



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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	)						->      Any:
			if attention_mask is None:
						A__       =    np.where(input_ids != config.pad_token_id ,	1 ,	0					)
			if decoder_attention_mask is None:
						A__       =    np.where(decoder_input_ids != config.pad_token_id ,	1 ,	0					)
			if head_mask is None:
						A__       =    np.ones((config.encoder_layers, config.encoder_attention_heads)					)
			if decoder_head_mask is None:
						A__       =    np.ones((config.decoder_layers, config.decoder_attention_heads)					)
			if cross_attn_head_mask is None:
						A__       =    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      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Tuple				,					_snake_case					:       Any				,					_snake_case					:       Any=13				,					_snake_case					:       int=7				,					_snake_case					:       List[str]=True				,					_snake_case					:       Union[str, Any]=False				,					_snake_case					:       List[str]=99				,					_snake_case					:       List[Any]=16				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       List[Any]=4				,					_snake_case					:       Tuple=4				,					_snake_case					:       int="gelu"				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       str=0.1				,					_snake_case					:       List[Any]=32				,					_snake_case					:       str=2				,					_snake_case					:       Dict=1				,					_snake_case					:       int=0				,					_snake_case					:       int=0.02				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    seq_length
								A__       =    is_training
								A__       =    use_labels
								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    eos_token_id
								A__       =    pad_token_id
								A__       =    bos_token_id
								A__       =    initializer_range



					def     _a							(     self					:       str					):






								"""simple docstring"""


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

								A__       =    shift_tokens_right(_snake_case				,					1				,					2					)

								A__       =    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=_snake_case				,					)
								A__       =    prepare_blenderbot_inputs_dict(_snake_case				,					_snake_case				,					_snake_case					)
								return config, inputs_dict



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.prepare_config_and_inputs()
								return config, inputs_dict



					def     _a							(     self					:       Any				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       int				,					_snake_case					:       str					):






								"""simple docstring"""


								A__       =    20
								A__       =    model_class_name(_snake_case					)

								A__       =    model.encode(inputs_dict['input_ids']					)

								A__     , A__       =    (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								A__       =    model.init_cache(decoder_input_ids.shape[0]				,					_snake_case				,					_snake_case					)
								A__       =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,					dtype='i4'					)

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

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

								A__       =    model.decode(_snake_case				,					_snake_case					)

								A__       =    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     _a							(     self					:       Any				,					_snake_case					:       Optional[int]				,					_snake_case					:       List[Any]				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								A__       =    20
								A__       =    model_class_name(_snake_case					)

								A__       =    model.encode(inputs_dict['input_ids']					)

								A__     , A__       =    (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

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

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

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

								A__       =    model.decode(_snake_case				,					_snake_case				,					decoder_attention_mask=_snake_case					)

								A__       =    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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    99



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    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				,					)

								A__       =    input_ids.shape[0]
								A__       =    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     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__     , A__     , A__       =    self._get_config_and_data()
								A__       =    FlaxBlenderbotSmallForConditionalGeneration(_snake_case					)
								A__       =    lm_model(input_ids=_snake_case					)
								A__       =    (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['logits'].shape				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    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				,					)
								A__       =    FlaxBlenderbotSmallForConditionalGeneration(_snake_case					)
								A__       =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,					dtype=np.intaa					)
								A__       =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,					dtype=np.intaa					)
								A__       =    lm_model(input_ids=_snake_case				,					decoder_input_ids=_snake_case					)
								A__       =    (*summary.shape, config.vocab_size)
								self.assertEqual(outputs['logits'].shape				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


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







@require_flax
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase							,     UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    True
					A__				:		Union[str, Any]			       =    (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					A__				:		int			       =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    FlaxBlenderbotSmallModelTester(self					)



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






								"""simple docstring"""


								A__     , A__       =    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     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__     , A__       =    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					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
														A__       =    self._prepare_for_class(_snake_case				,					_snake_case					)
														A__       =    model_class(_snake_case					)

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

														with self.subTest('JIT Enabled'					):
																	A__       =    encode_jitted(**_snake_case					).to_tuple()

														with self.subTest('JIT Disabled'					):
																	with jax.disable_jit():
																				A__       =    encode_jitted(**_snake_case					).to_tuple()

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



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
														A__       =    model_class(_snake_case					)
														A__       =    model.encode(inputs_dict['input_ids']				,					inputs_dict['attention_mask']					)

														A__       =    {
														    '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(_snake_case					:       Optional[Any]				,					_snake_case					:       Tuple				,					_snake_case					:       Tuple					):
																	return model.decode(
																	    decoder_input_ids=_snake_case				,					decoder_attention_mask=_snake_case				,					encoder_outputs=_snake_case				,					)

														with self.subTest('JIT Enabled'					):
																	A__       =    decode_jitted(**_snake_case					).to_tuple()

														with self.subTest('JIT Disabled'					):
																	with jax.disable_jit():
																				A__       =    decode_jitted(**_snake_case					).to_tuple()

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



					@slow
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


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









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs



					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				,					reason='XFormers attention is only available with CUDA and `xformers` installed'				,					)
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								super().test_save_load_floataa(expected_max_diff=1E-1					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,					)




