





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 	UpperCamelCase_							:


							def __init__(				self     :List[Any]				,					__A     :Dict				,					__A     :Tuple=14				,					__A     :Optional[int]=7				,					__A     :Any=True				,					__A     :Tuple=True				,					__A     :List[str]=False				,					__A     :int=True				,					__A     :Optional[Any]=99				,					__A     :Any=32				,					__A     :Optional[int]=4				,					__A     :Optional[Any]=4				,					__A     :Union[str, Any]=4				,					__A     :Tuple=37				,					__A     :Tuple="gelu"				,					__A     :Union[str, Any]=0.1				,					__A     :List[str]=0.1				,					__A     :Optional[int]=512				,					__A     :str=0.0_2				,					) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   seq_length
									SCREAMING_SNAKE_CASE__								=   is_training
									SCREAMING_SNAKE_CASE__								=   use_input_mask
									SCREAMING_SNAKE_CASE__								=   use_token_type_ids
									SCREAMING_SNAKE_CASE__								=   use_labels
									SCREAMING_SNAKE_CASE__								=   vocab_size
									SCREAMING_SNAKE_CASE__								=   hidden_size
									SCREAMING_SNAKE_CASE__								=   rotary_dim
									SCREAMING_SNAKE_CASE__								=   num_hidden_layers
									SCREAMING_SNAKE_CASE__								=   num_attention_heads
									SCREAMING_SNAKE_CASE__								=   intermediate_size
									SCREAMING_SNAKE_CASE__								=   hidden_act
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   max_position_embeddings
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1


							def   _snake_case	(				self     :str		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size		)

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

									SCREAMING_SNAKE_CASE__								=   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=__A				,					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   _snake_case	(				self     :int		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   config_and_inputs
									SCREAMING_SNAKE_CASE__								=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict


							def   _snake_case	(				self     :int				,					__A     :List[Any]				,					__A     :int				,					__A     :Optional[Any]				,					__A     :int		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   20
									SCREAMING_SNAKE_CASE__								=   model_class_name(__A		)

									SCREAMING_SNAKE_CASE__								=   model.init_cache(input_ids.shape[0]				,					__A		)
									SCREAMING_SNAKE_CASE__								=   jnp.ones((input_ids.shape[0], max_decoder_length)				,					dtype="""i4"""		)

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

									SCREAMING_SNAKE_CASE__								=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, -1:]				,					attention_mask=__A				,					past_key_values=outputs_cache.past_key_values				,					position_ids=__A				,					)

									SCREAMING_SNAKE_CASE__								=   model(__A		)

									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :Any				,					__A     :str				,					__A     :int				,					__A     :Any				,					__A     :Any		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   20
									SCREAMING_SNAKE_CASE__								=   model_class_name(__A		)

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

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

									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)
									SCREAMING_SNAKE_CASE__								=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, -1:]				,					past_key_values=outputs_cache.past_key_values				,					attention_mask=__A				,					position_ids=__A				,					)

									SCREAMING_SNAKE_CASE__								=   model(__A				,					attention_mask=__A		)

									SCREAMING_SNAKE_CASE__								=   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_							(  UpperCamelCase__					,	UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowerCamelCase_							     =       (FlaxGPTJForCausalLM,) if is_flax_available() else ()


							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaxGPTJModelTester(self		)


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__A				,					__A				,					__A				,					__A		)


							def   _snake_case	(				self     :int		) -> Optional[int]:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __A				,					__A				,					__A				,					__A		)


							@tooslow
							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   GPTaTokenizer.from_pretrained("""gpt2"""				,					pad_token="""<|endoftext|>"""				,					padding_side="""left"""		)
									SCREAMING_SNAKE_CASE__								=   tokenizer(["""Hello this is a long string""", """Hey"""]				,					return_tensors="""np"""				,					padding=__A				,					truncation=__A		)

									SCREAMING_SNAKE_CASE__								=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		)
									SCREAMING_SNAKE_CASE__								=   False
									SCREAMING_SNAKE_CASE__								=   model.config.eos_token_id

									SCREAMING_SNAKE_CASE__								=   jax.jit(model.generate		)

									SCREAMING_SNAKE_CASE__								=   jit_generate(
									    inputs["""input_ids"""]				,					attention_mask=inputs["""attention_mask"""]				,					pad_token_id=tokenizer.pad_token_id		).sequences

									SCREAMING_SNAKE_CASE__								=   tokenizer.batch_decode(__A				,					skip_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__								=   [
									    """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(__A				,					__A		)


							@is_pt_flax_cross_test
							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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
													SCREAMING_SNAKE_CASE__								=   self._prepare_for_class(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

													SCREAMING_SNAKE_CASE__								=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,					__A		)
													SCREAMING_SNAKE_CASE__								=   fx_state

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model(**__A		).to_tuple()

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

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

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


							@is_pt_flax_cross_test
							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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
													SCREAMING_SNAKE_CASE__								=   self._prepare_for_class(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

													SCREAMING_SNAKE_CASE__								=   pt_model_class(__A		).eval()
													SCREAMING_SNAKE_CASE__								=   model_class(__A				,					dtype=jnp.floataa		)

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

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

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model(**__A		).to_tuple()

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

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

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model_loaded(**__A		).to_tuple()

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


							@tooslow
							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
											SCREAMING_SNAKE_CASE__								=   model(np.ones((1, 1)		)		)
											self.assertIsNotNone(__A		)





'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






