

"""simple docstring"""




import math
import sys





class     __UpperCamelCase				:


		def __init__(self				:	Union[str, Any]):
							A    =      0
							A    =      []
							A    =      {}  # To store the pos of node in array


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							return self.cur_size == 0


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      self.left(__SCREAMING_SNAKE_CASE)
							A    =      self.right(__SCREAMING_SNAKE_CASE)
							if lc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE)[0] < self.array(__SCREAMING_SNAKE_CASE)[0]:
												A    =      lc
							else:
												A    =      idx
							if rc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE)[0] < self.array(__SCREAMING_SNAKE_CASE)[0]:
												A    =      rc
							if smallest != idx:
												self.swap(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
												self.min_heapify(__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Any):
							# Inserts a node into the Priority Queue
							A    =      self.cur_size
							self.cur_size += 1
							self.array.append((sys.maxsize, tup[1]))
							self.decrease_key((sys.maxsize, tup[1])  ,  tup[0])


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							# Removes and returns the min element at top of priority queue
							A    =      self.array[0][1]
							A    =      self.array[self.cur_size - 1]
							self.cur_size -= 1
							self.min_heapify(1)
							del self.pos[min_node]
							return min_node


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							# returns the index of left child
							return 2 * i + 1


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]):
							# returns the index of right child
							return 2 * i + 2


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):
							# returns the index of parent
							return math.floor(i / 2)


		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							# swaps array elements at indices i and j
							# update the pos{}
							A    =      j
							A    =      i
							A    =      self.array[i]
							A    =      self.array[j]
							A    =      temp


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      self.pos[tup[1]]
							# assuming the new_d is atmost old_d
							A    =      (new_d, tup[1])
							while idx > 0 and self.array[self.par(__SCREAMING_SNAKE_CASE)][0] > self.array[idx][0]:
												self.swap(__SCREAMING_SNAKE_CASE  ,  self.par(__SCREAMING_SNAKE_CASE))
												A    =      self.par(__SCREAMING_SNAKE_CASE)





class     __UpperCamelCase				:


		def __init__(self				:	int  ,  __SCREAMING_SNAKE_CASE				:	int):
							A    =      {}  # To store graph: u -> (v,w)
							A    =      num  # Number of nodes in graph
							# To store the distance from source vertex
							A    =      [0] * self.num_nodes
							A    =      [-1] * self.num_nodes  # To store the path


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	int):
							#  Edge going from node u to v and v to u with weight w
							# u (w)-> v, v (w) -> u
							# Check if u already in graph
							if u in self.adjList:
												self.adjList[u].append((v, w))
							else:
												A    =      [(v, w)]

							# Assuming undirected graph
							if v in self.adjList:
												self.adjList[v].append((u, w))
							else:
												A    =      [(u, w)]


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							# u -> v(w)
							for u in self.adjList:
												print(__SCREAMING_SNAKE_CASE  ,  "->"  ,  " -> ".join(str(F"""{v}({w})""") for v, w in self.adjList[u]))


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]):
							# Flush old junk values in par[]
							A    =      [-1] * self.num_nodes
							# src is the source node
							A    =      0
							A    =      PriorityQueue()
							q.insert((0, src))  # (dist from src, node)
							for u in self.adjList:
												if u != src:
																	A    =      sys.maxsize  # Infinity
																	A    =      -1

							while not q.is_empty():
												A    =      q.extract_min()  # Returns node with the min dist from source
												# Update the distance of all the neighbours of u and
												# if their prev dist was INFINITY then push them in Q
												for v, w in self.adjList[u]:
																	A    =      self.dist[u] + w
																	if self.dist[v] > new_dist:
																						if self.dist[v] == sys.maxsize:
																											q.insert((new_dist, v))
																						else:
																											q.decrease_key((self.dist[v], v)  ,  __SCREAMING_SNAKE_CASE)
																						A    =      new_dist
																						A    =      u

        # Show the shortest distances from src
							self.show_distances(__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]):
							print(F"""Distance from node: {src}""")
							for u in range(self.num_nodes):
												print(F"""Node {u} has distance: {self.dist[u]}""")


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
							# To show the shortest path from src to dest
							# WARNING: Use it *after* calling dijkstra
							A    =      []
							A    =      0
							A    =      dest
							# Backtracking from dest to src
							while self.par[temp] != -1:
												path.append(__SCREAMING_SNAKE_CASE)
												if temp != src:
																	for v, w in self.adjList[temp]:
																						if v == self.par[temp]:
																											cost += w
																											break
												A    =      self.par[temp]
							path.append(__SCREAMING_SNAKE_CASE)
							path.reverse()

							print(F"""----Path to reach {dest} from {src}----""")
							for u in path:
												print(F"""{u}"""  ,  end=" ")
												if u != dest:
																	print("-> "  ,  end="")

							print("\nTotal cost of path: "  ,  __SCREAMING_SNAKE_CASE)


if __name__ == "__main__":
	__A  :					str												=	Graph(9)
	graph.add_edge(0, 1, 4)
	graph.add_edge(0, 7, 8)
	graph.add_edge(1, 2, 8)
	graph.add_edge(1, 7, 11)
	graph.add_edge(2, 3, 7)
	graph.add_edge(2, 8, 2)
	graph.add_edge(2, 5, 4)
	graph.add_edge(3, 4, 9)
	graph.add_edge(3, 5, 14)
	graph.add_edge(4, 5, 10)
	graph.add_edge(5, 6, 2)
	graph.add_edge(6, 7, 1)
	graph.add_edge(6, 8, 6)
	graph.add_edge(7, 8, 7)
	graph.show_graph()
	graph.dijkstra(0)
	graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21








"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	__A  :					Optional[int]												=	'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None   ):
					"""simple docstring"""
					if attention_mask is None:
										A    =      np.where(input_ids != config.pad_token_id    ,							1    ,							0   )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class     __UpperCamelCase				:


		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int=1_3  ,  __SCREAMING_SNAKE_CASE				:	List[str]=7  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	Dict=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=9_9  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=4  ,  __SCREAMING_SNAKE_CASE				:	Any=4  ,  __SCREAMING_SNAKE_CASE				:	str="gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Any=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=2_0  ,  __SCREAMING_SNAKE_CASE				:	Any=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1  ,  __SCREAMING_SNAKE_CASE				:	Any=0  ,  __SCREAMING_SNAKE_CASE				:	str=1_6  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  ):
							A    =      parent
							A    =      batch_size
							A    =      seq_length
							A    =      is_training
							A    =      use_labels
							A    =      vocab_size
							A    =      hidden_size
							A    =      num_hidden_layers
							A    =      num_attention_heads
							A    =      intermediate_size
							A    =      hidden_act
							A    =      hidden_dropout_prob
							A    =      attention_probs_dropout_prob
							A    =      max_position_embeddings
							A    =      eos_token_id
							A    =      pad_token_id
							A    =      bos_token_id
							A    =      embed_dim
							A    =      word_embed_proj_dim
							A    =      initializer_range
							A    =      False


		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							A    =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,  self.vocab_size)  ,  3  ,  self.vocab_size)
							A    =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,  dtype=np.intaa))  ,  -1)

							A    =      OPTConfig(
							    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  ffn_dim=self.intermediate_size  ,  dropout=self.hidden_dropout_prob  ,  attention_dropout=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  eos_token_id=self.eos_token_id  ,  bos_token_id=self.bos_token_id  ,  pad_token_id=self.pad_token_id  ,  embed_dim=self.embed_dim  ,  is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  word_embed_proj_dim=self.word_embed_proj_dim  ,  initializer_range=self.initializer_range  ,  use_cache=__SCREAMING_SNAKE_CASE  ,  )
							A    =      prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    ,					A    =      self.prepare_config_and_inputs()
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    =      inputs_dict["input_ids"]
							A    =      inputs_dict["attention_mask"]

							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.ones((input_ids.shape[0], max_length)  ,  dtype="i4")

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

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

							A    =      model(__SCREAMING_SNAKE_CASE)

							A    =      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				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    ,					A    =      (
							    inputs_dict["input_ids"],
							    inputs_dict["attention_mask"],
							)

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

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

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

							A    =      model(__SCREAMING_SNAKE_CASE  ,  attention_mask=__SCREAMING_SNAKE_CASE)

							A    =      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				(     _A  ,	unittest.TestCase  ,	_A		):
		SCREAMING_SNAKE_CASE              =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		SCREAMING_SNAKE_CASE              =					() if is_flax_available() else ()


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      FlaxOPTModelTester(self)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    ,					A    =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    ,					A    =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							for model_class_name in self.all_model_classes:
												A    =      model_class_name.from_pretrained("facebook/opt-125m")
												A    =      np.ones((1, 1)) * model.config.eos_token_id
												A    =      model(__SCREAMING_SNAKE_CASE)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





@require_sentencepiece
@require_flax
class     __UpperCamelCase				(     unittest.TestCase		):


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    =      FlaxOPTModel.from_pretrained("facebook/opt-350m")
							A    =      jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
							A    =      model(input_ids=__SCREAMING_SNAKE_CASE).last_hidden_state
							A    =      (1, 1_1, 5_1_2)
							self.assertEqual(output.shape  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.array(
							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]])
							self.assertTrue(jnp.allclose(output[:, :3, :3]  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





@require_flax
@slow
class     __UpperCamelCase				(     unittest.TestCase		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							super().setUp()
							A    =      "facebook/opt-350m"


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      FlaxOPTForCausalLM.from_pretrained(self.path_model)
							A    =      GPTaTokenizer.from_pretrained(self.path_model)

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  add_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							A    =      jnp.array(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ])
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))

							A    =      jax.jit(__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





@require_flax
@slow
class     __UpperCamelCase				(     unittest.TestCase		):


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      "facebook/opt-125m"

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of New York, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							A    =      []

							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							A    =      "facebook/opt-350m"

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of San Francisco, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							A    =      []
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "facebook/opt-125m"
							A    =      [
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ]  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  )

							A    =      jax.jit(model.generate)

							A    =      jit_generate(inputs["input_ids"]  ,  attention_mask=inputs["attention_mask"]).sequences

							A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      "facebook/opt-350m"

							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

							A    =      "left"

							# use different length sentences to test batching
							A    =      [
							    "Hello, my dog is a little",
							    "Today, I",
							]

							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE)
							A    =      inputs["input_ids"]

							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  attention_mask=inputs["attention_mask"]  ,  trace=__SCREAMING_SNAKE_CASE)

							A    =      tokenizer(sentences[0]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE)

							A    =      inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
							A    =      tokenizer(sentences[1]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  max_length=model.config.max_length - num_paddings)

							A    =      tokenizer.batch_decode(outputs[0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_non_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)

							A    =      [
							    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
							    "Today, I was in the middle of a conversation with a friend about the",
							]
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  [non_padded_sentence, padded_sentence])





