





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, 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__  :     List[Any]   			='''platform'''

       import jax
       import jax.numpy as jnp

       from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , )  ->					Dict:





    if attention_mask is None:
        lowerCamelCase			=np.where(input_ids != config.pad_token_id     , 1     , 0			)
    if decoder_attention_mask is None:
        lowerCamelCase			=np.where(decoder_input_ids != config.pad_token_id     , 1     , 0			)
    if head_mask is None:
        lowerCamelCase			=np.ones((config.encoder_layers, config.encoder_attention_heads)			)
    if decoder_head_mask is None:
        lowerCamelCase			=np.ones((config.decoder_layers, config.decoder_attention_heads)			)
    if cross_attn_head_mask is None:
        lowerCamelCase			=np.ones((config.decoder_layers, config.decoder_attention_heads)			)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }

class  __A       :


    def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_=13				,	UpperCAmelCase_=7				,	UpperCAmelCase_=True				,	UpperCAmelCase_=False				,	UpperCAmelCase_=99				,	UpperCAmelCase_=16				,	UpperCAmelCase_=2				,	UpperCAmelCase_=4				,	UpperCAmelCase_=4				,	UpperCAmelCase_="gelu"				,	UpperCAmelCase_=0.1				,	UpperCAmelCase_=0.1				,	UpperCAmelCase_=32				,	UpperCAmelCase_=2				,	UpperCAmelCase_=1				,	UpperCAmelCase_=0				,	UpperCAmelCase_=0.0_2				,	):
        lowerCamelCase			=parent
        lowerCamelCase			=batch_size
        lowerCamelCase			=seq_length
        lowerCamelCase			=is_training
        lowerCamelCase			=use_labels
        lowerCamelCase			=vocab_size
        lowerCamelCase			=hidden_size
        lowerCamelCase			=num_hidden_layers
        lowerCamelCase			=num_attention_heads
        lowerCamelCase			=intermediate_size
        lowerCamelCase			=hidden_act
        lowerCamelCase			=hidden_dropout_prob
        lowerCamelCase			=attention_probs_dropout_prob
        lowerCamelCase			=max_position_embeddings
        lowerCamelCase			=eos_token_id
        lowerCamelCase			=pad_token_id
        lowerCamelCase			=bos_token_id
        lowerCamelCase			=initializer_range


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

        lowerCamelCase			=shift_tokens_right(_UpperCAmelCase				,	1				,	2							)

        lowerCamelCase			=BlenderbotSmallConfig(
            vocab_size=self.vocab_size				,	d_model=self.hidden_size				,	encoder_layers=self.num_hidden_layers				,	decoder_layers=self.num_hidden_layers				,	encoder_attention_heads=self.num_attention_heads				,	decoder_attention_heads=self.num_attention_heads				,	encoder_ffn_dim=self.intermediate_size				,	decoder_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				,	initializer_range=self.initializer_range				,	use_cache=_UpperCAmelCase				,	)
        lowerCamelCase			=prepare_blenderbot_inputs_dict(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase							)
        return config, inputs_dict


    def _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase			=self.prepare_config_and_inputs()
        return config, inputs_dict


    def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
        lowerCamelCase			=20
        lowerCamelCase			=model_class_name(_UpperCAmelCase							)

        lowerCamelCase			=model.encode(inputs_dict["""input_ids"""]							)

        lowerCamelCase	,					lowerCamelCase			=(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        lowerCamelCase			=model.init_cache(decoder_input_ids.shape[0]				,	_UpperCAmelCase				,	_UpperCAmelCase							)
        lowerCamelCase			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,	dtype="""i4"""							)

        lowerCamelCase			=jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]				,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,	)
        lowerCamelCase			=model.decode(
            decoder_input_ids[:, :-1]				,	_UpperCAmelCase				,	decoder_attention_mask=_UpperCAmelCase				,	past_key_values=_UpperCAmelCase				,	decoder_position_ids=_UpperCAmelCase				,	)

        lowerCamelCase			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,	dtype="""i4"""							)
        lowerCamelCase			=model.decode(
            decoder_input_ids[:, -1:]				,	_UpperCAmelCase				,	decoder_attention_mask=_UpperCAmelCase				,	past_key_values=outputs_cache.past_key_values				,	decoder_position_ids=_UpperCAmelCase				,	)

        lowerCamelCase			=model.decode(_UpperCAmelCase				,	_UpperCAmelCase							)

        lowerCamelCase			=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				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
        lowerCamelCase			=20
        lowerCamelCase			=model_class_name(_UpperCAmelCase							)

        lowerCamelCase			=model.encode(inputs_dict["""input_ids"""]							)

        lowerCamelCase	,					lowerCamelCase			=(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        lowerCamelCase			=jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
            ]				,	axis=-1				,	)

        lowerCamelCase			=model.init_cache(decoder_input_ids.shape[0]				,	_UpperCAmelCase				,	_UpperCAmelCase							)
        lowerCamelCase			=jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]				,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,	)

        lowerCamelCase			=model.decode(
            decoder_input_ids[:, :-1]				,	_UpperCAmelCase				,	decoder_attention_mask=_UpperCAmelCase				,	past_key_values=_UpperCAmelCase				,	decoder_position_ids=_UpperCAmelCase				,	)
        lowerCamelCase			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,	dtype="""i4"""							)
        lowerCamelCase			=model.decode(
            decoder_input_ids[:, -1:]				,	_UpperCAmelCase				,	past_key_values=outputs_cache.past_key_values				,	decoder_attention_mask=_UpperCAmelCase				,	decoder_position_ids=_UpperCAmelCase				,	)

        lowerCamelCase			=model.decode(_UpperCAmelCase				,	_UpperCAmelCase				,	decoder_attention_mask=_UpperCAmelCase							)

        lowerCamelCase			=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       (  unittest.TestCase      ):
    __A								=			99


    def _snake_case    (						self							):
        lowerCamelCase			=np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]				,	dtype=np.intaa				,	)

        lowerCamelCase			=input_ids.shape[0]
        lowerCamelCase			=BlenderbotSmallConfig(
            vocab_size=self.vocab_size				,	d_model=24				,	encoder_layers=2				,	decoder_layers=2				,	encoder_attention_heads=2				,	decoder_attention_heads=2				,	encoder_ffn_dim=32				,	decoder_ffn_dim=32				,	max_position_embeddings=48				,	eos_token_id=2				,	pad_token_id=1				,	bos_token_id=0				,	)
        return config, input_ids, batch_size


    def _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=self._get_config_and_data()
        lowerCamelCase			=FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase							)
        lowerCamelCase			=lm_model(input_ids=_UpperCAmelCase							)
        lowerCamelCase			=(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape				,	_UpperCAmelCase							)


    def _snake_case    (						self							):
        lowerCamelCase			=BlenderbotSmallConfig(
            vocab_size=self.vocab_size				,	d_model=14				,	encoder_layers=2				,	decoder_layers=2				,	encoder_attention_heads=2				,	decoder_attention_heads=2				,	encoder_ffn_dim=8				,	decoder_ffn_dim=8				,	max_position_embeddings=48				,	)
        lowerCamelCase			=FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase							)
        lowerCamelCase			=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,	dtype=np.intaa							)
        lowerCamelCase			=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,	dtype=np.intaa							)
        lowerCamelCase			=lm_model(input_ids=_UpperCAmelCase				,	decoder_input_ids=_UpperCAmelCase							)
        lowerCamelCase			=(*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape				,	_UpperCAmelCase							)


    def _snake_case    (						self							):
        lowerCamelCase			=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,	dtype=np.intaa							)
        lowerCamelCase			=shift_tokens_right(_UpperCAmelCase				,	1				,	2							)
        lowerCamelCase			=np.equal(_UpperCAmelCase				,	1							).astype(np.floataa							).sum()
        lowerCamelCase			=np.equal(_UpperCAmelCase				,	1							).astype(np.floataa							).sum()
        self.assertEqual(shifted.shape				,	input_ids.shape							)
        self.assertEqual(_UpperCAmelCase				,	n_pad_before - 1							)
        self.assertTrue(np.equal(shifted[:, 0]				,	2							).all()							)




@require_flax
class  __A       (  lowerCamelCase_      ,   unittest.TestCase      ,   lowerCamelCase_      ):
    __A								=			True
    __A								=			(
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    __A								=			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


    def _snake_case    (						self							):
        lowerCamelCase			=FlaxBlenderbotSmallModelTester(self							)


    def _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase			=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 _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase			=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							)


    def _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase			=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__							):
                lowerCamelCase			=self._prepare_for_class(_UpperCAmelCase				,	_UpperCAmelCase							)
                lowerCamelCase			=model_class(_UpperCAmelCase							)

                @jax.jit
                def encode_jitted(UpperCAmelCase_				,	UpperCAmelCase_=None				,	**UpperCAmelCase_							):
                    return model.encode(input_ids=_UpperCAmelCase				,	attention_mask=_UpperCAmelCase							)

                with self.subTest("""JIT Enabled"""							):
                    lowerCamelCase			=encode_jitted(**_UpperCAmelCase							).to_tuple()

                with self.subTest("""JIT Disabled"""							):
                    with jax.disable_jit():
                        lowerCamelCase			=encode_jitted(**_UpperCAmelCase							).to_tuple()

                self.assertEqual(len(_UpperCAmelCase							)				,	len(_UpperCAmelCase							)							)
                for jitted_output, output in zip(_UpperCAmelCase				,	_UpperCAmelCase							):
                    self.assertEqual(jitted_output.shape				,	output.shape							)


    def _snake_case    (						self							):
        lowerCamelCase	,					lowerCamelCase			=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__							):
                lowerCamelCase			=model_class(_UpperCAmelCase							)
                lowerCamelCase			=model.encode(inputs_dict["""input_ids"""]				,	inputs_dict["""attention_mask"""]							)

                lowerCamelCase			={
                    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                    """encoder_outputs""": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
                    return model.decode(
                        decoder_input_ids=_UpperCAmelCase				,	decoder_attention_mask=_UpperCAmelCase				,	encoder_outputs=_UpperCAmelCase				,	)

                with self.subTest("""JIT Enabled"""							):
                    lowerCamelCase			=decode_jitted(**_UpperCAmelCase							).to_tuple()

                with self.subTest("""JIT Disabled"""							):
                    with jax.disable_jit():
                        lowerCamelCase			=decode_jitted(**_UpperCAmelCase							).to_tuple()

                self.assertEqual(len(_UpperCAmelCase							)				,	len(_UpperCAmelCase							)							)
                for jitted_output, output in zip(_UpperCAmelCase				,	_UpperCAmelCase							):
                    self.assertEqual(jitted_output.shape				,	output.shape							)


    @slow
    def _snake_case    (						self							):






        for model_class_name in self.all_model_classes:
            lowerCamelCase			=model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            lowerCamelCase			=np.ones((1, 1)							) * model.config.eos_token_id
            lowerCamelCase			=model(_UpperCAmelCase							)
            self.assertIsNotNone(_UpperCAmelCase							)












import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline





@pytest.mark.integration
def        _lowercase   (  )  ->					str:
				with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT			):
								with pytest.raises(_UpperCAmelCase			):
												requests.request("""GET"""     , """https://huggingface.co"""			)
								with pytest.raises(requests.exceptions.ConnectTimeout			):
												requests.request("""GET"""     , """https://huggingface.co"""     , timeout=1.0			)





@pytest.mark.integration
def        _lowercase   (  )  ->					Union[str, Any]:
				with offline(OfflineSimulationMode.CONNECTION_FAILS			):
								with pytest.raises(requests.exceptions.ConnectionError			):
												requests.request("""GET"""     , """https://huggingface.co"""			)





def        _lowercase   (  )  ->					int:
				with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1			):
								with pytest.raises(_UpperCAmelCase			):
												http_head("""https://huggingface.co"""			)





