






"""simple docstring"""




from __future__ import annotations

import os





class     __UpperCamelCase				:
		SCREAMING_SNAKE_CASE              =					(
		    '''High card''',
		    '''One pair''',
		    '''Two pairs''',
		    '''Three of a kind''',
		    '''Straight''',
		    '''Flush''',
		    '''Full house''',
		    '''Four of a kind''',
		    '''Straight flush''',
		    '''Royal flush''',
		)

		SCREAMING_SNAKE_CASE              =					(
		    '''''',  # placeholder as tuples are zero-indexed
		    '''One''',
		    '''Two''',
		    '''Three''',
		    '''Four''',
		    '''Five''',
		    '''Six''',
		    '''Seven''',
		    '''Eight''',
		    '''Nine''',
		    '''Ten''',
		    '''Jack''',
		    '''Queen''',
		    '''King''',
		    '''Ace''',
		)


		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	str):






							if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												A    =      F"""Hand should be of type \'str\': {hand!r}"""
												raise TypeError(__SCREAMING_SNAKE_CASE)
							# split removes duplicate whitespaces so no need of strip
							if len(hand.split(" ")) != 5:
												A    =      F"""Hand should contain only 5 cards: {hand!r}"""
												raise ValueError(__SCREAMING_SNAKE_CASE)
							A    =      hand
							A    =      0
							A    =      0
							A    =      self._internal_state()
							A    =      self._get_hand_type()
							A    =      self._card_values[0]


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self._hand


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	PokerHand):






							if self._hand_type > other._hand_type:
												return "Win"
							elif self._hand_type < other._hand_type:
												return "Loss"
							elif self._first_pair == other._first_pair:
												if self._second_pair == other._second_pair:
																	return self._compare_cards(__SCREAMING_SNAKE_CASE)
												else:
																	return "Win" if self._second_pair > other._second_pair else "Loss"
							return "Win" if self._first_pair > other._first_pair else "Loss"


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							A    =      PokerHand._HAND_NAME[self._hand_type - 1_4]
							A    =      PokerHand._CARD_NAME[self._high_card]
							A    =      PokerHand._CARD_NAME[self._first_pair]
							A    =      PokerHand._CARD_NAME[self._second_pair]
							if self._hand_type in [2_2, 1_9, 1_8]:
												return name + F""", {high}-high"""
							elif self._hand_type in [2_1, 1_7, 1_5]:
												return name + F""", {paira}s"""
							elif self._hand_type in [2_0, 1_6]:
												A    =      "over" if self._hand_type == 2_0 else "and"
												return name + F""", {paira}s {join} {paira}s"""
							elif self._hand_type == 2_3:
												return name
							else:
												return name + F""", {high}"""


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	PokerHand):






							for index, card_value in enumerate(self._card_values):
												if card_value != other._card_values[index]:
																	return "Win" if card_value > other._card_values[index] else "Loss"
							return "Tie"


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):






							if self._is_flush():
												if self._is_five_high_straight() or self._is_straight():
																	return 2_3 if sum(self._card_values) == 6_0 else 2_2
												return 1_9
							elif self._is_five_high_straight() or self._is_straight():
												return 1_8
							return 1_4 + self._is_same_kind()


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							return len(self._card_suit) == 1


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):






							if sorted(self._card_values) == [2, 3, 4, 5, 1_4]:
												if self._card_values[0] == 1_4:
																	# Remember, our list is sorted in reverse order
																	A    =      self._card_values.pop(0)
																	self._card_values.append(__SCREAMING_SNAKE_CASE)
												return True
							return False


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):






							for i in range(4):
												if self._card_values[i] - self._card_values[i + 1] != 1:
																	return False
							return True


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							A    =      0
							for i in range(4):
												# Compare two cards at a time, if they are same increase 'kind',
												# add the value of the card to val1, if it is repeating again we
												# will add 2 to 'kind' as there are now 3 cards with same value.
												# If we get card of different value than val1, we will do the same
												# thing with val2
												if self._card_values[i] == self._card_values[i + 1]:
																	if not vala:
																						A    =      self._card_values[i]
																						kind += 1
																	elif vala == self._card_values[i]:
																						kind += 2
																	elif not vala:
																						A    =      self._card_values[i]
																						kind += 1
																	elif vala == self._card_values[i]:
																						kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
							A    =      kind + 2 if kind in [4, 5] else kind
							# first meaning first pair to compare in 'compare_with'
							A    =      max(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							A    =      min(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							# If it's full house (three count pair + two count pair), make sure
							# first pair is three count and if not then switch them both.
							if kind == 6 and self._card_values.count(__SCREAMING_SNAKE_CASE) != 3:
												A    =      second, first
							A    =      first
							A    =      second
							return kind


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
							A    =      self._hand.translate(str.maketrans(__SCREAMING_SNAKE_CASE)).split()
							A    =      [int(card[:-1]) for card in new_hand]
							A    =      {card[-1] for card in new_hand}
							return sorted(__SCREAMING_SNAKE_CASE  ,  reverse=__SCREAMING_SNAKE_CASE), card_suit


		def __repr__(self				:	Any):
							return F"""{self.__class__}(\"{self._hand}\")"""


		def __str__(self				:	Optional[int]):
							return self._hand


		def __eq__(self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):






							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												return self.compare_with(__SCREAMING_SNAKE_CASE) == "Tie"
							return NotImplemented


		def __lt__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):






							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												return self.compare_with(__SCREAMING_SNAKE_CASE) == "Loss"
							return NotImplemented


		def __le__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):






							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												return self < other or self == other
							return NotImplemented


		def __gt__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):






							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												return not self < other and self != other
							return NotImplemented


		def __ge__(self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):






							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												return not self < other
							return NotImplemented


		def __hash__(self				:	Optional[Any]):
							return object.__hash__(self)

def      __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					# Solution for problem number 54 from Project Euler
					# Input from poker_hands.txt file
					A    =      0
					A    =      os.path.abspath(os.path.dirname(__lowerCAmelCase   )   )
					A    =      os.path.join(__lowerCAmelCase    ,							"poker_hands.txt"   )
					with open(__lowerCAmelCase   ) as file_hand:
										for line in file_hand:
															A    =      line[:14].strip()
															A    =      line[15:].strip()
															A    =      PokerHand(__lowerCAmelCase   ), PokerHand(__lowerCAmelCase   )
															A    =      player.compare_with(__lowerCAmelCase   )
															if output == "Win":
																				answer += 1
					return answer


if __name__ == "__main__":
	solution()








"""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])





