'''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
      _lowerCamelCase :				Any														= """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def  _lowerCAmelCase				(				__a			,      __a			,      __a=None			,      __a=None      )  ->      List[Any]:
     '''simple docstring'''







     if attention_mask is None:
          _UpperCamelCase  :List[str]       =np.where(input_ids != config.pad_token_id			,      1			,      0      )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }




@require_flax
class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=13					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=99					,      lowerCAmelCase__=16					,      lowerCAmelCase__=2					,      lowerCAmelCase__=4					,      lowerCAmelCase__=4					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=20					,      lowerCAmelCase__=2					,      lowerCAmelCase__=1					,      lowerCAmelCase__=0					,      lowerCAmelCase__=16					,      lowerCAmelCase__=16					,      lowerCAmelCase__=0.02					,      )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Dict       =parent
        _UpperCamelCase  :List[Any]       =batch_size
        _UpperCamelCase  :Dict       =seq_length
        _UpperCamelCase  :List[str]       =is_training
        _UpperCamelCase  :Optional[int]       =use_labels
        _UpperCamelCase  :int       =vocab_size
        _UpperCamelCase  :List[str]       =hidden_size
        _UpperCamelCase  :Union[str, Any]       =num_hidden_layers
        _UpperCamelCase  :Union[str, Any]       =num_attention_heads
        _UpperCamelCase  :Optional[Any]       =intermediate_size
        _UpperCamelCase  :Any       =hidden_act
        _UpperCamelCase  :Dict       =hidden_dropout_prob
        _UpperCamelCase  :int       =attention_probs_dropout_prob
        _UpperCamelCase  :Optional[int]       =max_position_embeddings
        _UpperCamelCase  :str       =eos_token_id
        _UpperCamelCase  :Tuple       =pad_token_id
        _UpperCamelCase  :Any       =bos_token_id
        _UpperCamelCase  :int       =embed_dim
        _UpperCamelCase  :List[str]       =word_embed_proj_dim
        _UpperCamelCase  :int       =initializer_range
        _UpperCamelCase  :Any       =False



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





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

        _UpperCamelCase  :Dict       =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=lowerCAmelCase__					,      word_embed_proj_dim=self.word_embed_proj_dim					,      initializer_range=self.initializer_range					,      use_cache=lowerCAmelCase__					,      )
        _UpperCamelCase  :List[Any]       =prepare_opt_inputs_dict(lowerCAmelCase__					,      lowerCAmelCase__     )
        return config, inputs_dict



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =self.prepare_config_and_inputs()
        return config, inputs_dict



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =20
        _UpperCamelCase  :List[Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :str       =inputs_dict["""input_ids"""]
        _UpperCamelCase  :str       =inputs_dict["""attention_mask"""]

        _UpperCamelCase  :Union[str, Any]       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =jnp.ones((input_ids.shape[0], max_length)					,      dtype="""i4"""     )

        _UpperCamelCase  :Dict       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)					,      )
        _UpperCamelCase  :str       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :Union[str, Any]       =model(
            input_ids[:, -1:]					,      attention_mask=lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :List[Any]       =model(lowerCAmelCase__     )

        _UpperCamelCase  :List[Any]       =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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =20
        _UpperCamelCase  :Any       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase       ,      _UpperCamelCase  :int       =(
            inputs_dict["""input_ids"""],
            inputs_dict["""attention_mask"""],
        )

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

        _UpperCamelCase  :int       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)					,      )

        _UpperCamelCase  :Union[str, Any]       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )
        _UpperCamelCase  :Any       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :Any       =model(
            input_ids[:, -1:]					,      past_key_values=outputs_cache.past_key_values					,      attention_mask=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =model(lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =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    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase       ,				__snake_case     ):
   __UpperCAmelCase           =						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   __UpperCAmelCase           =						() if is_flax_available() else ()



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =FlaxOPTModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =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(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )




   @slow
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :Dict       =model_class_name.from_pretrained("""facebook/opt-125m"""     )
             _UpperCamelCase  :Optional[int]       =np.ones((1, 1)     ) * model.config.eos_token_id
             _UpperCamelCase  :Union[str, Any]       =model(lowerCAmelCase__     )
             self.assertIsNotNone(lowerCAmelCase__     )




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



   @slow
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =FlaxOPTModel.from_pretrained("""facebook/opt-350m"""     )
        _UpperCamelCase  :Union[str, Any]       =jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     )
        _UpperCamelCase  :Optional[Any]       =model(input_ids=lowerCAmelCase__     ).last_hidden_state
        _UpperCamelCase  :int       =(1, 11, 512)
        self.assertEqual(output.shape					,      lowerCAmelCase__     )
        _UpperCamelCase  :Optional[int]       =jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
        self.assertTrue(jnp.allclose(output[:, :3, :3]					,      lowerCAmelCase__					,      atol=4e-2     )     )




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



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        super().setUp()
        _UpperCamelCase  :List[str]       ="""facebook/opt-350m"""




   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Any       =FlaxOPTForCausalLM.from_pretrained(self.path_model     )
        _UpperCamelCase  :Optional[Any]       =GPTaTokenizer.from_pretrained(self.path_model     )

        _UpperCamelCase  :List[str]       =[
            """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
        _UpperCamelCase  :int       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__					,      add_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :int       =model(inputs.input_ids					,      attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
        _UpperCamelCase  :Optional[Any]       =jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]     )
        self.assertTrue(jnp.allclose(lowerCAmelCase__					,      lowerCAmelCase__					,      atol=4e-2     )     )

        _UpperCamelCase  :Optional[int]       =jax.jit(lowerCAmelCase__     )
        _UpperCamelCase  :int       =model(inputs.input_ids					,      attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
        self.assertTrue(jnp.allclose(lowerCAmelCase__					,      lowerCAmelCase__					,      atol=4e-2     )     )




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



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        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  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       ="""facebook/opt-125m"""

        _UpperCamelCase  :Dict       =[
            """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""",
        ]

        _UpperCamelCase  :int       =[]

        _UpperCamelCase  :Union[str, Any]       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )

        for prompt in self.prompts:
             _UpperCamelCase  :Tuple       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""     ).input_ids

             _UpperCamelCase  :str       =model.generate(lowerCAmelCase__					,      max_length=10     )
             _UpperCamelCase  :str       =generated_ids[0]

             _UpperCamelCase  :Dict       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
             predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""facebook/opt-350m"""

        _UpperCamelCase  :List[str]       =[
            """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""",
        ]

        _UpperCamelCase  :Dict       =[]
        _UpperCamelCase  :Tuple       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :str       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )

        for prompt in self.prompts:
             _UpperCamelCase  :Dict       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""     ).input_ids

             _UpperCamelCase  :List[str]       =model.generate(lowerCAmelCase__					,      max_length=10     )
             _UpperCamelCase  :Tuple       =generated_ids[0]

             _UpperCamelCase  :List[str]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
             predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :int       ="""facebook/opt-125m"""
        _UpperCamelCase  :Union[str, Any]       =[
            """Today is a beautiful day and I want to thank""",
            """In the city of Rome Canaver Canaver Canaver Canaver""",
        ]
        _UpperCamelCase  :Any       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :str       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =tokenizer(
            [
                """Today is a beautiful day and I want to""",
                """In the city of""",
            ]					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =jax.jit(model.generate     )

        _UpperCamelCase  :Union[str, Any]       =jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]     ).sequences

        _UpperCamelCase  :List[Any]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )

        self.assertIsNotNone(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ="""facebook/opt-350m"""

        _UpperCamelCase  :List[Any]       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""left"""

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

        _UpperCamelCase  :Tuple       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__     )
        _UpperCamelCase  :Any       =inputs["""input_ids"""]

        _UpperCamelCase  :List[str]       =model.generate(input_ids=lowerCAmelCase__					,      attention_mask=inputs["""attention_mask"""]					,      trace=lowerCAmelCase__     )

        _UpperCamelCase  :int       =tokenizer(sentences[0]					,      return_tensors="""jax"""     ).input_ids
        _UpperCamelCase  :Any       =model.generate(input_ids=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
        _UpperCamelCase  :Dict       =tokenizer(sentences[1]					,      return_tensors="""jax"""     ).input_ids
        _UpperCamelCase  :Optional[Any]       =model.generate(input_ids=lowerCAmelCase__					,      max_length=model.config.max_length - num_paddings     )

        _UpperCamelCase  :int       =tokenizer.batch_decode(outputs[0]					,      skip_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =tokenizer.decode(output_non_padded[0][0]					,      skip_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.decode(output_padded[0][0]					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Any       =[
            """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(lowerCAmelCase__					,      lowerCAmelCase__     )
        self.assertListEqual(lowerCAmelCase__					,      [non_padded_sentence, padded_sentence]     )
'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__a			,      -1      )



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







     if not balanced_parentheses(__a      ):
          raise ValueError("""Mismatched parentheses"""      )
     _UpperCamelCase  :Stack[str]       =Stack()
     _UpperCamelCase  :Optional[int]       =[]
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__a      )
          elif char == "(":
               stack.push(__a      )
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()      )
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__a      ) <= precedence(stack.peek()      ):
                    postfix.append(stack.pop()      )
               stack.push(__a      )
     while not stack.is_empty():
          postfix.append(stack.pop()      )
     return " ".join(__a      )


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

      testmod()
      _lowerCamelCase :				Tuple														= """a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))