
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch







class   __lowercase				:
 def __init__(							self				,							A_				,							A_=14				,							A_=7				,							A_=True				,							A_=True				,							A_=False				,							A_=True				,							A_=99				,							A_=32				,							A_=5				,							A_=4				,							A_=37				,							A_="gelu"				,							A_=0.1				,							A_=0.1				,							A_=512				,							A_=0.02				,							) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						str = parent
       __lowerCAmelCase :						Tuple = batch_size
       __lowerCAmelCase :						Dict = seq_length
       __lowerCAmelCase :						Any = is_training
       __lowerCAmelCase :						int = use_input_mask
       __lowerCAmelCase :						List[Any] = use_token_type_ids
       __lowerCAmelCase :						Any = use_labels
       __lowerCAmelCase :						Union[str, Any] = vocab_size
       __lowerCAmelCase :						int = hidden_size
       __lowerCAmelCase :						Dict = num_hidden_layers
       __lowerCAmelCase :						int = num_attention_heads
       __lowerCAmelCase :						List[str] = intermediate_size
       __lowerCAmelCase :						List[Any] = hidden_act
       __lowerCAmelCase :						Any = hidden_dropout_prob
       __lowerCAmelCase :						int = attention_probs_dropout_prob
       __lowerCAmelCase :						int = max_position_embeddings
       __lowerCAmelCase :						Tuple = initializer_range
       __lowerCAmelCase :						Tuple = None
       __lowerCAmelCase :						str = vocab_size - 1
       __lowerCAmelCase :						Union[str, Any] = vocab_size - 1
       __lowerCAmelCase :						str = vocab_size - 1
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size )

       __lowerCAmelCase :						Any = None
       if self.use_input_mask:
             __lowerCAmelCase :						Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )

       __lowerCAmelCase :						List[Any] = GPTaConfig(
           vocab_size=self.vocab_size				,							n_embd=self.hidden_size				,							n_layer=self.num_hidden_layers				,							n_head=self.num_attention_heads				,							n_positions=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				,							)

       return (config, input_ids, input_mask)
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.prepare_config_and_inputs()
       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						str = config_and_inputs
       __lowerCAmelCase :						Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Optional[int] = self.prepare_config_and_inputs()

       __lowerCAmelCase :						Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       __lowerCAmelCase :						Optional[int] = 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				,							A_				,							A_				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = 20
       __lowerCAmelCase :						List[Any] = model_class_name(__UpperCamelCase )

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

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

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

       __lowerCAmelCase :						Dict = model(__UpperCamelCase )

       __lowerCAmelCase :						str = 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				,							A_				,							A_				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = 20
       __lowerCAmelCase :						List[Any] = model_class_name(__UpperCamelCase )

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

       __lowerCAmelCase :						Optional[int] = model.init_cache(input_ids.shape[0]				,							__UpperCamelCase )
       __lowerCAmelCase :						Any = jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1 )[None, :]				,							(input_ids.shape[0], input_ids.shape[-1] - 1) )

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

       __lowerCAmelCase :						Any = model(__UpperCamelCase				,							attention_mask=__UpperCamelCase )

       __lowerCAmelCase :						Dict = 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   __lowercase				(lowercase_		,       lowercase_		,       unittest.TestCase			):
 _UpperCamelCase        =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 _UpperCamelCase        =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = FlaxGPTaModelTester(self )
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Dict = self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase )
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						List[Any] = 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 ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = GPTaTokenizer.from_pretrained('''gpt2'''				,							pad_token='''</s>'''				,							padding_side='''left''' )
       __lowerCAmelCase :						Optional[Any] = tokenizer(['''Hello this is a long string''', '''Hey''']				,							return_tensors='''np'''				,							padding=__UpperCamelCase				,							truncation=__UpperCamelCase )

       __lowerCAmelCase :						Dict = FlaxGPTaLMHeadModel.from_pretrained('''gpt2''' )
       __lowerCAmelCase :						List[str] = False
       __lowerCAmelCase :						List[Any] = model.config.eos_token_id

       __lowerCAmelCase :						List[str] = jax.jit(model.generate )

       __lowerCAmelCase :						int = jit_generate(inputs['''input_ids''']				,							attention_mask=inputs['''attention_mask'''] ).sequences

       __lowerCAmelCase :						List[Any] = tokenizer.batch_decode(__UpperCamelCase				,							skip_special_tokens=__UpperCamelCase )

       __lowerCAmelCase :						Union[str, Any] = [
           '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
           '''Hey, I\'m not sure if I\'m going to be able to do''',
       ]

       self.assertListEqual(__UpperCamelCase				,							__UpperCamelCase )
 @is_pt_flax_cross_test
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase :						Tuple = 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
                   __lowerCAmelCase :						str = self._prepare_for_class(__UpperCamelCase				,							__UpperCamelCase )
                   __lowerCAmelCase :						List[Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                   __lowerCAmelCase,							__lowerCAmelCase :						Optional[int] = pt_inputs['''input_ids'''].shape
                   __lowerCAmelCase :						Any = np.random.randint(0				,							seq_length - 1				,							size=(batch_size,) )
                   for batch_idx, start_index in enumerate(__UpperCamelCase ):
                         __lowerCAmelCase :						Dict = 0
                         __lowerCAmelCase :						Any = 1
                         __lowerCAmelCase :						str = 0
                         __lowerCAmelCase :						Optional[int] = 1
                   __lowerCAmelCase :						str = pt_model_class(__UpperCamelCase ).eval()
                   __lowerCAmelCase :						Dict = model_class(__UpperCamelCase				,							dtype=jnp.floataa )

                   __lowerCAmelCase :						str = convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,							__UpperCamelCase )
                   __lowerCAmelCase :						List[str] = fx_state

                   with torch.no_grad():
                         __lowerCAmelCase :						Union[str, Any] = pt_model(**__UpperCamelCase ).to_tuple()

                   __lowerCAmelCase :						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 )
                         __lowerCAmelCase :						Tuple = model_class.from_pretrained(__UpperCamelCase				,							from_pt=__UpperCamelCase )

                   __lowerCAmelCase :						List[str] = 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 ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase :						List[str] = 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
                   __lowerCAmelCase :						Dict = self._prepare_for_class(__UpperCamelCase				,							__UpperCamelCase )
                   __lowerCAmelCase :						List[Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                   __lowerCAmelCase :						Dict = pt_model_class(__UpperCamelCase ).eval()
                   __lowerCAmelCase :						List[Any] = model_class(__UpperCamelCase				,							dtype=jnp.floataa )

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

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

                   with torch.no_grad():
                         __lowerCAmelCase :						str = pt_model(**__UpperCamelCase ).to_tuple()

                   __lowerCAmelCase :						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 )
                         __lowerCAmelCase :						Dict = pt_model_class.from_pretrained(__UpperCamelCase				,							from_flax=__UpperCamelCase )

                   with torch.no_grad():
                         __lowerCAmelCase :						Optional[int] = 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 ) ->Dict:



       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             __lowerCAmelCase :						str = model_class_name.from_pretrained('''gpt2'''				,							from_pt=__UpperCamelCase )
             __lowerCAmelCase :						Optional[Any] = model(np.ones((1, 1) ) )
             self.assertIsNotNone(__UpperCamelCase )





import heapq







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = [(0, start)]  # cost from start node,end node
      __lowerCAmelCase :						Any = set()
      while heap:
            ((__lowerCAmelCase),							(__lowerCAmelCase)) :						str = heapq.heappop(lowercase__						)
            if u in visited:
                  continue
            visited.add(lowercase__						)
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  __lowerCAmelCase :						str = cost + c
                  heapq.heappush(lowercase__       ,     (next_item, v)						)
      return -1


_UpperCamelCase			      =		{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_UpperCamelCase			      =		dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
 import doctest

 doctest.testmod()


