'''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 __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    List[str]=None			,		__lowerCamelCase      :    int=None					)						->				List[Any]:
      '''simple docstring'''


      if attention_mask is None:
            lowercase_     			=   np.where(input_ids != config.pad_token_id			,		1			,		0					)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }





@require_flax
class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						List[str]=13 ,		__lowerCAmelCase					:						List[str]=7 ,		__lowerCAmelCase					:						Optional[Any]=True ,		__lowerCAmelCase					:						Optional[int]=False ,		__lowerCAmelCase					:						Dict=99 ,		__lowerCAmelCase					:						str=16 ,		__lowerCAmelCase					:						Union[str, Any]=2 ,		__lowerCAmelCase					:						Optional[int]=4 ,		__lowerCAmelCase					:						Optional[int]=4 ,		__lowerCAmelCase					:						List[Any]="gelu" ,		__lowerCAmelCase					:						Tuple=0.1 ,		__lowerCAmelCase					:						Tuple=0.1 ,		__lowerCAmelCase					:						Tuple=20 ,		__lowerCAmelCase					:						Tuple=2 ,		__lowerCAmelCase					:						List[Any]=1 ,		__lowerCAmelCase					:						List[Any]=0 ,		__lowerCAmelCase					:						Optional[int]=16 ,		__lowerCAmelCase					:						Any=16 ,		__lowerCAmelCase					:						Optional[int]=0.02 ,		)   ->       str:
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   seq_length
         lowercase_     			=   is_training
         lowercase_     			=   use_labels
         lowercase_     			=   vocab_size
         lowercase_     			=   hidden_size
         lowercase_     			=   num_hidden_layers
         lowercase_     			=   num_attention_heads
         lowercase_     			=   intermediate_size
         lowercase_     			=   hidden_act
         lowercase_     			=   hidden_dropout_prob
         lowercase_     			=   attention_probs_dropout_prob
         lowercase_     			=   max_position_embeddings
         lowercase_     			=   eos_token_id
         lowercase_     			=   pad_token_id
         lowercase_     			=   bos_token_id
         lowercase_     			=   embed_dim
         lowercase_     			=   word_embed_proj_dim
         lowercase_     			=   initializer_range
         lowercase_     			=   False


   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         lowercase_     			=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,		self.vocab_size) ,		3 ,		self.vocab_size)
         lowercase_     			=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,		dtype=np.intaa)) ,		-1)

         lowercase_     			=   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__ ,		)
         lowercase_     			=   prepare_opt_inputs_dict(lowerCAmelCase__ ,		lowerCAmelCase__)
         return config, inputs_dict


   def __UpperCAmelCase							( self					:						Dict)   ->       List[Any]:
         lowercase_     			=   self.prepare_config_and_inputs()
         return config, inputs_dict


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Tuple)   ->       int:
         lowercase_     			=   20
         lowercase_     			=   model_class_name(lowerCAmelCase__)

         lowercase_     			=   inputs_dict["""input_ids"""]
         lowercase_     			=   inputs_dict["""attention_mask"""]

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

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

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

         lowercase_     			=   model(lowerCAmelCase__)

         lowercase_     			=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1e-3 ,		msg=F'Max diff is {diff}')


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						List[str])   ->       Optional[Any]:
         lowercase_     			=   20
         lowercase_     			=   model_class_name(lowerCAmelCase__)

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

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

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

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

         lowercase_     			=   model(lowerCAmelCase__ ,		attention_mask=lowerCAmelCase__)

         lowercase_     			=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1e-3 ,		msg=F'Max diff is {diff}')





@require_flax
class 				lowercase    (    __snake_case			,					unittest.TestCase			,					__snake_case			):
   lowerCamelCase_            =(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   lowerCamelCase_            =() if is_flax_available() else ()


   def __UpperCAmelCase							( self					:						Dict)   ->       List[Any]:
         lowercase_     			=   FlaxOPTModelTester(self)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       List[Any]:
         lowercase_     			=   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)   ->       Any:
         lowercase_     			=   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					:						Dict)   ->       Optional[Any]:





         for model_class_name in self.all_model_classes:
               lowercase_     			=   model_class_name.from_pretrained("facebook/opt-125m")
               lowercase_     			=   np.ones((1, 1)) * model.config.eos_token_id
               lowercase_     			=   model(lowerCAmelCase__)
               self.assertIsNotNone(lowerCAmelCase__)





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


   @slow
   def __UpperCAmelCase							( self					:						Any)   ->       str:
         lowercase_     			=   FlaxOPTModel.from_pretrained("facebook/opt-350m")
         lowercase_     			=   jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
         lowercase_     			=   model(input_ids=lowerCAmelCase__).last_hidden_state
         lowercase_     			=   (1, 11, 512)
         self.assertEqual(output.shape ,		lowerCAmelCase__)
         lowercase_     			=   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 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       int:
         super().setUp()
         lowercase_     			=   """facebook/opt-350m"""


   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[int]:
         lowercase_     			=   FlaxOPTForCausalLM.from_pretrained(self.path_model)
         lowercase_     			=   GPTaTokenizer.from_pretrained(self.path_model)

         lowercase_     			=   [
             """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
         lowercase_     			=   tokenizer(lowerCAmelCase__ ,		return_tensors="jax" ,		padding=lowerCAmelCase__ ,		add_special_tokens=lowerCAmelCase__)
         lowercase_     			=   model(inputs.input_ids ,		attention_mask=inputs.attention_mask)[0].mean(axis=-1)
         lowercase_     			=   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))

         lowercase_     			=   jax.jit(lowerCAmelCase__)
         lowercase_     			=   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 				lowercase    (    unittest.TestCase			):


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       Any:
         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					:						Optional[int])   ->       Any:
         lowercase_     			=   """facebook/opt-125m"""

         lowercase_     			=   [
             """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""",
         ]

         lowercase_     			=   []

         lowercase_     			=   FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)
         lowercase_     			=   GPTaTokenizer.from_pretrained(lowerCAmelCase__)

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

               lowercase_     			=   model.generate(lowerCAmelCase__ ,		max_length=10)
               lowercase_     			=   generated_ids[0]

               lowercase_     			=   tokenizer.batch_decode(lowerCAmelCase__ ,		skip_special_tokens=lowerCAmelCase__)
               predicted_outputs += generated_string

         self.assertListEqual(lowerCAmelCase__ ,		lowerCAmelCase__)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       List[Any]:
         lowercase_     			=   """facebook/opt-350m"""

         lowercase_     			=   [
             """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""",
         ]

         lowercase_     			=   []
         lowercase_     			=   FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)
         lowercase_     			=   GPTaTokenizer.from_pretrained(lowerCAmelCase__)

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

               lowercase_     			=   model.generate(lowerCAmelCase__ ,		max_length=10)
               lowercase_     			=   generated_ids[0]

               lowercase_     			=   tokenizer.batch_decode(lowerCAmelCase__ ,		skip_special_tokens=lowerCAmelCase__)
               predicted_outputs += generated_string

         self.assertListEqual(lowerCAmelCase__ ,		lowerCAmelCase__)


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

         lowercase_     			=   jax.jit(model.generate)

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

         lowercase_     			=   tokenizer.batch_decode(lowerCAmelCase__ ,		skip_special_tokens=lowerCAmelCase__)

         self.assertIsNotNone(lowerCAmelCase__ ,		lowerCAmelCase__)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Dict:
         lowercase_     			=   """facebook/opt-350m"""

         lowercase_     			=   GPTaTokenizer.from_pretrained(lowerCAmelCase__)
         lowercase_     			=   FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)

         lowercase_     			=   """left"""

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

         lowercase_     			=   tokenizer(lowerCAmelCase__ ,		return_tensors="jax" ,		padding=lowerCAmelCase__)
         lowercase_     			=   inputs["""input_ids"""]

         lowercase_     			=   model.generate(input_ids=lowerCAmelCase__ ,		attention_mask=inputs["attention_mask"] ,		trace=lowerCAmelCase__)

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

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

         lowercase_     			=   tokenizer.batch_decode(outputs[0] ,		skip_special_tokens=lowerCAmelCase__)
         lowercase_     			=   tokenizer.decode(output_non_padded[0][0] ,		skip_special_tokens=lowerCAmelCase__)
         lowercase_     			=   tokenizer.decode(output_padded[0][0] ,		skip_special_tokens=lowerCAmelCase__)

         lowercase_     			=   [
             """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 __future__ import annotations

_lowerCamelCase :				Optional[int]														= """Alexander Joslin"""



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







     if number <= 0:
          _UpperCamelCase  :Optional[Any]       =F'''n must be greater than 0. Got n = {number}'''
          raise ValueError(__a      )

     _UpperCamelCase  :Optional[Any]       =[number + 1] * (number + 1)

     # starting position
     _UpperCamelCase  :int       =0
     for i in range(1			,      __a      ):
          _UpperCamelCase  :List[str]       =min(table[i + 1]			,      table[i] + 1      )
          # check if out of bounds
          if i * 2 <= number:
               _UpperCamelCase  :int       =min(table[i * 2]			,      table[i] + 1      )
          # check if out of bounds
          if i * 3 <= number:
               _UpperCamelCase  :Tuple       =min(table[i * 3]			,      table[i] + 1      )
     return table[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()