

"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )
@require_tf
class        _lowercase					:
     _lowerCamelCase     =       XGLMConfig
     _lowerCamelCase     =       {}
     _lowerCamelCase     =       '''gelu'''







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=14 ,						UpperCamelCase_=7 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=99 ,						UpperCamelCase_=32 ,						UpperCamelCase_=2 ,						UpperCamelCase_=4 ,						UpperCamelCase_=37 ,						UpperCamelCase_="gelu" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=0.0_2 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_input_mask
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      d_model
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      ffn_dim
      __magic_name__     =      activation_function
      __magic_name__     =      activation_dropout
      __magic_name__     =      attention_dropout
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      initializer_range
      __magic_name__     =      None
      __magic_name__     =      0
      __magic_name__     =      2
      __magic_name__     =      1







     def 	lowerCAmelCase__    (      self       ):
      return XGLMConfig.from_pretrained('''facebook/xglm-564M'''       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tf.clip_by_value(
          ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       ) ,						clip_value_min=0 ,						clip_value_max=3       )

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

      __magic_name__     =      self.get_config()

      __magic_name__     =      floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,						2       )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )







     def 	lowerCAmelCase__    (      self       ):
      return 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=UpperCamelCase_ ,						bos_token_id=self.bos_token_id ,						eos_token_id=self.eos_token_id ,						pad_token_id=self.pad_token_id ,						return_dict=UpperCamelCase_ ,						)







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()

      (
          (
          __magic_name__						
      )   ,						(
          __magic_name__						
      )   ,						(
          __magic_name__						
      )   ,						(
          __magic_name__						
      )   ,												
      )     =      config_and_inputs

      __magic_name__     =      {
          '''input_ids''': input_ids,
          '''head_mask''': head_mask,
      }

      return config, inputs_dict


@require_tf
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     _lowerCamelCase     =       (TFXGLMForCausalLM,) if is_tf_available() else ()
     _lowerCamelCase     =       (
         {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
     )
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFXGLMModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_ ,						n_embd=37       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     @slow
     def 	lowerCAmelCase__    (      self       ):
      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __magic_name__     =      TFXGLMModel.from_pretrained(UpperCamelCase_       )
       self.assertIsNotNone(UpperCamelCase_       )







     @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''       )
     def 	lowerCAmelCase__    (      self       ):
      super().test_resize_token_embeddings()


@require_tf
class        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=True       ):
      __magic_name__     =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
      __magic_name__     =      tf.convert_to_tensor([[2, 268, 9865]] ,						dtype=tf.intaa       )  # The dog
      # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
      # fmt: off
      __magic_name__     =      [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
      # fmt: on
      __magic_name__     =      model.generate(UpperCamelCase_ ,						do_sample=UpperCamelCase_ ,						num_beams=1       )
      if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist() ,						UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )
      __magic_name__     =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )

      tf.random.set_seed(0       )
      __magic_name__     =      tokenizer('''Today is a nice day and''' ,						return_tensors='''tf'''       )
      __magic_name__     =      tokenized.input_ids
      # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
      with tf.device(''':/CPU:0'''       ):
       __magic_name__     =      model.generate(UpperCamelCase_ ,						do_sample=UpperCamelCase_ ,						seed=[7, 0]       )
      __magic_name__     =      tokenizer.decode(output_ids[0] ,						skip_special_tokens=UpperCamelCase_       )

      __magic_name__     =      (
          '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
      )
      self.assertEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
      __magic_name__     =      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )

      __magic_name__     =      '''left'''

      # use different length sentences to test batching
      __magic_name__     =      [
          '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
          '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
          '''regardless of whether left padding is applied or not. When''',
          '''Hello, my dog is a little''',
      ]

      __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''tf''' ,						padding=UpperCamelCase_       )
      __magic_name__     =      inputs['''input_ids''']

      __magic_name__     =      model.generate(input_ids=UpperCamelCase_ ,						attention_mask=inputs['''attention_mask'''] ,						max_new_tokens=12       )

      __magic_name__     =      tokenizer(sentences[0] ,						return_tensors='''tf'''       ).input_ids
      __magic_name__     =      model.generate(input_ids=UpperCamelCase_ ,						max_new_tokens=12       )

      __magic_name__     =      tokenizer(sentences[1] ,						return_tensors='''tf'''       ).input_ids
      __magic_name__     =      model.generate(input_ids=UpperCamelCase_ ,						max_new_tokens=12       )

      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_ ,						skip_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(output_non_padded[0] ,						skip_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(output_padded[0] ,						skip_special_tokens=UpperCamelCase_       )

      __magic_name__     =      [
          '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
          '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
          '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
          '''a single''',
          '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
      ]
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						[non_padded_sentence, padded_sentence]       )




"""simple docstring"""






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCamelCase										=		TypeVar("T")





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (position - 1) // 2





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (2 * position) + 1





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (2 * position) + 2
class        _lowercase					(     Generic[T]     ):







     def __init__(      self       ):
      __magic_name__     =      []
      __magic_name__     =      {}
      __magic_name__     =      0







     def __len__(      self       ):
      return self.elements







     def __repr__(      self       ):
      return str(self.heap       )







     def 	lowerCAmelCase__    (      self       ):
      # Check if the priority queue is empty
      return self.elements == 0







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)       )
      __magic_name__     =      self.elements
      self.elements += 1
      self._bubble_up(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
       self._swap_nodes(0 ,						self.elements - 1       )
      __magic_name__   ,						__magic_name__     =      self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
       __magic_name__   ,						__magic_name__     =      self.heap[0]
       self._bubble_down(UpperCamelCase_       )
      return elem







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Update the weight of the given key
      __magic_name__     =      self.position_map[elem]
      __magic_name__     =      (elem, weight)
      if position > 0:
       __magic_name__     =      get_parent_position(UpperCamelCase_       )
       __magic_name__   ,						__magic_name__     =      self.heap[parent_position]
       if parent_weight > weight:
        self._bubble_up(UpperCamelCase_       )
       else:
        self._bubble_down(UpperCamelCase_       )
      else:
       self._bubble_down(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      __magic_name__     =      self.position_map[elem]
      if curr_pos == 0:
       return None
      __magic_name__     =      get_parent_position(UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      self.heap[curr_pos]
      __magic_name__   ,						__magic_name__     =      self.heap[parent_position]
      if parent_weight > weight:
       self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
       return self._bubble_up(UpperCamelCase_       )
      return None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      __magic_name__     =      self.position_map[elem]
      __magic_name__   ,						__magic_name__     =      self.heap[curr_pos]
      __magic_name__     =      get_child_left_position(UpperCamelCase_       )
      __magic_name__     =      get_child_right_position(UpperCamelCase_       )
      if child_left_position < self.elements and child_right_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_left_position]
       __magic_name__   ,						__magic_name__     =      self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      if child_left_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_left_position]
       if child_left_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      else:
       return None
      if child_right_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_right_position]
       if child_right_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      return None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Swap the nodes at the given positions
      __magic_name__     =      self.heap[nodea_pos][0]
      __magic_name__     =      self.heap[nodea_pos][0]
      __magic_name__   ,						__magic_name__     =      (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      __magic_name__     =      nodea_pos
      __magic_name__     =      nodea_pos


class        _lowercase					(     Generic[T]     ):







     def __init__(      self       ):
      __magic_name__     =      {}
      __magic_name__     =      0







     def __repr__(      self       ):
      return str(self.connections       )







     def __len__(      self       ):
      return self.nodes







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
       __magic_name__     =      {}
       self.nodes += 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Add an edge between 2 nodes in the graph
      self.add_node(UpperCamelCase_       )
      self.add_node(UpperCamelCase_       )
      __magic_name__     =      weight
      __magic_name__     =      weight





def 	lowercase      (   __UpperCamelCase      ,   ) ->      tuple[dict[T, int], dict[T, T | None]]:
 __magic_name__     =      {node: maxsize for node in graph.connections}
 __magic_name__     =      {node: None for node in graph.connections}

 __magic_name__     =      MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(__UpperCamelCase      ,   __UpperCamelCase						)

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 __magic_name__     =      priority_queue.extract_min()
 __magic_name__     =      0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   __magic_name__     =      dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(__UpperCamelCase      ,   dist[neighbour]						)
   __magic_name__     =      node

    # running prim's algorithm
 while not priority_queue.is_empty():
  __magic_name__     =      priority_queue.extract_min()
  for neighbour in graph.connections[node]:
   if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
    __magic_name__     =      dist[node] + graph.connections[node][neighbour]
    priority_queue.update_key(__UpperCamelCase      ,   dist[neighbour]						)
    __magic_name__     =      node
 return dist, parent

