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
  UpperCAmelCase							   =      """platform"""

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None       )	->	List[str]:
     if attention_mask is None:
          a  :    Tuple				=						np.where(input_ids != config.pad_token_id						,      1						,      0       )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }






@require_flax
class 							_SCREAMING_SNAKE_CASE:
   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							,     )	->Dict:
        a  :    Optional[Any]				=						parent
        a  :    Dict				=						batch_size
        a  :    Union[str, Any]				=						seq_length
        a  :    Dict				=						is_training
        a  :    Optional[Any]				=						use_labels
        a  :    Optional[Any]				=						vocab_size
        a  :    Union[str, Any]				=						hidden_size
        a  :    Dict				=						num_hidden_layers
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						intermediate_size
        a  :    Optional[int]				=						hidden_act
        a  :    Any				=						hidden_dropout_prob
        a  :    Tuple				=						attention_probs_dropout_prob
        a  :    Tuple				=						max_position_embeddings
        a  :    Optional[Any]				=						eos_token_id
        a  :    Dict				=						pad_token_id
        a  :    Dict				=						bos_token_id
        a  :    Optional[int]				=						embed_dim
        a  :    List[str]				=						word_embed_proj_dim
        a  :    Union[str, Any]				=						initializer_range
        a  :    Union[str, Any]				=						False
   def _lowercase(    self    )	->Dict:
        a  :    Any				=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size    )							,     3							,     self.vocab_size    )
        a  :    Dict				=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,     dtype=np.intaa    ))							,     -1    )

        a  :    str				=						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							,     )
        a  :    str				=						prepare_opt_inputs_dict(__lowerCamelCase							,     __lowerCamelCase    )
        return config, inputs_dict
   def _lowercase(    self    )	->Dict:
        a  :    Any				=						self.prepare_config_and_inputs()
        return config, inputs_dict
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Dict:
        a  :    int				=						20
        a  :    Optional[int]				=						model_class_name(__lowerCamelCase    )

        a  :    List[Any]				=						inputs_dict["input_ids"]
        a  :    List[str]				=						inputs_dict["attention_mask"]

        a  :    Any				=						model.init_cache(input_ids.shape[0]							,     __lowerCamelCase    )
        a  :    Any				=						jnp.ones((input_ids.shape[0], max_length)							,     dtype='''i4'''    )

        a  :    List[Any]				=						jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1    )[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)							,     )
        a  :    Tuple				=						model(
            input_ids[:, :-1]							,     attention_mask=__lowerCamelCase							,     past_key_values=__lowerCamelCase							,     position_ids=__lowerCamelCase							,     )

        a  :    Union[str, Any]				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype='''i4'''    )
        a  :    Dict				=						model(
            input_ids[:, -1:]							,     attention_mask=__lowerCamelCase							,     past_key_values=outputs_cache.past_key_values							,     position_ids=__lowerCamelCase							,     )

        a  :    Dict				=						model(__lowerCamelCase    )

        a  :    int				=						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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Union[str, Any]:
        a  :    int				=						20
        a  :    Optional[int]				=						model_class_name(__lowerCamelCase    )

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

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

        a  :    List[Any]				=						model.init_cache(input_ids.shape[0]							,     __lowerCamelCase    )
        a  :    Union[str, Any]				=						jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1    )[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)							,     )

        a  :    int				=						model(
            input_ids[:, :-1]							,     attention_mask=__lowerCamelCase							,     past_key_values=__lowerCamelCase							,     position_ids=__lowerCamelCase							,     )
        a  :    Tuple				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype='''i4'''    )
        a  :    Optional[Any]				=						model(
            input_ids[:, -1:]							,     past_key_values=outputs_cache.past_key_values							,     attention_mask=__lowerCamelCase							,     position_ids=__lowerCamelCase							,     )

        a  :    Any				=						model(__lowerCamelCase							,     attention_mask=__lowerCamelCase    )

        a  :    Optional[int]				=						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 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      unittest.TestCase       ,      snake_case__	):
   UpperCamelCase_				:						List[Any] 			=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   UpperCamelCase_				:						str 			=   () if is_flax_available() else ()
   def _lowercase(    self    )	->str:
        a  :    str				=						FlaxOPTModelTester(self    )
   def _lowercase(    self    )	->Optional[Any]:
        a  :    List[Any]				=						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 _lowercase(    self    )	->Union[str, Any]:
        a  :    List[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(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase    )


   @slow
   def _lowercase(    self    )	->Optional[Any]:
        for model_class_name in self.all_model_classes:
             a  :    List[Any]				=						model_class_name.from_pretrained('''facebook/opt-125m'''    )
             a  :    Optional[int]				=						np.ones((1, 1)    ) * model.config.eos_token_id
             a  :    List[str]				=						model(__lowerCamelCase    )
             self.assertIsNotNone(__lowerCamelCase    )






@require_sentencepiece
@require_flax
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @slow
   def _lowercase(    self    )	->List[str]:
        a  :    List[str]				=						FlaxOPTModel.from_pretrained('''facebook/opt-350m'''    )
        a  :    Union[str, Any]				=						jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]    )
        a  :    Any				=						model(input_ids=__lowerCamelCase    ).last_hidden_state
        a  :    str				=						(1, 11, 5_12)
        self.assertEqual(output.shape							,     __lowerCamelCase    )
        a  :    Any				=						jnp.array(
            [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]    )
        self.assertTrue(jnp.allclose(output[:, :3, :3]							,     __lowerCamelCase							,     atol=4e-2    )    )






@require_flax
@slow
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def _lowercase(    self    )	->Any:
        super().setUp()
        a  :    Optional[int]				=						"facebook/opt-350m"


   def _lowercase(    self    )	->Union[str, Any]:
        a  :    Tuple				=						FlaxOPTForCausalLM.from_pretrained(self.path_model    )
        a  :    Optional[int]				=						GPTaTokenizer.from_pretrained(self.path_model    )

        a  :    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
        a  :    Optional[int]				=						tokenizer(__lowerCamelCase							,     return_tensors='''jax'''							,     padding=__lowerCamelCase							,     add_special_tokens=__lowerCamelCase    )
        a  :    Tuple				=						model(inputs.input_ids							,     attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
        a  :    str				=						jnp.array(
            [
                [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
            ]    )
        self.assertTrue(jnp.allclose(__lowerCamelCase							,     __lowerCamelCase							,     atol=4e-2    )    )

        a  :    Any				=						jax.jit(__lowerCamelCase    )
        a  :    Any				=						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 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @property
   def _lowercase(    self    )	->str:
        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 _lowercase(    self    )	->Optional[int]:
        a  :    List[str]				=						"facebook/opt-125m"

        a  :    List[str]				=						[
            "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  :    str				=						[]

        a  :    Optional[int]				=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase    )
        a  :    Tuple				=						GPTaTokenizer.from_pretrained(__lowerCamelCase    )

        for prompt in self.prompts:
             a  :    Union[str, Any]				=						tokenizer(__lowerCamelCase							,     return_tensors='''jax'''    ).input_ids

             a  :    List[Any]				=						model.generate(__lowerCamelCase							,     max_length=10    )
             a  :    List[str]				=						generated_ids[0]

             a  :    List[Any]				=						tokenizer.batch_decode(__lowerCamelCase							,     skip_special_tokens=__lowerCamelCase    )
             predicted_outputs += generated_string

        self.assertListEqual(__lowerCamelCase							,     __lowerCamelCase    )
   def _lowercase(    self    )	->List[str]:
        a  :    List[Any]				=						"facebook/opt-350m"

        a  :    Dict				=						[
            "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  :    str				=						[]
        a  :    Tuple				=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase    )
        a  :    int				=						GPTaTokenizer.from_pretrained(__lowerCamelCase    )

        for prompt in self.prompts:
             a  :    Union[str, Any]				=						tokenizer(__lowerCamelCase							,     return_tensors='''jax'''    ).input_ids

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

             a  :    Union[str, Any]				=						tokenizer.batch_decode(__lowerCamelCase							,     skip_special_tokens=__lowerCamelCase    )
             predicted_outputs += generated_string

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

        a  :    List[str]				=						jax.jit(model.generate    )

        a  :    Tuple				=						jit_generate(inputs['''input_ids''']							,     attention_mask=inputs['''attention_mask''']    ).sequences

        a  :    Any				=						tokenizer.batch_decode(__lowerCamelCase							,     skip_special_tokens=__lowerCamelCase    )

        self.assertIsNotNone(__lowerCamelCase							,     __lowerCamelCase    )


   def _lowercase(    self    )	->Dict:
        a  :    Optional[int]				=						"facebook/opt-350m"

        a  :    Union[str, Any]				=						GPTaTokenizer.from_pretrained(__lowerCamelCase    )
        a  :    Dict				=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase    )

        a  :    List[str]				=						"left"

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

        a  :    int				=						tokenizer(__lowerCamelCase							,     return_tensors='''jax'''							,     padding=__lowerCamelCase    )
        a  :    Optional[Any]				=						inputs["input_ids"]

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

        a  :    List[str]				=						tokenizer(sentences[0]							,     return_tensors='''jax'''    ).input_ids
        a  :    List[Any]				=						model.generate(input_ids=__lowerCamelCase    )

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

        a  :    Optional[Any]				=						tokenizer.batch_decode(outputs[0]							,     skip_special_tokens=__lowerCamelCase    )
        a  :    Tuple				=						tokenizer.decode(output_non_padded[0][0]							,     skip_special_tokens=__lowerCamelCase    )
        a  :    List[Any]				=						tokenizer.decode(output_padded[0][0]							,     skip_special_tokens=__lowerCamelCase    )

        a  :    int				=						[
            "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 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:
     return 1 if input_a == 0 else 0







def 	UpperCAmelCase       (		)	->	None:
     assert not_gate(0       ) == 1
     assert not_gate(1       ) == 0


if __name__ == "__main__":
  print(not_gate(0))
  print(not_gate(1))