
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 	A_       :




	'''simple docstring'''


	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=5		,   snake_case=4		,   snake_case=37		,   snake_case="gelu"		,   snake_case=0.1		,   snake_case=0.1		,   snake_case=512		,   snake_case=0.02		,   ):
		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   seq_length
		lowercase					  =   is_training
		lowercase					  =   use_input_mask
		lowercase					  =   use_token_type_ids
		lowercase					  =   use_labels
		lowercase					  =   vocab_size
		lowercase					  =   hidden_size
		lowercase					  =   num_hidden_layers
		lowercase					  =   num_attention_heads
		lowercase					  =   intermediate_size
		lowercase					  =   hidden_act
		lowercase					  =   hidden_dropout_prob
		lowercase					  =   attention_probs_dropout_prob
		lowercase					  =   max_position_embeddings
		lowercase					  =   initializer_range
		lowercase					  =   None
		lowercase					  =   vocab_size - 1
		lowercase					  =   vocab_size - 1
		lowercase					  =   vocab_size - 1





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

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

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.prepare_config_and_inputs()

		lowercase					  =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
		lowercase					  =   ids_tensor([self.batch_size, self.seq_length]		,   vocab_size=2	)

		return (
		    config,
		    input_ids,
		    attention_mask,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   20
		lowercase					  =   model_class_name(lowercase__	)

		lowercase					  =   model.init_cache(input_ids.shape[0]		,   lowercase__	)
		lowercase					  =   jnp.ones((input_ids.shape[0], max_decoder_length)		,   dtype='i4'	)

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

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

		lowercase					  =   model(lowercase__	)

		lowercase					  =   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	):
		lowercase					  =   20
		lowercase					  =   model_class_name(lowercase__	)

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

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

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

		lowercase					  =   model(lowercase__		,   attention_mask=lowercase__	)

		lowercase					  =   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 	A_       (      _UpperCAmelCase				,     _UpperCAmelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Union[str, Any]										=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	_UpperCamelCase					:					List[Any]										=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   FlaxGPTaModelTester(self	)





	def      SCREAMING_SNAKE_CASE__						(	self	):






		for model_class_name in self.all_model_classes:
			lowercase					  =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward(lowercase__		,   lowercase__		,   lowercase__		,   lowercase__	)





	def      SCREAMING_SNAKE_CASE__						(	self	):






		for model_class_name in self.all_model_classes:
			lowercase					  =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward_with_attn_mask(
			    lowercase__		,   lowercase__		,   lowercase__		,   lowercase__	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTaTokenizer.from_pretrained('gpt2'		,   pad_token='</s>'		,   padding_side='left'	)
		lowercase					  =   tokenizer(['Hello this is a long string', 'Hey']		,   return_tensors='np'		,   padding=lowercase__		,   truncation=lowercase__	)

		lowercase					  =   FlaxGPTaLMHeadModel.from_pretrained('gpt2'	)
		lowercase					  =   False
		lowercase					  =   model.config.eos_token_id

		lowercase					  =   jax.jit(model.generate	)

		lowercase					  =   jit_generate(inputs['input_ids']		,   attention_mask=inputs['attention_mask']	).sequences

		lowercase					  =   tokenizer.batch_decode(lowercase__		,   skip_special_tokens=lowercase__	)

		lowercase					  =   [
		    "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(lowercase__		,   lowercase__	)





	@is_pt_flax_cross_test
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   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
				lowercase					  =   self._prepare_for_class(lowercase__		,   lowercase__	)
				lowercase					  =   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

				lowercase					  =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,   lowercase__	)
				lowercase					  =   fx_state

				with torch.no_grad():
					lowercase					  =   pt_model(**lowercase__	).to_tuple()

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

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

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





	@is_pt_flax_cross_test
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   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
				lowercase					  =   self._prepare_for_class(lowercase__		,   lowercase__	)
				lowercase					  =   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

				lowercase					  =   pt_model_class(lowercase__	).eval()
				lowercase					  =   model_class(lowercase__		,   dtype=jnp.floataa	)

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

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

				with torch.no_grad():
					lowercase					  =   pt_model(**lowercase__	).to_tuple()

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

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

				with torch.no_grad():
					lowercase					  =   pt_model_loaded(**lowercase__	).to_tuple()

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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):






		for model_class_name in self.all_model_classes:
			lowercase					  =   model_class_name.from_pretrained('gpt2'		,   from_pt=lowercase__	)
			lowercase					  =   model(np.ones((1, 1)	)	)
			self.assertIsNotNone(lowercase__	)



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

