






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
       a		:	Optional[int]      					=			"platform"

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def     lowerCamelCase__				(   __lowerCamelCase      :					Tuple  ,		__lowerCamelCase      :					Optional[int]  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[Any]=None		):
  if attention_mask is None:
    __UpperCAmelCase							:     List[Any]									=  np.where(input_ids != config.pad_token_id  ,		1  ,		0		)
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }
@require_flax
class    a   :



      """simple docstring"""


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

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

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

        __UpperCAmelCase							:     Optional[Any]									=  model.init_cache(input_ids.shape[0]		,				__lowercase )
        __UpperCAmelCase							:     Tuple									=  jnp.ones((input_ids.shape[0], max_length)		,				dtype="""i4""" )

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

        __UpperCAmelCase							:     Optional[int]									=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     List[Any]									=  model(
            input_ids[:, -1:]		,				attention_mask=__lowercase		,				past_key_values=outputs_cache.past_key_values		,				position_ids=__lowercase		,				)

        __UpperCAmelCase							:     Optional[Any]									=  model(__lowercase )

        __UpperCAmelCase							:     Dict									=  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 :      Dict		,				__lowercase :      str		,				__lowercase :      int		,				__lowercase :      int )							->				str:
        __UpperCAmelCase							:     List[str]									=  20
        __UpperCAmelCase							:     List[Any]									=  model_class_name(__lowercase )

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

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

        __UpperCAmelCase							:     Optional[Any]									=  model.init_cache(input_ids.shape[0]		,				__lowercase )
        __UpperCAmelCase							:     str									=  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1)		,				)

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

        __UpperCAmelCase							:     Union[str, Any]									=  model(__lowercase		,				attention_mask=__lowercase )

        __UpperCAmelCase							:     Tuple									=  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    a   (			lowercase__							,	unittest.TestCase							,	lowercase__       ):



      """simple docstring"""


      a  :			Optional[Any]								=     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      a  :			str								=     () if is_flax_available() else ()
      def      UpperCAmelCase				(				self :      List[str] )							->				Union[str, Any]:
        __UpperCAmelCase							:     List[Any]									=  FlaxOPTModelTester(self )
      def      UpperCAmelCase				(				self :      List[Any] )							->				str:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__lowercase		,				__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Dict:
        __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_with_attn_mask(__lowercase		,				__lowercase		,				__lowercase )




      @slow
      def      UpperCAmelCase				(				self :      int )							->				int:
        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Dict									=  model_class_name.from_pretrained("""facebook/opt-125m""" )
          __UpperCAmelCase							:     List[Any]									=  np.ones((1, 1) ) * model.config.eos_token_id
          __UpperCAmelCase							:     Tuple									=  model(__lowercase )
          self.assertIsNotNone(__lowercase )


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



      """simple docstring"""


      @slow
      def      UpperCAmelCase				(				self :      Dict )							->				Optional[int]:
        __UpperCAmelCase							:     Optional[int]									=  FlaxOPTModel.from_pretrained("""facebook/opt-350m""" )
        __UpperCAmelCase							:     Union[str, Any]									=  jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
        __UpperCAmelCase							:     Dict									=  model(input_ids=__lowercase ).last_hidden_state
        __UpperCAmelCase							:     Any									=  (1, 11, 512)
        self.assertEqual(output.shape		,				__lowercase )
        __UpperCAmelCase							:     int									=  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]		,				__lowercase		,				atol=4e-2 ) )


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



      """simple docstring"""


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




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

        __UpperCAmelCase							:     Union[str, 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							:     Tuple									=  tokenizer(__lowercase		,				return_tensors="""jax"""		,				padding=__lowercase		,				add_special_tokens=__lowercase )
        __UpperCAmelCase							:     List[Any]									=  model(inputs.input_ids		,				attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
        __UpperCAmelCase							:     List[Any]									=  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(__lowercase		,				__lowercase		,				atol=4e-2 ) )

        __UpperCAmelCase							:     Union[str, Any]									=  jax.jit(__lowercase )
        __UpperCAmelCase							:     int									=  model(inputs.input_ids		,				attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
        self.assertTrue(jnp.allclose(__lowercase		,				__lowercase		,				atol=4e-2 ) )


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



      """simple docstring"""


      @property
      def      UpperCAmelCase				(				self :      str )							->				List[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 :      Any )							->				str:
        __UpperCAmelCase							:     Dict									=  """facebook/opt-125m"""

        __UpperCAmelCase							:     Optional[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[Any]									=  []

        __UpperCAmelCase							:     Optional[Any]									=  FlaxOPTForCausalLM.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  GPTaTokenizer.from_pretrained(__lowercase )

        for prompt in self.prompts:
          __UpperCAmelCase							:     Optional[int]									=  tokenizer(__lowercase		,				return_tensors="""jax""" ).input_ids

          __UpperCAmelCase							:     Tuple									=  model.generate(__lowercase		,				max_length=10 )
          __UpperCAmelCase							:     Dict									=  generated_ids[0]

          __UpperCAmelCase							:     str									=  tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase )
          predicted_outputs += generated_string

        self.assertListEqual(__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Tuple )							->				str:
        __UpperCAmelCase							:     Union[str, Any]									=  """facebook/opt-350m"""

        __UpperCAmelCase							:     Union[str, 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							:     Union[str, Any]									=  []
        __UpperCAmelCase							:     Any									=  FlaxOPTForCausalLM.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Dict									=  GPTaTokenizer.from_pretrained(__lowercase )

        for prompt in self.prompts:
          __UpperCAmelCase							:     List[str]									=  tokenizer(__lowercase		,				return_tensors="""jax""" ).input_ids

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

          __UpperCAmelCase							:     Any									=  tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase )
          predicted_outputs += generated_string

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

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

        __UpperCAmelCase							:     Tuple									=  jit_generate(inputs["""input_ids"""]		,				attention_mask=inputs["""attention_mask"""] ).sequences

        __UpperCAmelCase							:     int									=  tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase )

        self.assertIsNotNone(__lowercase		,				__lowercase )




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

        __UpperCAmelCase							:     List[str]									=  GPTaTokenizer.from_pretrained(__lowercase )
        __UpperCAmelCase							:     int									=  FlaxOPTForCausalLM.from_pretrained(__lowercase )

        __UpperCAmelCase							:     int									=  """left"""

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

        __UpperCAmelCase							:     Optional[Any]									=  tokenizer(__lowercase		,				return_tensors="""jax"""		,				padding=__lowercase )
        __UpperCAmelCase							:     Union[str, Any]									=  inputs["""input_ids"""]

        __UpperCAmelCase							:     Optional[Any]									=  model.generate(input_ids=__lowercase		,				attention_mask=inputs["""attention_mask"""]		,				trace=__lowercase )

        __UpperCAmelCase							:     List[Any]									=  tokenizer(sentences[0]		,				return_tensors="""jax""" ).input_ids
        __UpperCAmelCase							:     List[Any]									=  model.generate(input_ids=__lowercase )

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

        __UpperCAmelCase							:     List[str]									=  tokenizer.batch_decode(outputs[0]		,				skip_special_tokens=__lowercase )
        __UpperCAmelCase							:     Optional[int]									=  tokenizer.decode(output_non_padded[0][0]		,				skip_special_tokens=__lowercase )
        __UpperCAmelCase							:     Tuple									=  tokenizer.decode(output_padded[0][0]		,				skip_special_tokens=__lowercase )

        __UpperCAmelCase							:     Union[str, 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(__lowercase		,				__lowercase )
        self.assertListEqual(__lowercase		,				[non_padded_sentence, padded_sentence] )










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


