





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
						_SCREAMING_SNAKE_CASE							=						'platform'

						import jax
						import jax.numpy as jnp

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


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=None      ,       __a=None      ,       __a=None      ,       __a=None      ,       ):







	if attention_mask is None:
		snake_case_						:	List[Any]      =		np.where(input_ids != config.pad_token_id      ,       1      ,       0      )
	if decoder_attention_mask is None:
		snake_case_						:	Any      =		np.where(decoder_input_ids != config.pad_token_id      ,       1      ,       0      )
	if head_mask is None:
		snake_case_						:	int      =		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		snake_case_						:	Tuple      =		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		snake_case_						:	Optional[int]      =		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 SCREAMING_SNAKE_CASE_  :






						def __init__(					self					:					Any						,      _A					:					int						,      _A					:					List[Any]=13						,      _A					:					Union[str, Any]=7						,      _A					:					Optional[int]=True						,      _A					:					str=False						,      _A					:					Tuple=99						,      _A					:					Any=16						,      _A					:					List[Any]=2						,      _A					:					int=4						,      _A					:					Union[str, Any]=4						,      _A					:					List[Any]="gelu"						,      _A					:					Tuple=0.1						,      _A					:					Optional[Any]=0.1						,      _A					:					Optional[int]=32						,      _A					:					Optional[int]=2						,      _A					:					Dict=1						,      _A					:					List[str]=0						,      _A					:					Any=0.0_2						,      )							->			Optional[int]:







							"""simple docstring"""


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






						def UpperCAmelCase_ (					self					:					Dict		)							->			List[str]:







							"""simple docstring"""


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

							snake_case_						:	Optional[Any]      =		shift_tokens_right(__lowerCamelCase						,      1						,      2		)

							snake_case_						:	Tuple      =		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=__lowerCamelCase						,      )
							snake_case_						:	Tuple      =		prepare_blenderbot_inputs_dict(__lowerCamelCase						,      __lowerCamelCase						,      __lowerCamelCase		)
							return config, inputs_dict






						def UpperCAmelCase_ (					self					:					List[str]		)							->			Dict:







							"""simple docstring"""


							snake_case_  ,snake_case_						:	Optional[Any]      =		self.prepare_config_and_inputs()
							return config, inputs_dict






						def UpperCAmelCase_ (					self					:					Tuple						,      _A					:					Optional[int]						,      _A					:					Optional[int]						,      _A					:					Optional[Any]		)							->			Optional[Any]:







							"""simple docstring"""


							snake_case_						:	Any      =		20
							snake_case_						:	List[str]      =		model_class_name(__lowerCamelCase		)

							snake_case_						:	Any      =		model.encode(inputs_dict['input_ids']		)

							snake_case_  ,snake_case_						:	List[str]      =		(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							snake_case_						:	List[str]      =		model.init_cache(decoder_input_ids.shape[0]						,      __lowerCamelCase						,      __lowerCamelCase		)
							snake_case_						:	Tuple      =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,      dtype='i4'		)

							snake_case_						:	int      =		jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]						,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,      )
							snake_case_						:	List[Any]      =		model.decode(
							    decoder_input_ids[:, :-1]						,      __lowerCamelCase						,      decoder_attention_mask=__lowerCamelCase						,      past_key_values=__lowerCamelCase						,      decoder_position_ids=__lowerCamelCase						,      )

							snake_case_						:	List[Any]      =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,      dtype='i4'		)
							snake_case_						:	Dict      =		model.decode(
							    decoder_input_ids[:, -1:]						,      __lowerCamelCase						,      decoder_attention_mask=__lowerCamelCase						,      past_key_values=outputs_cache.past_key_values						,      decoder_position_ids=__lowerCamelCase						,      )

							snake_case_						:	List[Any]      =		model.decode(__lowerCamelCase						,      __lowerCamelCase		)

							snake_case_						:	Dict      =		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 UpperCAmelCase_ (					self					:					Optional[int]						,      _A					:					Any						,      _A					:					List[Any]						,      _A					:					int		)							->			Any:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		20
							snake_case_						:	Tuple      =		model_class_name(__lowerCamelCase		)

							snake_case_						:	List[str]      =		model.encode(inputs_dict['input_ids']		)

							snake_case_  ,snake_case_						:	Tuple      =		(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							snake_case_						:	List[str]      =		jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
							    ]						,      axis=-1						,      )

							snake_case_						:	int      =		model.init_cache(decoder_input_ids.shape[0]						,      __lowerCamelCase						,      __lowerCamelCase		)
							snake_case_						:	Optional[int]      =		jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]						,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,      )

							snake_case_						:	List[Any]      =		model.decode(
							    decoder_input_ids[:, :-1]						,      __lowerCamelCase						,      decoder_attention_mask=__lowerCamelCase						,      past_key_values=__lowerCamelCase						,      decoder_position_ids=__lowerCamelCase						,      )
							snake_case_						:	List[Any]      =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,      dtype='i4'		)
							snake_case_						:	int      =		model.decode(
							    decoder_input_ids[:, -1:]						,      __lowerCamelCase						,      past_key_values=outputs_cache.past_key_values						,      decoder_attention_mask=__lowerCamelCase						,      decoder_position_ids=__lowerCamelCase						,      )

							snake_case_						:	str      =		model.decode(__lowerCamelCase						,      __lowerCamelCase						,      decoder_attention_mask=__lowerCamelCase		)

							snake_case_						:	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 SCREAMING_SNAKE_CASE_  (    unittest.TestCase    ):
						__magic_name__:	Union[str, Any]       					=				99






						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			Any:







							"""simple docstring"""


							snake_case_						:	List[Any]      =		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						,      )

							snake_case_						:	int      =		input_ids.shape[0]
							snake_case_						:	List[str]      =		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 UpperCAmelCase_ (					self					:					Optional[int]		)							->			Tuple:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_						:	Optional[Any]      =		self._get_config_and_data()
							snake_case_						:	Optional[Any]      =		FlaxBlenderbotSmallForConditionalGeneration(__lowerCamelCase		)
							snake_case_						:	str      =		lm_model(input_ids=__lowerCamelCase		)
							snake_case_						:	List[Any]      =		(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape						,      __lowerCamelCase		)






						def UpperCAmelCase_ (					self					:					List[str]		)							->			Tuple:







							"""simple docstring"""


							snake_case_						:	str      =		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						,      )
							snake_case_						:	Optional[Any]      =		FlaxBlenderbotSmallForConditionalGeneration(__lowerCamelCase		)
							snake_case_						:	List[Any]      =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,      dtype=np.intaa		)
							snake_case_						:	Optional[Any]      =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,      dtype=np.intaa		)
							snake_case_						:	Any      =		lm_model(input_ids=__lowerCamelCase						,      decoder_input_ids=__lowerCamelCase		)
							snake_case_						:	Optional[int]      =		(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape						,      __lowerCamelCase		)






						def UpperCAmelCase_ (					self					:					str		)							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Union[str, Any]      =		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,      dtype=np.intaa		)
							snake_case_						:	Union[str, Any]      =		shift_tokens_right(__lowerCamelCase						,      1						,      2		)
							snake_case_						:	int      =		np.equal(__lowerCamelCase						,      1		).astype(np.floataa		).sum()
							snake_case_						:	Tuple      =		np.equal(__lowerCamelCase						,      1		).astype(np.floataa		).sum()
							self.assertEqual(shifted.shape						,      input_ids.shape		)
							self.assertEqual(__lowerCamelCase						,      n_pad_before - 1		)
							self.assertTrue(np.equal(shifted[:, 0]						,      2		).all()		)
@require_flax
class SCREAMING_SNAKE_CASE_  (    _UpperCamelCase				, unittest.TestCase				, _UpperCamelCase    ):
						__magic_name__:	List[Any]       					=				True
						__magic_name__:	Optional[int]       					=				(
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						__magic_name__:	int       					=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






						def UpperCAmelCase_ (					self					:					List[Any]		)							->			Optional[Any]:







							"""simple docstring"""


							snake_case_						:	Any      =		FlaxBlenderbotSmallModelTester(self		)






						def UpperCAmelCase_ (					self					:					List[str]		)							->			Any:







							"""simple docstring"""


							snake_case_  ,snake_case_						:	Tuple      =		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 UpperCAmelCase_ (					self					:					Tuple		)							->			Optional[Any]:







							"""simple docstring"""


							snake_case_  ,snake_case_						:	List[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		)






						def UpperCAmelCase_ (					self					:					Optional[Any]		)							->			List[Any]:







							"""simple docstring"""


							snake_case_  ,snake_case_						:	List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__		):
									snake_case_						:	str      =		self._prepare_for_class(__lowerCamelCase						,      __lowerCamelCase		)
									snake_case_						:	Optional[Any]      =		model_class(__lowerCamelCase		)

									@jax.jit
									def encode_jitted(_A					:					List[Any]						,      _A					:					Union[str, Any]=None						,      **_A					:					Optional[int]		):
										return model.encode(input_ids=__lowerCamelCase						,      attention_mask=__lowerCamelCase		)

									with self.subTest('JIT Enabled'		):
										snake_case_						:	Optional[Any]      =		encode_jitted(**__lowerCamelCase		).to_tuple()

									with self.subTest('JIT Disabled'		):
										with jax.disable_jit():
											snake_case_						:	List[Any]      =		encode_jitted(**__lowerCamelCase		).to_tuple()

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






						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			Tuple:







							"""simple docstring"""


							snake_case_  ,snake_case_						:	Any      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__		):
									snake_case_						:	List[Any]      =		model_class(__lowerCamelCase		)
									snake_case_						:	str      =		model.encode(inputs_dict['input_ids']						,      inputs_dict['attention_mask']		)

									snake_case_						:	Dict      =		{
									    '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(_A					:					Union[str, Any]						,      _A					:					Dict						,      _A					:					Dict		):
										return model.decode(
										    decoder_input_ids=__lowerCamelCase						,      decoder_attention_mask=__lowerCamelCase						,      encoder_outputs=__lowerCamelCase						,      )

									with self.subTest('JIT Enabled'		):
										snake_case_						:	Dict      =		decode_jitted(**__lowerCamelCase		).to_tuple()

									with self.subTest('JIT Disabled'		):
										with jax.disable_jit():
											snake_case_						:	Optional[Any]      =		decode_jitted(**__lowerCamelCase		).to_tuple()

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






						@slow
						def UpperCAmelCase_ (					self					:					List[str]		)							->			str:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_						:	Union[str, Any]      =		model_class_name.from_pretrained('facebook/blenderbot_small-90M'		)
								# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
								snake_case_						:	Union[str, Any]      =		np.ones((1, 1)		) * model.config.eos_token_id
								snake_case_						:	Tuple      =		model(__lowerCamelCase		)
								self.assertIsNotNone(__lowerCamelCase		)







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






