






"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=14 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=False ,				snake_case_=True ,				snake_case_=99 ,				snake_case_=32 ,				snake_case_=4 ,				snake_case_=4 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=0.02 ,				)		->       Optional[int]:
       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__         =    rotary_dim
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    max_position_embeddings
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    None
       UpperCamelCase__         =    vocab_size - 1
       UpperCamelCase__         =    vocab_size - 1
       UpperCamelCase__         =    vocab_size - 1

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       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__         =    GPTJConfig(
           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=snake_case_ ,				bos_token_id=self.bos_token_id ,				eos_token_id=self.eos_token_id ,				pad_token_id=self.pad_token_id ,				rotary_dim=self.rotary_dim ,				)

       return (config, input_ids, input_mask)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    20
       UpperCamelCase__         =    model_class_name(snake_case_      )

       UpperCamelCase__         =    model.init_cache(input_ids.shape[0] ,				snake_case_      )
       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=snake_case_ ,				past_key_values=snake_case_ ,				position_ids=snake_case_ ,				)

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

       UpperCamelCase__         =    model(snake_case_      )

       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       UpperCamelCase__         =    20
       UpperCamelCase__         =    model_class_name(snake_case_      )

       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] ,				snake_case_      )
       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=snake_case_ ,				past_key_values=snake_case_ ,				position_ids=snake_case_ ,				)
       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=snake_case_ ,				position_ids=snake_case_ ,				)

       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_      )

       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    __lowerCamelCase    (  _a							,	_a							,	unittest.TestCase  ):
   a      :				Optional[Any] 						=(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   a      :				List[Any] 						=(FlaxGPTJForCausalLM,) if is_flax_available() else ()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    FlaxGPTJModelTester(self      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       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(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       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(
               snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

   @tooslow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    GPTaTokenizer.from_pretrained('gpt2' ,				pad_token='<|endoftext|>' ,				padding_side='left'      )
       UpperCamelCase__         =    tokenizer(['Hello this is a long string', 'Hey'] ,				return_tensors='np' ,				padding=snake_case_ ,				truncation=snake_case_      )

       UpperCamelCase__         =    FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
       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(snake_case_ ,				skip_special_tokens=snake_case_      )

       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(snake_case_ ,				snake_case_      )

   @is_pt_flax_cross_test
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       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(snake_case_ ,				snake_case_      )
               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(snake_case_ ,				snake_case_      )

               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(snake_case_      ):
                   UpperCamelCase__         =    0
                   UpperCamelCase__         =    1
                   UpperCamelCase__         =    0
                   UpperCamelCase__         =    1
               UpperCamelCase__         =    pt_model_class(snake_case_      ).eval()
               UpperCamelCase__         =    model_class(snake_case_ ,				dtype=jnp.floataa      )

               UpperCamelCase__         =    convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,				snake_case_      )
               UpperCamelCase__         =    fx_state

               with torch.no_grad():
                   UpperCamelCase__         =    pt_model(**snake_case_      ).to_tuple()

               UpperCamelCase__         =    fx_model(**snake_case_      ).to_tuple()
               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      ) ,				'Output lengths differ between Flax and PyTorch'      )
               for fx_output, pt_output in zip(snake_case_ ,				snake_case_      ):
                   self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4E-2      )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(snake_case_      )
                   UpperCamelCase__         =    model_class.from_pretrained(snake_case_ ,				from_pt=snake_case_      )

               UpperCamelCase__         =    fx_model_loaded(**snake_case_      ).to_tuple()
               self.assertEqual(
                   len(snake_case_      ) ,				len(snake_case_      ) ,				'Output lengths differ between Flax and PyTorch'      )
               for fx_output_loaded, pt_output in zip(snake_case_ ,				snake_case_      ):
                   self.assert_almost_equals(fx_output_loaded[:, -1] ,				pt_output[:, -1].numpy() ,				4E-2      )

   @is_pt_flax_cross_test
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       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(snake_case_ ,				snake_case_      )
               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(snake_case_ ,				snake_case_      )

               UpperCamelCase__         =    pt_model_class(snake_case_      ).eval()
               UpperCamelCase__         =    model_class(snake_case_ ,				dtype=jnp.floataa      )

               UpperCamelCase__         =    load_flax_weights_in_pytorch_model(snake_case_ ,				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(snake_case_      ):
                   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(**snake_case_      ).to_tuple()

               UpperCamelCase__         =    fx_model(**snake_case_      ).to_tuple()
               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      ) ,				'Output lengths differ between Flax and PyTorch'      )
               for fx_output, pt_output in zip(snake_case_ ,				snake_case_      ):
                   self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4E-2      )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(snake_case_      )
                   UpperCamelCase__         =    pt_model_class.from_pretrained(snake_case_ ,				from_flax=snake_case_      )

               with torch.no_grad():
                   UpperCamelCase__         =    pt_model_loaded(**snake_case_      ).to_tuple()

               self.assertEqual(
                   len(snake_case_      ) ,				len(snake_case_      ) ,				'Output lengths differ between Flax and PyTorch'      )
               for fx_output, pt_output in zip(snake_case_ ,				snake_case_      ):
                   self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4E-2      )







   @tooslow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__         =    model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
           UpperCamelCase__         =    model(np.ones((1, 1)      )      )
           self.assertIsNotNone(snake_case_      )












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




