


'''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
       __lowercase:						Dict  		=   'platform'

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Dict			,						_UpperCamelCase					: Any			,						_UpperCamelCase					: Optional[Any]=None			,						_UpperCamelCase					: Tuple=None      )						->    Optional[int]:





   '''simple docstring'''







   if attention_mask is None:
      UpperCamelCase__         =  np.where(input_ids != config.pad_token_id			,						1			,						0      )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }







@require_flax
class        UpperCAmelCase       :

 def __init__(       self       :		int,    a_       :		Optional[Any],    a_       :		Any=13,    a_       :		Dict=7,    a_       :		Union[str, Any]=True,    a_       :		Union[str, Any]=False,    a_       :		Any=99,    a_       :		Any=16,    a_       :		Dict=2,    a_       :		Union[str, Any]=4,    a_       :		Optional[Any]=4,    a_       :		Optional[Any]="gelu",    a_       :		Dict=0.1,    a_       :		Optional[Any]=0.1,    a_       :		Dict=20,    a_       :		Any=2,    a_       :		Any=1,    a_       :		Union[str, Any]=0,    a_       :		Union[str, Any]=16,    a_       :		int=16,    a_       :		str=0.02,    ):





    """simple docstring"""



    UpperCamelCase__         =  parent
    UpperCamelCase__         =  batch_size
    UpperCamelCase__         =  seq_length
    UpperCamelCase__         =  is_training
    UpperCamelCase__         =  use_labels
    UpperCamelCase__         =  vocab_size
    UpperCamelCase__         =  hidden_size
    UpperCamelCase__         =  num_hidden_layers
    UpperCamelCase__         =  num_attention_heads
    UpperCamelCase__         =  intermediate_size
    UpperCamelCase__         =  hidden_act
    UpperCamelCase__         =  hidden_dropout_prob
    UpperCamelCase__         =  attention_probs_dropout_prob
    UpperCamelCase__         =  max_position_embeddings
    UpperCamelCase__         =  eos_token_id
    UpperCamelCase__         =  pad_token_id
    UpperCamelCase__         =  bos_token_id
    UpperCamelCase__         =  embed_dim
    UpperCamelCase__         =  word_embed_proj_dim
    UpperCamelCase__         =  initializer_range
    UpperCamelCase__         =  False

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    UpperCamelCase__         =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1],    self.vocab_size    ),    3,    self.vocab_size    )
    UpperCamelCase__         =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),    dtype=np.intaa    )),    -1    )

    UpperCamelCase__         =  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__         =  prepare_opt_inputs_dict(lowerCAmelCase__,    lowerCAmelCase__    )
    return config, inputs_dict

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.prepare_config_and_inputs()
    return config, inputs_dict

 def 						lowercase_  (       self       :		List[str],    a_       :		str,    a_       :		Union[str, Any],    a_       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  20
    UpperCamelCase__         =  model_class_name(lowerCAmelCase__    )

    UpperCamelCase__         =  inputs_dict['input_ids']
    UpperCamelCase__         =  inputs_dict['attention_mask']

    UpperCamelCase__         =  model.init_cache(input_ids.shape[0],    lowerCAmelCase__    )
    UpperCamelCase__         =  jnp.ones((input_ids.shape[0], max_length),    dtype="i4"    )

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

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

    UpperCamelCase__         =  model(lowerCAmelCase__    )

    UpperCamelCase__         =  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,    a_       :		Optional[int],    a_       :		Tuple,    a_       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  20
    UpperCamelCase__         =  model_class_name(lowerCAmelCase__    )

    UpperCamelCase__         =  (
        inputs_dict['input_ids'],
        inputs_dict['attention_mask'],
    )

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

    UpperCamelCase__         =  model.init_cache(input_ids.shape[0],    lowerCAmelCase__    )
    UpperCamelCase__         =  jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1    )[None, :],    (input_ids.shape[0], input_ids.shape[-1] - 1),    )

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

    UpperCamelCase__         =  model(lowerCAmelCase__,    attention_mask=lowerCAmelCase__    )

    UpperCamelCase__         =  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       (				__SCREAMING_SNAKE_CASE						,	unittest.TestCase						,	__SCREAMING_SNAKE_CASE):
 _lowerCamelCase       :    List[Any]			    =    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 _lowerCamelCase       :    Any			    =    () if is_flax_available() else ()

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  FlaxOPTModelTester(self    )

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  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       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  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       :		Dict    ):





    """simple docstring"""



    for model_class_name in self.all_model_classes:
       UpperCamelCase__         =  model_class_name.from_pretrained("facebook/opt-125m"    )
       UpperCamelCase__         =  np.ones((1, 1)    ) * model.config.eos_token_id
       UpperCamelCase__         =  model(lowerCAmelCase__    )
       self.assertIsNotNone(lowerCAmelCase__    )







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

 @slow
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  FlaxOPTModel.from_pretrained("facebook/opt-350m"    )
    UpperCamelCase__         =  jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    )
    UpperCamelCase__         =  model(input_ids=lowerCAmelCase__    ).last_hidden_state
    UpperCamelCase__         =  (1, 11, 512)
    self.assertEqual(output.shape,    lowerCAmelCase__    )
    UpperCamelCase__         =  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],    lowerCAmelCase__,    atol=4e-2    )    )







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

 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    super().setUp()
    UpperCamelCase__         =  'facebook/opt-350m'

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  FlaxOPTForCausalLM.from_pretrained(self.path_model    )
    UpperCamelCase__         =  GPTaTokenizer.from_pretrained(self.path_model    )

    UpperCamelCase__         =  [
        '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__         =  tokenizer(lowerCAmelCase__,    return_tensors="jax",    padding=lowerCAmelCase__,    add_special_tokens=lowerCAmelCase__    )
    UpperCamelCase__         =  model(inputs.input_ids,    attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
    UpperCamelCase__         =  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(lowerCAmelCase__,    lowerCAmelCase__,    atol=4e-2    )    )

    UpperCamelCase__         =  jax.jit(lowerCAmelCase__    )
    UpperCamelCase__         =  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        UpperCAmelCase       (				unittest.TestCase):

 @property
 def 						lowercase_  (       self       :		Dict    ):





    """simple docstring"""



    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[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  'facebook/opt-125m'

    UpperCamelCase__         =  [
        '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__         =  []

    UpperCamelCase__         =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
    UpperCamelCase__         =  GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

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

       UpperCamelCase__         =  model.generate(lowerCAmelCase__,    max_length=10    )
       UpperCamelCase__         =  generated_ids[0]

       UpperCamelCase__         =  tokenizer.batch_decode(lowerCAmelCase__,    skip_special_tokens=lowerCAmelCase__    )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCAmelCase__,    lowerCAmelCase__    )

 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    UpperCamelCase__         =  'facebook/opt-350m'

    UpperCamelCase__         =  [
        '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__         =  []
    UpperCamelCase__         =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
    UpperCamelCase__         =  GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

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

       UpperCamelCase__         =  model.generate(lowerCAmelCase__,    max_length=10    )
       UpperCamelCase__         =  generated_ids[0]

       UpperCamelCase__         =  tokenizer.batch_decode(lowerCAmelCase__,    skip_special_tokens=lowerCAmelCase__    )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCAmelCase__,    lowerCAmelCase__    )

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  'facebook/opt-125m'
    UpperCamelCase__         =  [
        'Today is a beautiful day and I want to thank',
        'In the city of Rome Canaver Canaver Canaver Canaver',
    ]
    UpperCamelCase__         =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
    UpperCamelCase__         =  GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
    UpperCamelCase__         =  tokenizer(
        [
            "Today is a beautiful day and I want to",
            "In the city of",
        ],    return_tensors="jax",    padding=lowerCAmelCase__,    )

    UpperCamelCase__         =  jax.jit(model.generate    )

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

    UpperCamelCase__         =  tokenizer.batch_decode(lowerCAmelCase__,    skip_special_tokens=lowerCAmelCase__    )

    self.assertIsNotNone(lowerCAmelCase__,    lowerCAmelCase__    )

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  'facebook/opt-350m'

    UpperCamelCase__         =  GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
    UpperCamelCase__         =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )

    UpperCamelCase__         =  'left'

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

    UpperCamelCase__         =  tokenizer(lowerCAmelCase__,    return_tensors="jax",    padding=lowerCAmelCase__    )
    UpperCamelCase__         =  inputs['input_ids']

    UpperCamelCase__         =  model.generate(input_ids=lowerCAmelCase__,    attention_mask=inputs["attention_mask"],    trace=lowerCAmelCase__    )

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

    UpperCamelCase__         =  inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
    UpperCamelCase__         =  tokenizer(sentences[1],    return_tensors="jax"    ).input_ids
    UpperCamelCase__         =  model.generate(input_ids=lowerCAmelCase__,    max_length=model.config.max_length - num_paddings    )

    UpperCamelCase__         =  tokenizer.batch_decode(outputs[0],    skip_special_tokens=lowerCAmelCase__    )
    UpperCamelCase__         =  tokenizer.decode(output_non_padded[0][0],    skip_special_tokens=lowerCAmelCase__    )
    UpperCamelCase__         =  tokenizer.decode(output_padded[0][0],    skip_special_tokens=lowerCAmelCase__    )

    UpperCamelCase__         =  [
        '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]    )



from __future__ import annotations






def 		a__       (							A__							):
			if len(A__							) == 0:
						raise ValueError('find_max() arg is an empty sequence'							)
			SCREAMING_SNAKE_CASE_						:			str       						= nums[0]
			for x in nums:
						if x > max_num:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= x
			return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)





