

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TFSharedEmbeddings,
     )

     if _tf_gpu_memory_limit is not None:
          _lowerCamelCase       :     Tuple           =							tf.config.list_physical_devices("GPU")
          for gpu in gpus:
               # Restrict TensorFlow to only allocate x GB of memory on the GPUs
               try:
                    tf.config.set_logical_device_configuration(
                        gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                    )
                    _lowerCamelCase       :     Any           =							tf.config.list_logical_devices("GPU")
                    print("Logical GPUs", logical_gpus)
               except RuntimeError as e:
                    # Virtual devices must be set before GPUs have been initialized
                    print(e)





@require_tf
class       __UpperCAmelCase  :
   UpperCamelCase       =  None
   UpperCamelCase       =  ()
   UpperCamelCase       =  ()
   UpperCamelCase       =  True
   UpperCamelCase       =  True
   UpperCamelCase       =  True
   UpperCamelCase       =  False



   def  __magic_name__					(							self							:  Tuple,	__A							:  Tuple,	__A							:  Any,	__A							:  Optional[int]=False		):
     UpperCAmelCase :							int								=				copy.deepcopy(__A		)

     if model_class in get_values(__A		):
       UpperCAmelCase :							Optional[Any]								=				{
           k: tf.tile(tf.expand_dims(__A,	1		),	(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
           if isinstance(__A,	tf.Tensor		) and v.ndim > 0
           else v
           for k, v in inputs_dict.items()
       }

     if return_labels:
       if model_class in get_values(__A		):
         UpperCAmelCase :							Optional[int]								=				tf.ones(self.model_tester.batch_size,	dtype=tf.intaa		)
       elif model_class in get_values(__A		):
         UpperCAmelCase :							Union[str, Any]								=				tf.zeros(self.model_tester.batch_size,	dtype=tf.intaa		)
         UpperCAmelCase :							Optional[Any]								=				tf.zeros(self.model_tester.batch_size,	dtype=tf.intaa		)
       elif model_class in [
           *get_values(__A		),
           *get_values(__A		),
       ]:
         UpperCAmelCase :							Dict								=				tf.zeros(self.model_tester.batch_size,	dtype=tf.intaa		)
       elif model_class in get_values(__A		):
         UpperCAmelCase :							List[str]								=				tf.zeros(self.model_tester.batch_size,	dtype=tf.intaa		)
       elif model_class in [
           *get_values(__A		),
           *get_values(__A		),
           *get_values(__A		),
           *get_values(__A		),
           *get_values(__A		),
       ]:
         UpperCAmelCase :							List[str]								=				tf.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length),	dtype=tf.intaa		)
     return inputs_dict



   @slow
   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase				,		UpperCAmelCase :							str								=				self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       UpperCAmelCase :							int								=				self._prepare_for_class(__A,	__A		)
       UpperCAmelCase :							str								=				model_class(__A		)

       @tf.function
       def run_in_graph_mode():
         return model(__A		)

       UpperCAmelCase :							int								=				run_in_graph_mode()
       self.assertIsNotNone(__A		)



   @slow
   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase				,		UpperCAmelCase :							List[str]								=				self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       UpperCAmelCase :							int								=				self._prepare_for_class(__A,	__A		)
       UpperCAmelCase :							Any								=				model_class(__A		)

       @tf.function(experimental_compile=__A		)
       def run_in_graph_mode():
         return model(__A		)

       UpperCAmelCase :							Union[str, Any]								=				run_in_graph_mode()
       self.assertIsNotNone(__A		)



   @slow
   def  __magic_name__					(							self							:  Tuple		):
     # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
     UpperCAmelCase				,		UpperCAmelCase :							List[str]								=				self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       UpperCAmelCase :							Optional[Any]								=				model_class(__A		)
       if getattr(__A,	'''hf_compute_loss''',	__A		):
         # Test that model correctly compute the loss with kwargs
         UpperCAmelCase :							int								=				self._prepare_for_class(inputs_dict.copy(),	__A,	return_labels=__A		)
         # Is there a better way to remove these decoder inputs?
         UpperCAmelCase :							List[str]								=				{
             key: val
             for key, val in prepared_for_class.items()
             if key not in ('''head_mask''', '''decoder_head_mask''', '''cross_attn_head_mask''', '''decoder_input_ids''')
         }

         UpperCAmelCase :							Optional[Any]								=				{
             '''labels''',
             '''label''',
             '''label_ids''',
             '''start_positions''',
             '''start_position''',
             '''end_positions''',
             '''end_position''',
             '''next_sentence_label''',
         }
         UpperCAmelCase :							Any								=				possible_label_cols.intersection(set(__A		)		)
         self.assertGreater(len(__A		),	0,	msg='''No matching label names found!'''		)
         UpperCAmelCase :							Any								=				{key: val for key, val in prepared_for_class.items() if key in label_names}
         UpperCAmelCase :							str								=				{key: val for key, val in prepared_for_class.items() if key not in label_names}
         self.assertGreater(len(__A		),	0		)

         # Make sure it works with XLA!
         model.compile(optimizer=tf.keras.optimizers.SGD(0.0		),	jit_compile=__A		)
         # Make sure the model fits without crashing regardless of where we pass the labels
         UpperCAmelCase :							Any								=				model.fit(
             __A,	validation_data=__A,	steps_per_epoch=1,	validation_steps=1,	shuffle=__A,	verbose=0,	)
         UpperCAmelCase :							Optional[Any]								=				history.history['''loss'''][0]
         self.assertTrue(not isnan(__A		)		)
         UpperCAmelCase :							List[str]								=				history.history['''val_loss'''][0]
         self.assertTrue(not isnan(__A		)		)

         # Now test it with separate labels, to make sure that path works in XLA too.
         UpperCAmelCase :							Union[str, Any]								=				model_class(__A		)
         model.compile(optimizer=tf.keras.optimizers.SGD(0.0		),	jit_compile=__A		)
         UpperCAmelCase :							Any								=				model.fit(
             __A,	__A,	validation_data=(inputs_minus_labels, labels),	steps_per_epoch=1,	validation_steps=1,	shuffle=__A,	verbose=0,	)

         UpperCAmelCase :							List[Any]								=				history.history['''loss'''][0]
         self.assertTrue(not isnan(__A		)		)
         UpperCAmelCase :							Tuple								=				history.history['''val_loss'''][0]
         self.assertTrue(not isnan(__A		)		)



   @slow
   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase				,		UpperCAmelCase :							str								=				self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase :							Dict								=				True
     UpperCAmelCase :							Optional[Any]								=				True

     if hasattr(__A,	'''use_cache'''		):
       UpperCAmelCase :							List[str]								=				True

     UpperCAmelCase :							Any								=				getattr(self.model_tester,	'''encoder_seq_length''',	self.model_tester.seq_length		)
     UpperCAmelCase :							int								=				getattr(self.model_tester,	'''key_length''',	__A		)

     for model_class in self.all_model_classes[:2]:
       UpperCAmelCase :							Tuple								=				self._prepare_for_class(__A,	__A		)
       UpperCAmelCase :							Union[str, Any]								=				model_class(__A		)
       model.build()
       UpperCAmelCase :							Optional[int]								=				len(model(__A		)		)

       for key in list(class_inputs_dict.keys()		):
         # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
         if key not in model.input_signature:
           del class_inputs_dict[key]
         # Check it's a tensor, in case the inputs dict has some bools in it too
         elif isinstance(class_inputs_dict[key],	tf.Tensor		) and class_inputs_dict[key].dtype.is_integer:
           UpperCAmelCase :							Union[str, Any]								=				tf.cast(class_inputs_dict[key],	tf.intaa		)

       if set(class_inputs_dict.keys()		) != set(model.input_signature.keys()		):
         continue  # Some models have inputs that the preparation functions don't create, we skip those

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__A,	saved_model=__A		)
         UpperCAmelCase :							List[str]								=				os.path.join(__A,	'''saved_model''',	'''1'''		)
         UpperCAmelCase :							Union[str, Any]								=				tf.keras.models.load_model(__A		)
         UpperCAmelCase :							List[str]								=				model(__A		)

         if self.is_encoder_decoder:
           UpperCAmelCase :							int								=				outputs['''encoder_hidden_states''']
           UpperCAmelCase :							Union[str, Any]								=				outputs['''encoder_attentions''']
         else:
           UpperCAmelCase :							Optional[Any]								=				outputs['''hidden_states''']
           UpperCAmelCase :							List[str]								=				outputs['''attentions''']

         self.assertEqual(len(__A		),	__A		)

         UpperCAmelCase :							str								=				getattr(
             self.model_tester,	'''expected_num_hidden_layers''',	self.model_tester.num_hidden_layers + 1		)

         self.assertEqual(len(__A		),	__A		)
         self.assertListEqual(
             list(output_hidden_states[0].shape[-2:]		),	[self.model_tester.seq_length, self.model_tester.hidden_size],	)

         self.assertEqual(len(__A		),	self.model_tester.num_hidden_layers		)
         self.assertListEqual(
             list(output_attentions[0].shape[-3:]		),	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],	)



   @slow
   def  __magic_name__					(							self							:  Optional[int]		):
     tf.keras.mixed_precision.set_global_policy('''mixed_float16'''		)

     # try/finally block to ensure subsequent tests run in float32
     try:
       UpperCAmelCase				,		UpperCAmelCase :							Optional[Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
         UpperCAmelCase :							Any								=				self._prepare_for_class(__A,	__A		)
         UpperCAmelCase :							Dict								=				model_class(__A		)
         UpperCAmelCase :							Any								=				model(__A		)

         self.assertIsNotNone(__A		)
     finally:
       tf.keras.mixed_precision.set_global_policy('''float32'''		)



   @slow
   def  __magic_name__					(							self							:  Any		):
     UpperCAmelCase				,		UpperCAmelCase :							Any								=				self.model_tester.prepare_config_and_inputs_for_common()
     # head_mask and decoder_head_mask has different shapes than other input args
     if "head_mask" in inputs_dict:
       del inputs_dict["head_mask"]
     if "decoder_head_mask" in inputs_dict:
       del inputs_dict["decoder_head_mask"]
     if "cross_attn_head_mask" in inputs_dict:
       del inputs_dict["cross_attn_head_mask"]
     UpperCAmelCase :							List[Any]								=				{
         module_member
         for model_class in self.all_model_classes
         for module in (import_module(model_class.__module__		),)
         for module_member_name in dir(__A		)
         if module_member_name.endswith('''MainLayer'''		)
         for module_member in (getattr(__A,	__A		),)
         if isinstance(__A,	__A		)
         and tf.keras.layers.Layer in module_member.__bases__
         and getattr(__A,	'''_keras_serializable''',	__A		)
     }

     for main_layer_class in tf_main_layer_classes:
       # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
       if "T5" in main_layer_class.__name__:
         # Take the same values than in TFT5ModelTester for this shared layer
         UpperCAmelCase :							List[str]								=				TFSharedEmbeddings(self.model_tester.vocab_size,	self.model_tester.hidden_size,	name='''shared'''		)
         UpperCAmelCase :							int								=				False
         UpperCAmelCase :							Optional[int]								=				main_layer_class(__A,	embed_tokens=__A		)
       else:
         UpperCAmelCase :							List[Any]								=				main_layer_class(__A		)

       UpperCAmelCase :							Optional[Any]								=				{
           name: tf.keras.Input(tensor.shape[1:],	dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
       }

       if hasattr(self.model_tester,	'''num_labels'''		):
         UpperCAmelCase :							Tuple								=				self.model_tester.num_labels
       else:
         UpperCAmelCase :							List[Any]								=				2

       UpperCAmelCase :							Optional[Any]								=				tf.data.Dataset.from_tensor_slices(
           (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)		))		).batch(1		)

       UpperCAmelCase :							int								=				main_layer(__A		)[0]
       UpperCAmelCase :							List[str]								=				tf.keras.layers.Dense(__A,	activation='''softmax''',	name='''outputs'''		)(__A		)
       UpperCAmelCase :							str								=				tf.keras.models.Model(inputs=__A,	outputs=[outputs]		)

       model.compile(loss='''binary_crossentropy''',	optimizer='''adam''',	metrics=['''binary_accuracy''']		)
       model.fit(__A,	epochs=1		)

       with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCAmelCase :							str								=				os.path.join(__A,	'''keras_model.h5'''		)
         model.save(__A		)
         if "T5" in main_layer_class.__name__:
           UpperCAmelCase :							Any								=				tf.keras.models.load_model(
               __A,	custom_objects={
                   main_layer_class.__name__: main_layer_class,
                   '''TFSharedEmbeddings''': TFSharedEmbeddings,
               },	)
         else:
           UpperCAmelCase :							Tuple								=				tf.keras.models.load_model(
               __A,	custom_objects={main_layer_class.__name__: main_layer_class}		)
         assert isinstance(__A,	tf.keras.Model		)
         model(__A		)



   @slow
   def  __magic_name__					(							self							:  Any		):
     UpperCAmelCase				,		UpperCAmelCase :							Dict								=				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:2]:
       UpperCAmelCase :							Dict								=				model_class(__A		)

       UpperCAmelCase :							List[str]								=				copy.deepcopy(__A		)

       if not self.is_encoder_decoder:
         UpperCAmelCase :							int								=				inputs['''input_ids''']
         del inputs["input_ids"]
       else:
         UpperCAmelCase :							Optional[Any]								=				inputs['''input_ids''']
         UpperCAmelCase :							Optional[Any]								=				inputs.get('''decoder_input_ids''',	__A		)
         del inputs["input_ids"]
         inputs.pop('''decoder_input_ids''',	__A		)

       if not self.is_encoder_decoder:
         UpperCAmelCase :							Dict								=				model.get_input_embeddings()(__A		)
       else:
         UpperCAmelCase :							List[str]								=				model.get_input_embeddings()(__A		)
         UpperCAmelCase :							Any								=				model.get_input_embeddings()(__A		)

       UpperCAmelCase :							str								=				self._prepare_for_class(__A,	__A		)

       @tf.function
       def run_in_graph_mode():
         return model(__A		)

       UpperCAmelCase :							int								=				run_in_graph_mode()
       self.assertIsNotNone(__A		)



   def  __magic_name__					(							self							:  List[str],	__A							:  Dict,	__A							:  Tuple		):
     # special tokens cannot be bad tokens
     UpperCAmelCase :							Union[str, Any]								=				[]
     if model.config.bos_token_id is not None:
       special_tokens.append(model.config.bos_token_id		)
     if model.config.pad_token_id is not None:
       special_tokens.append(model.config.pad_token_id		)
     if model.config.eos_token_id is not None:
       special_tokens.append(model.config.eos_token_id		)

     # create random bad tokens that are not special tokens
     UpperCAmelCase :							List[Any]								=				[]
     while len(__A		) < num_bad_tokens:
       UpperCAmelCase :							str								=				tf.squeeze(ids_tensor((1, 1),	self.model_tester.vocab_size		),	0		).numpy()[0]
       if token not in special_tokens:
         bad_tokens.append(__A		)
     return bad_tokens



   def  __magic_name__					(							self							:  int,	__A							:  Any		):
     for token_id in output_ids[0].numpy().tolist():
       self.assertGreaterEqual(__A,	0		)
       self.assertLess(__A,	self.model_tester.vocab_size		)





   def  __magic_name__					(							self							:  Any,	__A							:  Dict,	__A							:  List[Any]		):
     # for all bad word tokens
     for bad_word_ids in bad_words_ids:
       # for all slices in batch
       for generated_ids_slice in generated_ids:
         # for all word idx
         for i in range(len(__A		),	len(__A		)		):
           # if tokens match
           if generated_ids_slice[i - len(__A		) : i] == bad_word_ids:
             return True
     return False








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

def     a__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    Optional[int]    )						->		Any:
  UpperCAmelCase :							Union[str, Any]								=				float('''inf'''    )
  UpperCAmelCase :							str								=				-1
  for i in range(UpperCAmelCase    ):
    if (not vset[i]) and mdist[i] < min_val:
      UpperCAmelCase :							int								=				i
      UpperCAmelCase :							Union[str, Any]								=				mdist[i]
  return min_ind

def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Optional[Any]       ,				UpperCAmelCase							:    str    )						->		Tuple:
  UpperCAmelCase :							str								=				[float('''inf'''    ) for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							Optional[int]								=				[False for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							List[Any]								=				0.0

  for _ in range(v - 1    ):
    UpperCAmelCase :							Any								=				min_dist(UpperCAmelCase       ,				UpperCAmelCase       ,				UpperCAmelCase    )
    UpperCAmelCase :							List[Any]								=				True

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

  print_dist(UpperCAmelCase       ,				UpperCAmelCase    )


if __name__ == "__main__":
     _lowerCamelCase       :     Tuple           =							int(input("Enter number of vertices: ").strip())
     _lowerCamelCase       :     List[str]           =							int(input("Enter number of edges: ").strip())

     _lowerCamelCase       :     Optional[int]           =							[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          _lowerCamelCase       :     Union[str, Any]           =							0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          _lowerCamelCase       :     List[str]           =							int(input("Enter source:").strip())
          _lowerCamelCase       :     List[str]           =							int(input("Enter destination:").strip())
          _lowerCamelCase       :     List[Any]           =							float(input("Enter weight:").strip())
          _lowerCamelCase       :     Any           =							weight

     _lowerCamelCase       :     List[str]           =							int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)





