

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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=None ) ->       str:






    '''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    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=13       , lowercase__=7       , lowercase__=True       , lowercase__=False       , lowercase__=99       , lowercase__=16       , lowercase__=2       , lowercase__=4       , lowercase__=4       , lowercase__="gelu"       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=20       , lowercase__=2       , lowercase__=1       , lowercase__=0       , lowercase__=16       , lowercase__=16       , lowercase__=0.02       , )       -> str:
        __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        lowerCAmelCase_  (self					)       -> List[Any]:
        __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=lowercase__       , word_embed_proj_dim=self.word_embed_proj_dim       , initializer_range=self.initializer_range       , use_cache=lowercase__       , )
        __UpperCAmelCase			   =     prepare_opt_inputs_dict(lowercase__       , lowercase__					)
        return config, inputs_dict


    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.prepare_config_and_inputs()
        return config, inputs_dict


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> List[str]:
        __UpperCAmelCase			   =     20
        __UpperCAmelCase			   =     model_class_name(lowercase__					)

        __UpperCAmelCase			   =     inputs_dict['''input_ids''']
        __UpperCAmelCase			   =     inputs_dict['''attention_mask''']

        __UpperCAmelCase			   =     model.init_cache(input_ids.shape[0]       , lowercase__					)
        __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=lowercase__       , past_key_values=lowercase__       , position_ids=lowercase__       , )

        __UpperCAmelCase			   =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       , dtype='''i4'''					)
        __UpperCAmelCase			   =     model(
            input_ids[:, -1:]       , attention_mask=lowercase__       , past_key_values=outputs_cache.past_key_values       , position_ids=lowercase__       , )

        __UpperCAmelCase			   =     model(lowercase__					)

        __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        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> Optional[int]:
        __UpperCAmelCase			   =     20
        __UpperCAmelCase			   =     model_class_name(lowercase__					)

        __UpperCAmelCase   ,       __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]       , lowercase__					)
        __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=lowercase__       , past_key_values=lowercase__       , position_ids=lowercase__       , )
        __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=lowercase__       , position_ids=lowercase__       , )

        __UpperCAmelCase			   =     model(lowercase__       , attention_mask=lowercase__					)

        __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    A_				( _a	,       unittest.TestCase	,       _a  ):


    '''simple docstring'''






    a__       							=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    a__       							=    () if is_flax_available() else ()


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     FlaxOPTModelTester(self					)


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     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        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase   ,       __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(lowercase__       , lowercase__       , lowercase__					)






    @slow
    def        lowerCAmelCase_  (self					)       -> Any:
        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(lowercase__					)
            self.assertIsNotNone(lowercase__					)






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


    '''simple docstring'''






    @slow
    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        __UpperCAmelCase			   =     FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
        __UpperCAmelCase			   =     jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					)
        __UpperCAmelCase			   =     model(input_ids=lowercase__					).last_hidden_state
        __UpperCAmelCase			   =     (1, 11, 512)
        self.assertEqual(output.shape       , lowercase__					)
        __UpperCAmelCase			   =     jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]					)
        self.assertTrue(jnp.allclose(output[:, :3, :3]       , lowercase__       , atol=4E-2					)					)






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


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        super().setUp()
        __UpperCAmelCase			   =     '''facebook/opt-350m'''






    def        lowerCAmelCase_  (self					)       -> Tuple:
        __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(lowercase__       , return_tensors='''jax'''       , padding=lowercase__       , add_special_tokens=lowercase__					)
        __UpperCAmelCase			   =     model(inputs.input_ids       , attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
        __UpperCAmelCase			   =     jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]					)
        self.assertTrue(jnp.allclose(lowercase__       , lowercase__       , atol=4E-2					)					)

        __UpperCAmelCase			   =     jax.jit(lowercase__					)
        __UpperCAmelCase			   =     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        lowerCAmelCase_  (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        lowerCAmelCase_  (self					)       -> Optional[int]:
        __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(lowercase__					)
        __UpperCAmelCase			   =     GPTaTokenizer.from_pretrained(lowercase__					)

        for prompt in self.prompts:
            __UpperCAmelCase			   =     tokenizer(lowercase__       , return_tensors='''jax'''					).input_ids

            __UpperCAmelCase			   =     model.generate(lowercase__       , max_length=10					)
            __UpperCAmelCase			   =     generated_ids[0]

            __UpperCAmelCase			   =     tokenizer.batch_decode(lowercase__       , skip_special_tokens=lowercase__					)
            predicted_outputs += generated_string

        self.assertListEqual(lowercase__       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Dict:
        __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(lowercase__					)
        __UpperCAmelCase			   =     GPTaTokenizer.from_pretrained(lowercase__					)

        for prompt in self.prompts:
            __UpperCAmelCase			   =     tokenizer(lowercase__       , return_tensors='''jax'''					).input_ids

            __UpperCAmelCase			   =     model.generate(lowercase__       , max_length=10					)
            __UpperCAmelCase			   =     generated_ids[0]

            __UpperCAmelCase			   =     tokenizer.batch_decode(lowercase__       , skip_special_tokens=lowercase__					)
            predicted_outputs += generated_string

        self.assertListEqual(lowercase__       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __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(lowercase__					)
        __UpperCAmelCase			   =     GPTaTokenizer.from_pretrained(lowercase__					)
        __UpperCAmelCase			   =     tokenizer(
            [
                '''Today is a beautiful day and I want to''',
                '''In the city of''',
            ]       , return_tensors='''jax'''       , padding=lowercase__       , )

        __UpperCAmelCase			   =     jax.jit(model.generate					)

        __UpperCAmelCase			   =     jit_generate(inputs['''input_ids''']       , attention_mask=inputs['''attention_mask''']					).sequences

        __UpperCAmelCase			   =     tokenizer.batch_decode(lowercase__       , skip_special_tokens=lowercase__					)

        self.assertIsNotNone(lowercase__       , lowercase__					)






    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        __UpperCAmelCase			   =     '''facebook/opt-350m'''

        __UpperCAmelCase			   =     GPTaTokenizer.from_pretrained(lowercase__					)
        __UpperCAmelCase			   =     FlaxOPTForCausalLM.from_pretrained(lowercase__					)

        __UpperCAmelCase			   =     '''left'''

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

        __UpperCAmelCase			   =     tokenizer(lowercase__       , return_tensors='''jax'''       , padding=lowercase__					)
        __UpperCAmelCase			   =     inputs['''input_ids''']

        __UpperCAmelCase			   =     model.generate(input_ids=lowercase__       , attention_mask=inputs['''attention_mask''']       , trace=lowercase__					)

        __UpperCAmelCase			   =     tokenizer(sentences[0]       , return_tensors='''jax'''					).input_ids
        __UpperCAmelCase			   =     model.generate(input_ids=lowercase__					)

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

        __UpperCAmelCase			   =     tokenizer.batch_decode(outputs[0]       , skip_special_tokens=lowercase__					)
        __UpperCAmelCase			   =     tokenizer.decode(output_non_padded[0][0]       , skip_special_tokens=lowercase__					)
        __UpperCAmelCase			   =     tokenizer.decode(output_padded[0][0]       , skip_special_tokens=lowercase__					)

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







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




