

"""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
   _a				   =							'platform'

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def 						_A						(						UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : List[str]=None,    UpperCamelCase_  : Optional[int]=None)			->		Union[str, 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       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Any,      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    List[str]=1_3,      UpperCAmelCase__							:    Optional[int]=7,      UpperCAmelCase__							:    List[Any]=True,      UpperCAmelCase__							:    Tuple=False,      UpperCAmelCase__							:    List[Any]=9_9,      UpperCAmelCase__							:    Any=1_6,      UpperCAmelCase__							:    int=2,      UpperCAmelCase__							:    Union[str, Any]=4,      UpperCAmelCase__							:    Tuple=4,      UpperCAmelCase__							:    Tuple="gelu",      UpperCAmelCase__							:    List[Any]=0.1,      UpperCAmelCase__							:    Union[str, Any]=0.1,      UpperCAmelCase__							:    Tuple=2_0,      UpperCAmelCase__							:    List[Any]=2,      UpperCAmelCase__							:    Union[str, Any]=1,      UpperCAmelCase__							:    int=0,      UpperCAmelCase__							:    List[Any]=1_6,      UpperCAmelCase__							:    Any=1_6,      UpperCAmelCase__							:    Dict=0.02,      ):
    __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       _lowercase				(    self							:    Tuple			):
    __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=UpperCAmelCase__,      word_embed_proj_dim=self.word_embed_proj_dim,      initializer_range=self.initializer_range,      use_cache=UpperCAmelCase__,      )
    __lowercase			=		prepare_opt_inputs_dict(UpperCAmelCase__,      UpperCAmelCase__			)
    return config, inputs_dict


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase	,__lowercase			=		self.prepare_config_and_inputs()
    return config, inputs_dict


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    Tuple			):
    __lowercase			=		2_0
    __lowercase			=		model_class_name(UpperCAmelCase__			)

    __lowercase			=		inputs_dict["input_ids"]
    __lowercase			=		inputs_dict["attention_mask"]

    __lowercase			=		model.init_cache(input_ids.shape[0],      UpperCAmelCase__			)
    __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=UpperCAmelCase__,      past_key_values=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )

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

    __lowercase			=		model(UpperCAmelCase__			)

    __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       _lowercase				(    self							:    int,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Dict			):
    __lowercase			=		2_0
    __lowercase			=		model_class_name(UpperCAmelCase__			)

    __lowercase	,__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],      UpperCAmelCase__			)
    __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=UpperCAmelCase__,      past_key_values=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )
    __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=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )

    __lowercase			=		model(UpperCAmelCase__,      attention_mask=UpperCAmelCase__			)

    __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       _lowerCAmelCase						(			lowercase  ,unittest.TestCase  ,lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Union[str, Any]     	=			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 __UpperCAmelCase			:      List[str]     	=			() if is_flax_available() else ()


 def       _lowercase				(    self							:    int			):
    __lowercase			=		FlaxOPTModelTester(self			)


 def       _lowercase				(    self							:    Any			):
    __lowercase	,__lowercase			=		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       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase	,__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(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)



 @slow
 def       _lowercase				(    self							:    List[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(UpperCAmelCase__			)
       self.assertIsNotNone(UpperCAmelCase__			)







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







 """simple docstring"""







 @slow
 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		FlaxOPTModel.from_pretrained("facebook/opt-350m"			)
    __lowercase			=		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]]			)
    __lowercase			=		model(input_ids=UpperCAmelCase__			).last_hidden_state
    __lowercase			=		(1, 1_1, 5_1_2)
    self.assertEqual(output.shape,      UpperCAmelCase__			)
    __lowercase			=		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],      UpperCAmelCase__,      atol=4E-2			)			)







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







 """simple docstring"""







 def       _lowercase				(    self							:    Dict			):
    super().setUp()
    __lowercase			=		"facebook/opt-350m"



 def       _lowercase				(    self							:    Union[str, Any]			):
    __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(UpperCAmelCase__,      return_tensors="jax",      padding=UpperCAmelCase__,      add_special_tokens=UpperCAmelCase__			)
    __lowercase			=		model(inputs.input_ids,      attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
    __lowercase			=		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(UpperCAmelCase__,      UpperCAmelCase__,      atol=4E-2			)			)

    __lowercase			=		jax.jit(UpperCAmelCase__			)
    __lowercase			=		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       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @property
 def       _lowercase				(    self							:    List[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							:    int			):
    __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(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)

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

       __lowercase			=		model.generate(UpperCAmelCase__,      max_length=1_0			)
       __lowercase			=		generated_ids[0]

       __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)
       predicted_outputs += generated_string

    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    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(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)

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

       __lowercase			=		model.generate(UpperCAmelCase__,      max_length=1_0			)
       __lowercase			=		generated_ids[0]

       __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)
       predicted_outputs += generated_string

    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any]			):
    __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(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		tokenizer(
        [
            "Today is a beautiful day and I want to",
            "In the city of",
        ],      return_tensors="jax",      padding=UpperCAmelCase__,      )

    __lowercase			=		jax.jit(model.generate			)

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

    __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)

    self.assertIsNotNone(UpperCAmelCase__,      UpperCAmelCase__			)



 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		"facebook/opt-350m"

    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase__			)

    __lowercase			=		"left"

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

    __lowercase			=		tokenizer(UpperCAmelCase__,      return_tensors="jax",      padding=UpperCAmelCase__			)
    __lowercase			=		inputs["input_ids"]

    __lowercase			=		model.generate(input_ids=UpperCAmelCase__,      attention_mask=inputs["attention_mask"],      trace=UpperCAmelCase__			)

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

    __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=UpperCAmelCase__,      max_length=model.config.max_length - num_paddings			)

    __lowercase			=		tokenizer.batch_decode(outputs[0],      skip_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.decode(output_non_padded[0][0],      skip_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.decode(output_padded[0][0],      skip_special_tokens=UpperCAmelCase__			)

    __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(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertListEqual(UpperCAmelCase__,      [non_padded_sentence, padded_sentence]			)







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : float,    UpperCamelCase_  : float)			->		float:
   '''simple docstring'''


   __lowercase			=		float()
   try:
      __lowercase			=		mass * acceleration
   except Exception:
      return -0.0
   return force


if __name__ == "__main__":
   import doctest

   # run doctest
   doctest.testmod()

   # demo
   _a				   =							12.5
   _a				   =							10
   _a				   =							newtons_second_law_of_motion(mass, acceleration)
   print('The force is ', force, 'N')




