamelCase=4							,       _UpperCamelCase=[[["global", "local"], 2]]							,       _UpperCamelCase=37							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.1							,       _UpperCamelCase=0.1							,       _UpperCamelCase=512							,       _UpperCamelCase=7							,       _UpperCamelCase=0.02							,       ):
        _UpperCAmelCase											=      parent
        _UpperCAmelCase											=      batch_size
        _UpperCAmelCase											=      seq_length
        _UpperCAmelCase											=      is_training
        _UpperCAmelCase											=      use_input_mask
        _UpperCAmelCase											=      use_token_type_ids
        _UpperCAmelCase											=      use_labels
        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      attention_types
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      max_position_embeddings
        _UpperCAmelCase											=      window_size
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      None
        _UpperCAmelCase											=      vocab_size - 1
        _UpperCAmelCase											=      vocab_size - 1
        _UpperCAmelCase											=      vocab_size - 1





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size				)

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

        _UpperCAmelCase											=      GPTNeoConfig(
            vocab_size=self.vocab_size							,       hidden_size=self.hidden_size							,       num_layers=self.num_hidden_layers							,       num_heads=self.num_attention_heads							,       max_position_embeddings=self.max_position_embeddings							,       use_cache=_UpperCamelCase							,       bos_token_id=self.bos_token_id							,       eos_token_id=self.eos_token_id							,       pad_token_id=self.pad_token_id							,       window_size=self.window_size							,       attention_types=self.attention_types							,       )

        return (config, input_ids, input_mask)





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      self.prepare_config_and_inputs()
        _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase											=      config_and_inputs
        _UpperCAmelCase											=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      20
        _UpperCAmelCase											=      model_class_name(_UpperCamelCase				)

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

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

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

        _UpperCAmelCase											=      model(_UpperCamelCase				)

        _UpperCAmelCase											=      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							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      20
        _UpperCAmelCase											=      model_class_name(_UpperCamelCase				)

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

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

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

        _UpperCAmelCase											=      model(_UpperCamelCase							,       attention_mask=_UpperCamelCase				)

        _UpperCAmelCase											=      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_flax
class  __UpperCamelCase						(       A__  ,   A__  ,   unittest.TestCase						):
     __A  :     List[Any]            =      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     __A  :     Tuple            =      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      FlaxGPTNeoModelTester(self				)





     def 		UpperCamelCase(    self				):
        for model_class_name in self.all_model_classes:
           _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        for model_class_name in self.all_model_classes:
           _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				)





     @slow
     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      GPTaTokenizer.from_pretrained('''gpt2'''							,       pad_token='''<|endoftext|>'''							,       padding_side='''left'''				)
        _UpperCAmelCase											=      tokenizer(['''Hello this is a long string''', '''Hey''']							,       return_tensors='''np'''							,       padding=_UpperCamelCase							,       truncation=_UpperCamelCase				)

        _UpperCAmelCase											=      FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''				)
        _UpperCAmelCase											=      False
        _UpperCAmelCase											=      model.config.eos_token_id

        _UpperCAmelCase											=      jax.jit(model.generate				)

        _UpperCAmelCase											=      jit_generate(
            inputs['''input_ids''']							,       attention_mask=inputs['''attention_mask''']							,       pad_token_id=tokenizer.pad_token_id				).sequences

        _UpperCAmelCase											=      tokenizer.batch_decode(_UpperCamelCase							,       skip_special_tokens=_UpperCamelCase				)

        _UpperCAmelCase											=      [
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(_UpperCamelCase							,       _UpperCamelCase				)





     @is_pt_flax_cross_test
     def 		UpperCamelCase(    self				):
        _UpperCAmelCase					,			_UpperCAmelCase											=      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											=      self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase				)
              _UpperCAmelCase											=      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

              _UpperCAmelCase					,			_UpperCAmelCase											=      pt_inputs['''input_ids'''].shape
              _UpperCAmelCase											=      np.random.randint(0							,       seq_length - 1							,       size=(batch_size,)				)
              for batch_idx, start_index in enumerate(_UpperCamelCase				):
                 _UpperCAmelCase											=      0
                 _UpperCAmelCase											=      1
                 _UpperCAmelCase											=      0
                 _UpperCAmelCase											=      1
              _UpperCAmelCase											=      pt_model_class(_UpperCamelCase				).eval()
              _UpperCAmelCase											=      model_class(_UpperCamelCase							,       dtype=jnp.floataa				)

              _UpperCAmelCase											=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,       _UpperCamelCase				)
              _UpperCAmelCase											=      fx_state

              with torch.no_grad():
                 _UpperCAmelCase											=      pt_model(**_UpperCamelCase				).to_tuple()

              _UpperCAmelCase											=      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											=      model_class.from_pretrained(_UpperCamelCase							,       from_pt=_UpperCamelCase				)

              _UpperCAmelCase											=      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 		UpperCamelCase(    self				):
        _UpperCAmelCase					,			_UpperCAmelCase											=      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											=      self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase				)
              _UpperCAmelCase											=      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

              _UpperCAmelCase											=      pt_model_class(_UpperCamelCase				).eval()
              _UpperCAmelCase											=      model_class(_UpperCamelCase							,       dtype=jnp.floataa				)

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

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

              with torch.no_grad():
                 _UpperCAmelCase											=      pt_model(**_UpperCamelCase				).to_tuple()

              _UpperCAmelCase											=      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											=      pt_model_class.from_pretrained(_UpperCamelCase							,       from_flax=_UpperCamelCase				)

              with torch.no_grad():
                 _UpperCAmelCase											=      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 		UpperCamelCase(    self				):
        for model_class_name in self.all_model_classes:
           _UpperCAmelCase											=      model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''				)
           _UpperCAmelCase											=      model(np.ones((1, 1)				)				)
           self.assertIsNotNone(_UpperCamelCase				)






def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     List[str]:






   """simple docstring"""







   print('''\nVertex Distance'''	)
   for i in range(SCREAMING_SNAKE_CASE_	):
      if dist[i] != float('''inf'''	):
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  int(dist[i]	)							,  end='''\t'''	)
      else:
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  '''INF'''							,  end='''\t'''	)
      print()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      float('''inf'''	)
   _UpperCAmelCase											=      -1
   for i in range(SCREAMING_SNAKE_CASE_	):
      if (not vset[i]) and mdist[i] < min_val:
         _UpperCAmelCase											=      i
         _UpperCAmelCase											=      mdist[i]
   return min_ind
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      [float('''inf'''	) for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      [False for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      0.0

   for _ in range(v - 1	):
      _UpperCAmelCase											=      min_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      _UpperCAmelCase											=      True

      for i in range(SCREAMING_SNAKE_CASE_	):
         if (
             (not vset[i])
             and graph[u][i] != float('''inf'''	)
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _UpperCAmelCase											=      mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  int(input("Enter number of vertices: ").strip())
   UpperCAmelCase_											=  int(input("Enter number of edges: ").strip())

   UpperCAmelCase_											=  [[float("inf") for i in range(V)] for j in range(V)]

   for i in range(V):
      UpperCAmelCase_											=  0.0

   for i in range(E):
      print("\nEdge ", i + 1)
      UpperCAmelCase_											=  int(input("Enter source:").strip())
      UpperCAmelCase_											=  int(input("Enter destination:").strip())
      UpperCAmelCase_											=  float(input("Enter weight:").strip())
      UpperCAmelCase_											=  weight

   UpperCAmelCase_											=  int(input("\nEnter shortest path source:").strip())
   dijkstra(graph, V, gsrc)