



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
 __snake_case							 =  'platform'

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def   _A       (    SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     int=None					,  SCREAMING_SNAKE_CASE__  :     Tuple=None	):
    if attention_mask is None:
        UpperCamelCase	:Tuple     							=       np.where(input_ids != config.pad_token_id					,  1					,  0	)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }







@require_flax
class       UpperCAmelCase_						:



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=13   ,    SCREAMING_SNAKE_CASE_=7   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=False   ,    SCREAMING_SNAKE_CASE_=99   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=2   ,    SCREAMING_SNAKE_CASE_=4   ,    SCREAMING_SNAKE_CASE_=4   ,    SCREAMING_SNAKE_CASE_="gelu"   ,    SCREAMING_SNAKE_CASE_=0.1   ,    SCREAMING_SNAKE_CASE_=0.1   ,    SCREAMING_SNAKE_CASE_=20   ,    SCREAMING_SNAKE_CASE_=2   ,    SCREAMING_SNAKE_CASE_=1   ,    SCREAMING_SNAKE_CASE_=0   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=0.02   ,    )    ->				Dict:
          UpperCamelCase	:List[Any]     							=       parent
          UpperCamelCase	:Tuple     							=       batch_size
          UpperCamelCase	:Optional[int]     							=       seq_length
          UpperCamelCase	:Any     							=       is_training
          UpperCamelCase	:Any     							=       use_labels
          UpperCamelCase	:Any     							=       vocab_size
          UpperCamelCase	:List[str]     							=       hidden_size
          UpperCamelCase	:Optional[Any]     							=       num_hidden_layers
          UpperCamelCase	:List[Any]     							=       num_attention_heads
          UpperCamelCase	:Tuple     							=       intermediate_size
          UpperCamelCase	:Union[str, Any]     							=       hidden_act
          UpperCamelCase	:Any     							=       hidden_dropout_prob
          UpperCamelCase	:Optional[int]     							=       attention_probs_dropout_prob
          UpperCamelCase	:Optional[int]     							=       max_position_embeddings
          UpperCamelCase	:Any     							=       eos_token_id
          UpperCamelCase	:Optional[Any]     							=       pad_token_id
          UpperCamelCase	:Optional[Any]     							=       bos_token_id
          UpperCamelCase	:Union[str, Any]     							=       embed_dim
          UpperCamelCase	:Dict     							=       word_embed_proj_dim
          UpperCamelCase	:Any     							=       initializer_range
          UpperCamelCase	:Any     							=       False
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:str     							=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,    self.vocab_size			)   ,    3   ,    self.vocab_size			)
          UpperCamelCase	:int     							=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,    dtype=np.intaa			))   ,    -1			)

          UpperCamelCase	:Union[str, Any]     							=       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=UpperCamelCase__   ,    word_embed_proj_dim=self.word_embed_proj_dim   ,    initializer_range=self.initializer_range   ,    use_cache=UpperCamelCase__   ,    )
          UpperCamelCase	:Union[str, Any]     							=       prepare_opt_inputs_dict(UpperCamelCase__   ,    UpperCamelCase__			)
          return config, inputs_dict
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Union[str, Any]     							=       self.prepare_config_and_inputs()
          return config, inputs_dict
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				int:
          UpperCamelCase	:Tuple     							=       20
          UpperCamelCase	:Optional[int]     							=       model_class_name(UpperCamelCase__			)

          UpperCamelCase	:Union[str, Any]     							=       inputs_dict["input_ids"]
          UpperCamelCase	:Tuple     							=       inputs_dict["attention_mask"]

          UpperCamelCase	:List[Any]     							=       model.init_cache(input_ids.shape[0]   ,    UpperCamelCase__			)
          UpperCamelCase	:str     							=       jnp.ones((input_ids.shape[0], max_length)   ,    dtype='''i4'''			)

          UpperCamelCase	:Optional[Any]     							=       jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,    (input_ids.shape[0], input_ids.shape[-1] - 1)   ,    )
          UpperCamelCase	:int     							=       model(
              input_ids[:, :-1]   ,    attention_mask=UpperCamelCase__   ,    past_key_values=UpperCamelCase__   ,    position_ids=UpperCamelCase__   ,    )

          UpperCamelCase	:Optional[int]     							=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,    dtype='''i4'''			)
          UpperCamelCase	:List[str]     							=       model(
              input_ids[:, -1:]   ,    attention_mask=UpperCamelCase__   ,    past_key_values=outputs_cache.past_key_values   ,    position_ids=UpperCamelCase__   ,    )

          UpperCamelCase	:Tuple     							=       model(UpperCamelCase__			)

          UpperCamelCase	: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}'''			)




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Tuple:
          UpperCamelCase	:Union[str, Any]     							=       20
          UpperCamelCase	:Optional[Any]     							=       model_class_name(UpperCamelCase__			)

          UpperCamelCase	:List[str]     							=       (
              inputs_dict["input_ids"],
              inputs_dict["attention_mask"],
          )

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

          UpperCamelCase	:Dict     							=       model.init_cache(input_ids.shape[0]   ,    UpperCamelCase__			)
          UpperCamelCase	:Optional[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=UpperCamelCase__   ,    past_key_values=UpperCamelCase__   ,    position_ids=UpperCamelCase__   ,    )
          UpperCamelCase	:Optional[int]     							=       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=UpperCamelCase__   ,    position_ids=UpperCamelCase__   ,    )

          UpperCamelCase	:Dict     							=       model(UpperCamelCase__   ,    attention_mask=UpperCamelCase__			)

          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       UpperCAmelCase_						( lowerCAmelCase__,     unittest.TestCase,     lowerCAmelCase__			):



      """simple docstring"""
      UpperCamelCase_    : Any									=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      UpperCamelCase_    : str									=() if is_flax_available() else ()
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Optional[Any]     							=       FlaxOPTModelTester(self			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:str     							=       self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	:Any     							=       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(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          for model_class_name in self.all_model_classes:
              UpperCamelCase	:str     							=       model_class_name.from_pretrained('''facebook/opt-125m'''			)
              UpperCamelCase	:Dict     							=       np.ones((1, 1)			) * model.config.eos_token_id
              UpperCamelCase	:Union[str, Any]     							=       model(UpperCamelCase__			)
              self.assertIsNotNone(UpperCamelCase__			)







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



      """simple docstring"""
      @slow
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:List[Any]     							=       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''			)
          UpperCamelCase	:Dict     							=       jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]			)
          UpperCamelCase	:Tuple     							=       model(input_ids=UpperCamelCase__			).last_hidden_state
          UpperCamelCase	:List[str]     							=       (1, 11, 512)
          self.assertEqual(output.shape   ,    UpperCamelCase__			)
          UpperCamelCase	:Optional[Any]     							=       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]   ,    UpperCamelCase__   ,    atol=4e-2			)			)







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



      """simple docstring"""
      def    UpperCAmelCase						(    self			)    ->				Any:
          super().setUp()
          UpperCamelCase	:Dict     							=       "facebook/opt-350m"




      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:int     							=       FlaxOPTForCausalLM.from_pretrained(self.path_model			)
          UpperCamelCase	:Optional[Any]     							=       GPTaTokenizer.from_pretrained(self.path_model			)

          UpperCamelCase	:Optional[Any]     							=       [
              "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	:Dict     							=       tokenizer(UpperCamelCase__   ,    return_tensors='''jax'''   ,    padding=UpperCamelCase__   ,    add_special_tokens=UpperCamelCase__			)
          UpperCamelCase	:int     							=       model(inputs.input_ids   ,    attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
          UpperCamelCase	: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(UpperCamelCase__   ,    UpperCamelCase__   ,    atol=4e-2			)			)

          UpperCamelCase	:Dict     							=       jax.jit(UpperCamelCase__			)
          UpperCamelCase	:Tuple     							=       model(inputs.input_ids   ,    attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
          self.assertTrue(jnp.allclose(UpperCamelCase__   ,    UpperCamelCase__   ,    atol=4e-2			)			)







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



      """simple docstring"""
      @property
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          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			)    ->				List[Any]:
          UpperCamelCase	:Any     							=       "facebook/opt-125m"

          UpperCamelCase	:List[Any]     							=       [
              "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	:Optional[int]     							=       []

          UpperCamelCase	:List[Any]     							=       FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__			)
          UpperCamelCase	:Dict     							=       GPTaTokenizer.from_pretrained(UpperCamelCase__			)

          for prompt in self.prompts:
              UpperCamelCase	:Optional[Any]     							=       tokenizer(UpperCamelCase__   ,    return_tensors='''jax'''			).input_ids

              UpperCamelCase	:Union[str, Any]     							=       model.generate(UpperCamelCase__   ,    max_length=10			)
              UpperCamelCase	:Optional[int]     							=       generated_ids[0]

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

          self.assertListEqual(UpperCamelCase__   ,    UpperCamelCase__			)
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          UpperCamelCase	:Dict     							=       "facebook/opt-350m"

          UpperCamelCase	:List[Any]     							=       [
              "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	:Optional[int]     							=       []
          UpperCamelCase	:Any     							=       FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__			)
          UpperCamelCase	:List[Any]     							=       GPTaTokenizer.from_pretrained(UpperCamelCase__			)

          for prompt in self.prompts:
              UpperCamelCase	:Any     							=       tokenizer(UpperCamelCase__   ,    return_tensors='''jax'''			).input_ids

              UpperCamelCase	:Optional[int]     							=       model.generate(UpperCamelCase__   ,    max_length=10			)
              UpperCamelCase	:List[str]     							=       generated_ids[0]

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

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

          UpperCamelCase	:str     							=       jax.jit(model.generate			)

          UpperCamelCase	:List[Any]     							=       jit_generate(inputs['''input_ids''']   ,    attention_mask=inputs['''attention_mask''']			).sequences

          UpperCamelCase	:Dict     							=       tokenizer.batch_decode(UpperCamelCase__   ,    skip_special_tokens=UpperCamelCase__			)

          self.assertIsNotNone(UpperCamelCase__   ,    UpperCamelCase__			)




      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          UpperCamelCase	:int     							=       "facebook/opt-350m"

          UpperCamelCase	:Dict     							=       GPTaTokenizer.from_pretrained(UpperCamelCase__			)
          UpperCamelCase	:Optional[int]     							=       FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__			)

          UpperCamelCase	:Any     							=       "left"

          # use different length sentences to test batching
          UpperCamelCase	:Union[str, Any]     							=       [
              "Hello, my dog is a little",
              "Today, I",
          ]

          UpperCamelCase	:str     							=       tokenizer(UpperCamelCase__   ,    return_tensors='''jax'''   ,    padding=UpperCamelCase__			)
          UpperCamelCase	:Dict     							=       inputs["input_ids"]

          UpperCamelCase	:Union[str, Any]     							=       model.generate(input_ids=UpperCamelCase__   ,    attention_mask=inputs['''attention_mask''']   ,    trace=UpperCamelCase__			)

          UpperCamelCase	:Union[str, Any]     							=       tokenizer(sentences[0]   ,    return_tensors='''jax'''			).input_ids
          UpperCamelCase	:Optional[int]     							=       model.generate(input_ids=UpperCamelCase__			)

          UpperCamelCase	:Union[str, Any]     							=       inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
          UpperCamelCase	:int     							=       tokenizer(sentences[1]   ,    return_tensors='''jax'''			).input_ids
          UpperCamelCase	:Optional[int]     							=       model.generate(input_ids=UpperCamelCase__   ,    max_length=model.config.max_length - num_paddings			)

          UpperCamelCase	:str     							=       tokenizer.batch_decode(outputs[0]   ,    skip_special_tokens=UpperCamelCase__			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.decode(output_non_padded[0][0]   ,    skip_special_tokens=UpperCamelCase__			)
          UpperCamelCase	:List[Any]     							=       tokenizer.decode(output_padded[0][0]   ,    skip_special_tokens=UpperCamelCase__			)

          UpperCamelCase	:Tuple     							=       [
              "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(UpperCamelCase__   ,    UpperCamelCase__			)
          self.assertListEqual(UpperCamelCase__   ,    [non_padded_sentence, padded_sentence]			)













def   _A       (    SCREAMING_SNAKE_CASE__  :     int = 998001	):

    # fetches the next number
    for number in range(n - 1					,  9999					,  -1	):
        UpperCamelCase	:Union[str, Any]     							=       str(SCREAMING_SNAKE_CASE__	)

        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
            UpperCamelCase	:Dict     							=       999

            # if 'number' is a product of two 3-digit numbers
            # then number is the answer otherwise fetch next number.
            while divisor != 99:
                if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
                    return number
                divisor -= 1
    raise ValueError('''That number is larger than our acceptable range.'''	)


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





