

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__							=			"platform"

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def   _lowerCAmelCase(						__A ,							__A ,							__A=None ,							__A=None      ):
  if attention_mask is None:
    UpperCAmelCase          =	np.where(input_ids != config.pad_token_id ,							1 ,							0      )
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }






@require_flax
class     __magic_name__     :


 def __init__(    self		:			Tuple      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			int=1_3      ,    lowerCAmelCase__		:			Union[str, Any]=7      ,    lowerCAmelCase__		:			Union[str, Any]=True      ,    lowerCAmelCase__		:			Dict=False      ,    lowerCAmelCase__		:			Dict=9_9      ,    lowerCAmelCase__		:			List[str]=1_6      ,    lowerCAmelCase__		:			Optional[int]=2      ,    lowerCAmelCase__		:			Dict=4      ,    lowerCAmelCase__		:			Tuple=4      ,    lowerCAmelCase__		:			str="gelu"      ,    lowerCAmelCase__		:			Optional[Any]=0.1      ,    lowerCAmelCase__		:			Optional[Any]=0.1      ,    lowerCAmelCase__		:			int=2_0      ,    lowerCAmelCase__		:			Dict=2      ,    lowerCAmelCase__		:			Optional[Any]=1      ,    lowerCAmelCase__		:			Optional[Any]=0      ,    lowerCAmelCase__		:			List[str]=1_6      ,    lowerCAmelCase__		:			Tuple=1_6      ,    lowerCAmelCase__		:			int=0.02      ,    )     ->   List[Any]:
   UpperCAmelCase          =	parent
   UpperCAmelCase          =	batch_size
   UpperCAmelCase          =	seq_length
   UpperCAmelCase          =	is_training
   UpperCAmelCase          =	use_labels
   UpperCAmelCase          =	vocab_size
   UpperCAmelCase          =	hidden_size
   UpperCAmelCase          =	num_hidden_layers
   UpperCAmelCase          =	num_attention_heads
   UpperCAmelCase          =	intermediate_size
   UpperCAmelCase          =	hidden_act
   UpperCAmelCase          =	hidden_dropout_prob
   UpperCAmelCase          =	attention_probs_dropout_prob
   UpperCAmelCase          =	max_position_embeddings
   UpperCAmelCase          =	eos_token_id
   UpperCAmelCase          =	pad_token_id
   UpperCAmelCase          =	bos_token_id
   UpperCAmelCase          =	embed_dim
   UpperCAmelCase          =	word_embed_proj_dim
   UpperCAmelCase          =	initializer_range
   UpperCAmelCase          =	False


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

   UpperCAmelCase          =	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          =	prepare_opt_inputs_dict(lowerCAmelCase__      ,    lowerCAmelCase__     )
   return config, inputs_dict


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


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			Optional[int]     )     ->   Tuple:
   UpperCAmelCase          =	2_0
   UpperCAmelCase          =	model_class_name(lowerCAmelCase__     )

   UpperCAmelCase          =	inputs_dict["input_ids"]
   UpperCAmelCase          =	inputs_dict["attention_mask"]

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

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

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

   UpperCAmelCase          =	model(lowerCAmelCase__     )

   UpperCAmelCase          =	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		:			int      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			int     )     ->   Dict:
   UpperCAmelCase          =	2_0
   UpperCAmelCase          =	model_class_name(lowerCAmelCase__     )

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

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

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

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

   UpperCAmelCase          =	model(lowerCAmelCase__      ,    attention_mask=lowerCAmelCase__     )

   UpperCAmelCase          =	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     __magic_name__     (							_snake_case						,    unittest.TestCase						,    _snake_case     ):
 UpperCAmelCase				     =    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 UpperCAmelCase				     =    () if is_flax_available() else ()


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   str:
   UpperCAmelCase          =	FlaxOPTModelTester(self     )


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






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


 @slow
 def _UpperCamelCase						(    self		:			Dict     )     ->   Optional[Any]:
   UpperCAmelCase          =	FlaxOPTModel.from_pretrained("facebook/opt-350m"     )
   UpperCAmelCase          =	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]]     )
   UpperCAmelCase          =	model(input_ids=lowerCAmelCase__     ).last_hidden_state
   UpperCAmelCase          =	(1, 1_1, 5_1_2)
   self.assertEqual(output.shape      ,    lowerCAmelCase__     )
   UpperCAmelCase          =	jnp.array(
       [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]     )
   self.assertTrue(jnp.allclose(output[:, :3, :3]      ,    lowerCAmelCase__      ,    atol=4e-2     )     )






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


 def _UpperCamelCase						(    self		:			Tuple     )     ->   List[Any]:
   super().setUp()
   UpperCAmelCase          =	"facebook/opt-350m"


 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   Union[str, Any]:
   UpperCAmelCase          =	FlaxOPTForCausalLM.from_pretrained(self.path_model     )
   UpperCAmelCase          =	GPTaTokenizer.from_pretrained(self.path_model     )

   UpperCAmelCase          =	[
       "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          =	tokenizer(lowerCAmelCase__      ,    return_tensors="jax"      ,    padding=lowerCAmelCase__      ,    add_special_tokens=lowerCAmelCase__     )
   UpperCAmelCase          =	model(inputs.input_ids      ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
   UpperCAmelCase          =	jnp.array(
       [
           [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
           [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
           [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
           [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
       ]     )
   self.assertTrue(jnp.allclose(lowerCAmelCase__      ,    lowerCAmelCase__      ,    atol=4e-2     )     )

   UpperCAmelCase          =	jax.jit(lowerCAmelCase__     )
   UpperCAmelCase          =	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     __magic_name__     (							unittest.TestCase     ):


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

   UpperCAmelCase          =	[
       "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          =	[]

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

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

     UpperCAmelCase          =	model.generate(lowerCAmelCase__      ,    max_length=1_0     )
     UpperCAmelCase          =	generated_ids[0]

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

   self.assertListEqual(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Any     )     ->   Optional[Any]:
   UpperCAmelCase          =	"facebook/opt-350m"

   UpperCAmelCase          =	[
       "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          =	[]
   UpperCAmelCase          =	FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
   UpperCAmelCase          =	GPTaTokenizer.from_pretrained(lowerCAmelCase__     )

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

     UpperCAmelCase          =	model.generate(lowerCAmelCase__      ,    max_length=1_0     )
     UpperCAmelCase          =	generated_ids[0]

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

   self.assertListEqual(lowerCAmelCase__      ,    lowerCAmelCase__     )


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

   UpperCAmelCase          =	jax.jit(model.generate     )

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

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

   self.assertIsNotNone(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			str     )     ->   Any:
   UpperCAmelCase          =	"facebook/opt-350m"

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

   UpperCAmelCase          =	"left"

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

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

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

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

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

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

   UpperCAmelCase          =	[
       "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]     )






def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(a + 1 ,							400      ):
      for c in range(b + 1 ,							500      ):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1
def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(400      ):
      UpperCAmelCase          =	1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1
def   _lowerCAmelCase(						):
  import timeit

  print(
      timeit.timeit("solution()" ,							setup="from __main__ import solution" ,							number=1000      )      )
  print(
      timeit.timeit(
          "solution_fast()" ,							setup="from __main__ import solution_fast" ,							number=1000      )      )


if __name__ == "__main__":
      print(f"{solution() = }")



