


'''simple docstring'''



import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class 	UpperCamelCase__:




				def __init__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict=3				,   lowerCAmelCase      :			str=32				,   lowerCAmelCase      :			Tuple=3				,   lowerCAmelCase      :			List[str]=10				,   lowerCAmelCase      :			str=[8, 16, 32, 64]				,   lowerCAmelCase      :			Any=[1, 1, 2, 1]				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			Optional[int]=True				,   lowerCAmelCase      :			Any="relu"				,   lowerCAmelCase      :			List[str]=3				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			Dict=["stage2", "stage3", "stage4"]				,   lowerCAmelCase      :			int=[2, 3, 4]				,   lowerCAmelCase      :			int=1				,   )->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     embeddings_size
										UpperCAmelCase					    =     hidden_sizes
										UpperCAmelCase					    =     depths
										UpperCAmelCase					    =     is_training
										UpperCAmelCase					    =     use_labels
										UpperCAmelCase					    =     hidden_act
										UpperCAmelCase					    =     num_labels
										UpperCAmelCase					    =     scope
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     out_indices
										UpperCAmelCase					    =     num_groups




				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										UpperCAmelCase					    =     None
										if self.use_labels:
																UpperCAmelCase					    =     ids_tensor([self.batch_size]				,   self.num_labels				)

										UpperCAmelCase					    =     self.get_config()

										return config, pixel_values, labels




				def 					a__(		self      :			Optional[Any]				)->					str:

										"""simple docstring"""




										return BitConfig(
										    num_channels=self.num_channels				,   embeddings_size=self.embeddings_size				,   hidden_sizes=self.hidden_sizes				,   depths=self.depths				,   hidden_act=self.hidden_act				,   num_labels=self.num_labels				,   out_features=self.out_features				,   out_indices=self.out_indices				,   num_groups=self.num_groups				,   )




				def 					a__(		self      :			str				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			List[Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     BitModel(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     model(lowerCAmelCase				)
										self.parent.assertEqual(
										    result.last_hidden_state.shape				,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,   )




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     self.num_labels
										UpperCAmelCase					    =     BitForImageClassification(lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     model(lowerCAmelCase				,   labels=lowerCAmelCase				)
										self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)				)




				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Optional[int]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     BitBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     model(lowerCAmelCase				)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps				)				,   len(config.out_features				)				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				)				,   [self.batch_size, self.hidden_sizes[1], 4, 4]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				)				,   len(config.out_features				)				)
										self.parent.assertListEqual(model.channels				,   config.hidden_sizes[1:]				)

										# verify backbone works with out_features=None
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     BitBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     model(lowerCAmelCase				)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps				)				,   1				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				)				,   [self.batch_size, self.hidden_sizes[-1], 1, 1]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				)				,   1				)
										self.parent.assertListEqual(model.channels				,   [config.hidden_sizes[-1]]				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     config_and_inputs
										UpperCAmelCase					    =     {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				__magic_name__    :				str  		=	(
				    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__    :				Dict  		=	False
				__magic_name__    :				Optional[Any]  		=	False
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				List[Any]  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     BitModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)




				def 					a__(		self      :			str				)->					Any:

										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




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

										"""simple docstring"""




										return




				@unittest.skip(reason='''Bit does not output attentions'''				)
				def 					a__(		self      :			Tuple				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not use inputs_embeds'''				)
				def 					a__(		self      :			Union[str, Any]				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not support input and output embeddings'''				)
				def 					a__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										pass




				def 					a__(		self      :			Dict				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																UpperCAmelCase					    =     inspect.signature(model.forward				)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																UpperCAmelCase					    =     [*signature.parameters.keys()]

																UpperCAmelCase					    =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,   lowerCAmelCase				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase				)




				def 					a__(		self      :			Tuple				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(config=lowerCAmelCase				)
																for name, module in model.named_modules():
																						if isinstance(lowerCAmelCase				,   (nn.BatchNormad, nn.GroupNorm)				):
																												self.assertTrue(
																												    torch.all(module.weight == 1				)				,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""				,   )
																												self.assertTrue(
																												    torch.all(module.bias == 0				)				,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""				,   )




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

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[Any]				):
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()

																with torch.no_grad():
																						UpperCAmelCase					    =     model(**self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)				)

																UpperCAmelCase					    =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																UpperCAmelCase					    =     self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase				)				,   expected_num_stages + 1				)

																# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]				)				,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,   )

										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     ['''preactivation''', '''bottleneck''']
										for model_class in self.all_model_classes:
																for layer_type in layers_type:
																						UpperCAmelCase					    =     layer_type
																						UpperCAmelCase					    =     True
																						check_hidden_states_output(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						UpperCAmelCase					    =     True

																						check_hidden_states_output(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)




				@unittest.skip(reason='''Bit does not use feedforward chunking'''				)
				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase				)





				@slow
				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase					    =     BitModel.from_pretrained(lowerCAmelCase				)
																self.assertIsNotNone(lowerCAmelCase				)







def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
						return image






@require_torch
@require_vision
class 	UpperCamelCase__(       unittest.TestCase	):




				@cached_property
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
										)





				@slow
				def 					a__(		self      :			List[Any]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowerCAmelCase				)

										UpperCAmelCase					    =     self.default_image_processor
										UpperCAmelCase					    =     prepare_img()
										UpperCAmelCase					    =     image_processor(images=lowerCAmelCase				,   return_tensors='''pt'''				).to(lowerCAmelCase				)

										# forward pass
										with torch.no_grad():
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

										# verify the logits
										UpperCAmelCase					    =     torch.Size((1, 1000)				)
										self.assertEqual(outputs.logits.shape				,   lowerCAmelCase				)

										UpperCAmelCase					    =     torch.tensor([[-0.6526, -0.5263, -1.4398]]				).to(lowerCAmelCase				)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]				,   lowerCAmelCase				,   atol=1E-4				)				)







@require_torch
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				str  		=	(BitBackbone,) if is_torch_available() else ()
				__magic_name__    :				List[Any]  		=	BitConfig

				__magic_name__    :				Optional[Any]  		=	False




				def 					a__(		self      :			List[str]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     BitModelTester(self				)









'''simple docstring'''



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

						UpperCAmelCase					    =     1
						for dim in shape:
												total_dims *= dim

						UpperCAmelCase					    =     []
						for _ in range(A					):
												values.append(rng.randint(0				,       vocab_size - 1					)					)

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     inputs['''input_ids'''].shape[-1] // 2
										UpperCAmelCase					    =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																# hack to allow generate for models such as GPT2 as is done in `generate()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     pt_model_class(lowerCAmelCase				).eval()
																UpperCAmelCase					    =     load_flax_weights_in_pytorch_model(lowerCAmelCase				,   flax_model.params				)

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase					    =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																self.assertListEqual(pt_generation_outputs.numpy().tolist()				,   flax_generation_outputs.tolist()				)




				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[0]				,   input_ids.shape[0] * config.num_return_sequences				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Union[str, Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)





				def 					a__(		self      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(lowerCAmelCase				,   '''do_samples'''				):
																model.generate(lowerCAmelCase				,   do_samples=lowerCAmelCase				)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(lowerCAmelCase				,   '''foo'''				):
																UpperCAmelCase					    =     {'''foo''': '''bar'''}
																model.generate(lowerCAmelCase				,   **lowerCAmelCase				)





