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 SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]=None		,    lowercase_:						List[str]=None					):
       if attention_mask is None:
              _UpperCamelCase  :							Optional[Any]	    =	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							:		List[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[Any]=1_3							,      lowerCamelCase							:		List[Any]=7							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		List[Any]=False							,      lowerCamelCase							:		Any=9_9							,      lowerCamelCase							:		Any=1_6							,      lowerCamelCase							:		Dict=2							,      lowerCamelCase							:		List[Any]=4							,      lowerCamelCase							:		Any=4							,      lowerCamelCase							:		List[str]="gelu"							,      lowerCamelCase							:		str=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		Optional[Any]=2_0							,      lowerCamelCase							:		Tuple=2							,      lowerCamelCase							:		int=1							,      lowerCamelCase							:		List[str]=0							,      lowerCamelCase							:		Tuple=1_6							,      lowerCamelCase							:		str=1_6							,      lowerCamelCase							:		Optional[Any]=0.0_2							,      )			->				List[str]:
        _UpperCamelCase  :							Optional[int]	    =	parent
        _UpperCamelCase  :							Optional[Any]	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	seq_length
        _UpperCamelCase  :							int	    =	is_training
        _UpperCamelCase  :							Optional[int]	    =	use_labels
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size
        _UpperCamelCase  :							Optional[Any]	    =	hidden_size
        _UpperCamelCase  :							Optional[Any]	    =	num_hidden_layers
        _UpperCamelCase  :							Tuple	    =	num_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	intermediate_size
        _UpperCamelCase  :							Any	    =	hidden_act
        _UpperCamelCase  :							Dict	    =	hidden_dropout_prob
        _UpperCamelCase  :							Optional[int]	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							Optional[Any]	    =	max_position_embeddings
        _UpperCamelCase  :							Tuple	    =	eos_token_id
        _UpperCamelCase  :							Dict	    =	pad_token_id
        _UpperCamelCase  :							str	    =	bos_token_id
        _UpperCamelCase  :							Optional[int]	    =	embed_dim
        _UpperCamelCase  :							Tuple	    =	word_embed_proj_dim
        _UpperCamelCase  :							Optional[Any]	    =	initializer_range
        _UpperCamelCase  :							str	    =	False

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

        _UpperCamelCase  :							Optional[int]	    =	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  :							Optional[Any]	    =	prepare_opt_inputs_dict(lowerCamelCase							,      lowerCamelCase	)
        return config, inputs_dict

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

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		List[str]	)			->				Optional[int]:
        _UpperCamelCase  :							Optional[int]	    =	2_0
        _UpperCamelCase  :							str	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							str	    =	inputs_dict['input_ids']
        _UpperCamelCase  :							str	    =	inputs_dict['attention_mask']

        _UpperCamelCase  :							Dict	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	jnp.ones((input_ids.shape[0], max_length)							,      dtype='i4'	)

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)							,      )
        _UpperCamelCase  :							Optional[Any]	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Optional[int]	    =	model(
            input_ids[:, -1:]							,      attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							int	    =	model(lowerCamelCase	)

        _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        __snake_case       (      self							:		Any							,      lowerCamelCase							:		int							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Union[str, Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	2_0
        _UpperCamelCase  :							Optional[int]	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	(
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

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

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

        _UpperCamelCase  :							Any	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)

        _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  __lowerCAmelCase	(							a_,						unittest.TestCase,						a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[str, Any]					     =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 _lowerCamelCase    :						List[Any]					     =			() if is_flax_available() else ()

 def        __snake_case       (      self							:		List[Any]	)			->				int:
        _UpperCamelCase  :							Optional[Any]	    =	FlaxOPTModelTester(self	)

 def        __snake_case       (      self							:		List[str]	)			->				Any:
        _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(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

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







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



 '''simple docstring'''

 @slow
 def        __snake_case       (      self							:		Dict	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[str]	    =	FlaxOPTModel.from_pretrained('facebook/opt-350m'	)
        _UpperCamelCase  :							str	    =	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  :							Tuple	    =	model(input_ids=lowerCamelCase	).last_hidden_state
        _UpperCamelCase  :							Any	    =	(1, 1_1, 5_1_2)
        self.assertEqual(output.shape							,      lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	jnp.array(
            [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]	)
        self.assertTrue(jnp.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=4e-2	)	)







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



 '''simple docstring'''

 def        __snake_case       (      self							:		str	)			->				str:
        super().setUp()
        _UpperCamelCase  :							int	    =	'facebook/opt-350m'

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

        _UpperCamelCase  :							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  :							Optional[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase							,      add_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	model(inputs.input_ids							,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        _UpperCamelCase  :							Any	    =	jnp.array(
            [
                [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
            ]	)
        self.assertTrue(jnp.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=4e-2	)	)

        _UpperCamelCase  :							Dict	    =	jax.jit(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	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  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		List[str]	)			->				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        __snake_case       (      self							:		List[Any]	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'facebook/opt-125m'

        _UpperCamelCase  :							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  :							Tuple	    =	[]

        _UpperCamelCase  :							List[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

        for prompt in self.prompts:
               _UpperCamelCase  :							List[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'	).input_ids

               _UpperCamelCase  :							Optional[Any]	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							int	    =	generated_ids[0]

               _UpperCamelCase  :							Dict	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[int]:
        _UpperCamelCase  :							Tuple	    =	'facebook/opt-350m'

        _UpperCamelCase  :							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',
        ]

        _UpperCamelCase  :							str	    =	[]
        _UpperCamelCase  :							Tuple	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

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

               _UpperCamelCase  :							Tuple	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							List[Any]	    =	generated_ids[0]

               _UpperCamelCase  :							Any	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	'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[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ]							,      return_tensors='jax'							,      padding=lowerCamelCase							,      )

        _UpperCamelCase  :							Optional[Any]	    =	jax.jit(model.generate	)

        _UpperCamelCase  :							str	    =	jit_generate(inputs['input_ids']							,      attention_mask=inputs['attention_mask']	).sequences

        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)

        self.assertIsNotNone(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Dict:
        _UpperCamelCase  :							Optional[Any]	    =	'facebook/opt-350m'

        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	'left'

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

        _UpperCamelCase  :							Union[str, Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	inputs['input_ids']

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

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

        _UpperCamelCase  :							Tuple	    =	inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        _UpperCamelCase  :							List[str]	    =	tokenizer(sentences[1]							,      return_tensors='jax'	).input_ids
        _UpperCamelCase  :							List[Any]	    =	model.generate(input_ids=lowerCamelCase							,      max_length=model.config.max_length - num_paddings	)

        _UpperCamelCase  :							int	    =	tokenizer.batch_decode(outputs[0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_non_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	[
            '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(   lowercase_ = 100     ,   lowercase_ = 100				)      ->							int:





  '''simple docstring'''





  return max(
      sum(int(lowercase_				) for x in str(base**power				)				)
      for base in range(lowercase_				)
      for power in range(lowercase_				)				)


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()