





"""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
   lowerCamelCase_	:		Optional[int]         =   'platform'

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None   ):






  """simple docstring"""





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

@require_flax
class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=1_3   , snake_case_=7   , snake_case_=True   , snake_case_=False   , snake_case_=9_9   , snake_case_=1_6   , snake_case_=2   , snake_case_=4   , snake_case_=4   , snake_case_="gelu"   , snake_case_=0.1   , snake_case_=0.1   , snake_case_=2_0   , snake_case_=2   , snake_case_=1   , snake_case_=0   , snake_case_=1_6   , snake_case_=1_6   , snake_case_=0.02   , ):

        """simple docstring"""

        A_    :			Dict									=    parent
        A_    :			Dict									=    batch_size
        A_    :			Dict									=    seq_length
        A_    :			Optional[int]									=    is_training
        A_    :			Union[str, Any]									=    use_labels
        A_    :			Tuple									=    vocab_size
        A_    :			Optional[int]									=    hidden_size
        A_    :			Any									=    num_hidden_layers
        A_    :			Any									=    num_attention_heads
        A_    :			List[Any]									=    intermediate_size
        A_    :			Dict									=    hidden_act
        A_    :			Tuple									=    hidden_dropout_prob
        A_    :			Tuple									=    attention_probs_dropout_prob
        A_    :			Any									=    max_position_embeddings
        A_    :			List[str]									=    eos_token_id
        A_    :			str									=    pad_token_id
        A_    :			Union[str, Any]									=    bos_token_id
        A_    :			str									=    embed_dim
        A_    :			List[Any]									=    word_embed_proj_dim
        A_    :			Optional[int]									=    initializer_range
        A_    :			List[Any]									=    False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

        A_    :			List[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=snake_case_   , word_embed_proj_dim=self.word_embed_proj_dim   , initializer_range=self.initializer_range   , use_cache=snake_case_   , )
        A_    :			Optional[int]									=    prepare_opt_inputs_dict(snake_case_   , snake_case_    )
        return config, inputs_dict

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Union[str, Any]									=    self.prepare_config_and_inputs()
        return config, inputs_dict

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    2_0
        A_    :			Union[str, Any]									=    model_class_name(snake_case_    )

        A_    :			Any									=    inputs_dict['input_ids']
        A_    :			int									=    inputs_dict['attention_mask']

        A_    :			List[str]									=    model.init_cache(input_ids.shape[0]   , snake_case_    )
        A_    :			Optional[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_    :			str									=    model(
            input_ids[:, :-1]   , attention_mask=snake_case_   , past_key_values=snake_case_   , position_ids=snake_case_   , )

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

        A_    :			str									=    model(snake_case_    )

        A_    :			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    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    2_0
        A_    :			Tuple									=    model_class_name(snake_case_    )

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

        A_    :			List[Any]									=    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]   , snake_case_    )
        A_    :			Optional[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=snake_case_   , past_key_values=snake_case_   , position_ids=snake_case_   , )
        A_    :			Dict									=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   , dtype='i4'    )
        A_    :			Optional[int]									=    model(
            input_ids[:, -1:]   , past_key_values=outputs_cache.past_key_values   , attention_mask=snake_case_   , position_ids=snake_case_   , )

        A_    :			str									=    model(snake_case_   , attention_mask=snake_case_    )

        A_    :			List[Any]									=    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    (  UpperCAmelCase__     ,  unittest.TestCase     ,  UpperCAmelCase__						):



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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    FlaxOPTModelTester(self    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[int]									=    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(snake_case_   , snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			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(snake_case_   , snake_case_   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for model_class_name in self.all_model_classes:
          A_    :			int									=    model_class_name.from_pretrained('facebook/opt-125m'    )
          A_    :			Dict									=    np.ones((1, 1)    ) * model.config.eos_token_id
          A_    :			Any									=    model(snake_case_    )
          self.assertIsNotNone(snake_case_    )


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



      '''simple docstring'''

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
        A_    :			Any									=    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]]    )
        A_    :			Optional[Any]									=    model(input_ids=snake_case_    ).last_hidden_state
        A_    :			Optional[int]									=    (1, 1_1, 5_1_2)
        self.assertEqual(output.shape   , snake_case_    )
        A_    :			List[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]   , snake_case_   , atol=4E-2    )    )


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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().setUp()
        A_    :			List[Any]									=    'facebook/opt-350m'

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    FlaxOPTForCausalLM.from_pretrained(self.path_model    )
        A_    :			List[Any]									=    GPTaTokenizer.from_pretrained(self.path_model    )

        A_    :			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
        A_    :			List[str]									=    tokenizer(snake_case_   , return_tensors='jax'   , padding=snake_case_   , add_special_tokens=snake_case_    )
        A_    :			List[Any]									=    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(snake_case_   , snake_case_   , atol=4E-2    )    )

        A_    :			str									=    jax.jit(snake_case_    )
        A_    :			Optional[Any]									=    model(inputs.input_ids   , attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
        self.assertTrue(jnp.allclose(snake_case_   , snake_case_   , atol=4E-2    )    )


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



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """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    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    'facebook/opt-125m'

        A_    :			Tuple									=    [
            '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_    :			Optional[Any]									=    []

        A_    :			Optional[Any]									=    FlaxOPTForCausalLM.from_pretrained(snake_case_    )
        A_    :			List[Any]									=    GPTaTokenizer.from_pretrained(snake_case_    )

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

          A_    :			Tuple									=    model.generate(snake_case_   , max_length=1_0    )
          A_    :			Any									=    generated_ids[0]

          A_    :			int									=    tokenizer.batch_decode(snake_case_   , skip_special_tokens=snake_case_    )
          predicted_outputs += generated_string

        self.assertListEqual(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    'facebook/opt-350m'

        A_    :			int									=    [
            '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_    :			Tuple									=    []
        A_    :			Optional[Any]									=    FlaxOPTForCausalLM.from_pretrained(snake_case_    )
        A_    :			str									=    GPTaTokenizer.from_pretrained(snake_case_    )

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

          A_    :			Tuple									=    model.generate(snake_case_   , max_length=1_0    )
          A_    :			str									=    generated_ids[0]

          A_    :			Optional[Any]									=    tokenizer.batch_decode(snake_case_   , skip_special_tokens=snake_case_    )
          predicted_outputs += generated_string

        self.assertListEqual(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    'facebook/opt-125m'
        A_    :			Tuple									=    [
            '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(snake_case_    )
        A_    :			Optional[int]									=    GPTaTokenizer.from_pretrained(snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ]   , return_tensors='jax'   , padding=snake_case_   , )

        A_    :			str									=    jax.jit(model.generate    )

        A_    :			Optional[int]									=    jit_generate(inputs['input_ids']   , attention_mask=inputs['attention_mask']    ).sequences

        A_    :			int									=    tokenizer.batch_decode(snake_case_   , skip_special_tokens=snake_case_    )

        self.assertIsNotNone(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    'facebook/opt-350m'

        A_    :			str									=    GPTaTokenizer.from_pretrained(snake_case_    )
        A_    :			Dict									=    FlaxOPTForCausalLM.from_pretrained(snake_case_    )

        A_    :			Optional[Any]									=    'left'

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

        A_    :			Union[str, Any]									=    tokenizer(snake_case_   , return_tensors='jax'   , padding=snake_case_    )
        A_    :			Any									=    inputs['input_ids']

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

        A_    :			List[str]									=    tokenizer(sentences[0]   , return_tensors='jax'    ).input_ids
        A_    :			Optional[int]									=    model.generate(input_ids=snake_case_    )

        A_    :			List[Any]									=    inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        A_    :			Optional[Any]									=    tokenizer(sentences[1]   , return_tensors='jax'    ).input_ids
        A_    :			Optional[int]									=    model.generate(input_ids=snake_case_   , max_length=model.config.max_length - num_paddings    )

        A_    :			int									=    tokenizer.batch_decode(outputs[0]   , skip_special_tokens=snake_case_    )
        A_    :			Tuple									=    tokenizer.decode(output_non_padded[0][0]   , skip_special_tokens=snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.decode(output_padded[0][0]   , skip_special_tokens=snake_case_    )

        A_    :			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(snake_case_   , snake_case_    )
        self.assertListEqual(snake_case_   , [non_padded_sentence, padded_sentence]    )



"""simple docstring"""







from __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()