code: 
  
  
  
  
  
  
  """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()
  
  
  
  
  
  
style_context: 
  
  """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])
  
  
  
  
  
  