'''simple docstring'''




import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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_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 transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = 13	,   __lowerCAmelCase = 64	,   __lowerCAmelCase = 2	,   __lowerCAmelCase = 3	,   __lowerCAmelCase = 3	,   __lowerCAmelCase = True	,   __lowerCAmelCase = True	,   __lowerCAmelCase = 128	,   __lowerCAmelCase=[16, 32, 64, 128]	,   __lowerCAmelCase = 7	,   __lowerCAmelCase = 4	,   __lowerCAmelCase = 37	,   __lowerCAmelCase = "gelu"	,   __lowerCAmelCase = 0.1	,   __lowerCAmelCase = 0.1	,   __lowerCAmelCase = 10	,   __lowerCAmelCase = 0.02	,   __lowerCAmelCase = 2	,   __lowerCAmelCase = 1	,   __lowerCAmelCase = 128	,   __lowerCAmelCase = [2, 2, 2, 2]	,   __lowerCAmelCase = 2	,   __lowerCAmelCase = 2	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  image_size
					lowerCAmelCase        =  patch_size
					lowerCAmelCase        =  num_channels
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_labels
					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        =  type_sequence_label_size
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  encoder_stride
					lowerCAmelCase        =  num_attention_outputs
					lowerCAmelCase        =  embed_dim
					lowerCAmelCase        =  embed_dim + 1
					lowerCAmelCase        =  resolution
					lowerCAmelCase        =  depths
					lowerCAmelCase        =  hidden_sizes
					lowerCAmelCase        =  dim
					lowerCAmelCase        =  mlp_expansion_ratio



			def 							a_	(       self):




					"""simple docstring"""


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

					lowerCAmelCase        =  None
					if self.use_labels:
							lowerCAmelCase        =  ids_tensor([self.batch_size]	,   self.type_sequence_label_size)

					lowerCAmelCase        =  self.get_config()
					return config, pixel_values, labels



			def 							a_	(       self):




					"""simple docstring"""


					return EfficientFormerConfig(
					    image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   hidden_size=self.hidden_size	,   num_hidden_layers=self.num_hidden_layers	,   num_attention_heads=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   is_decoder=__lowerCAmelCase	,   initializer_range=self.initializer_range	,   encoder_stride=self.encoder_stride	,   resolution=self.resolution	,   depths=self.depths	,   hidden_sizes=self.hidden_sizes	,   dim=self.dim	,   mlp_expansion_ratio=self.mlp_expansion_ratio	,   )



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  EfficientFormerModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size))



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.type_sequence_label_size
					lowerCAmelCase        =  EfficientFormerForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  model(__lowerCAmelCase	,   labels=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size))

					# test greyscale images
					lowerCAmelCase        =  1
					lowerCAmelCase        =  EfficientFormerForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()

					lowerCAmelCase        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size))







			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					(
					    (
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					
					)        =  config_and_inputs
					lowerCAmelCase        =  {"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
class     a__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					List[Any]								=	(
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCAmelCase_      :					str								=	(
			    {
			        '''feature-extraction''': EfficientFormerModel,
			        '''image-classification''': (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCAmelCase_      :					List[Any]								=	False

			UpperCAmelCase_      :					List[str]								=	False
			UpperCAmelCase_      :					Optional[Any]								=	False
			UpperCAmelCase_      :					int								=	False



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  EfficientFormerModelTester(self)
					lowerCAmelCase        =  ConfigTester(
					    self	,   config_class=__lowerCAmelCase	,   has_text_modality=__lowerCAmelCase	,   hidden_size=37)



			def 							a_	(       self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""")
			def 							a_	(       self):




					"""simple docstring"""


					pass



			@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""")
			def 							a_	(       self):




					"""simple docstring"""


					pass



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase        =  ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					def check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()

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

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

							lowerCAmelCase        =  getattr(
							    self.model_tester	,   """expected_num_hidden_layers"""	,   self.model_tester.num_hidden_layers + 1)
							self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)

							if hasattr(self.model_tester	,   """encoder_seq_length"""):
									lowerCAmelCase        =  self.model_tester.encoder_seq_length
									if hasattr(self.model_tester	,   """chunk_length""") and self.model_tester.chunk_length > 1:
											lowerCAmelCase        =  seq_length * self.model_tester.chunk_length
							else:
									lowerCAmelCase        =  self.model_tester.seq_length

							self.assertListEqual(
							    list(hidden_states[-1].shape[-2:])	,   [seq_length, self.model_tester.hidden_size]	,   )

							if config.is_encoder_decoder:
									lowerCAmelCase        =  outputs.decoder_hidden_states

									self.assertIsInstance(__lowerCAmelCase	,   (list, tuple))
									self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)
									lowerCAmelCase        =  getattr(self.model_tester	,   """seq_length"""	,   __lowerCAmelCase)
									lowerCAmelCase        =  getattr(self.model_tester	,   """decoder_seq_length"""	,   __lowerCAmelCase)

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:])	,   [decoder_seq_length, self.model_tester.hidden_size]	,   )

					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase        =  True
							check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

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

							check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase        =  super()._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)

					if return_labels:
							if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
									del inputs_dict["labels"]

					return inputs_dict



			def 							a_	(       self):




					"""simple docstring"""


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



			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""")
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


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



			def 							a_	(       self):




					"""simple docstring"""


					if not self.model_tester.is_training:
							return

					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase        =  True

					for model_class in self.all_model_classes:
							# EfficientFormerForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(__lowerCAmelCase)
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.train()
							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)
							lowerCAmelCase        =  model(**__lowerCAmelCase).loss
							loss.backward()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					lowerCAmelCase        =  [
					    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
					    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
					    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
					]

					for model_class in self.all_model_classes:
							if (
							    model_class
							    not in [
							        *get_values(__lowerCAmelCase),
							    ]
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue

							for problem_type in problem_types:
									with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
											lowerCAmelCase        =  problem_type["""title"""]
											lowerCAmelCase        =  problem_type["""num_labels"""]

											lowerCAmelCase        =  model_class(__lowerCAmelCase)
											model.to(__lowerCAmelCase)
											model.train()

											lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)

											if problem_type["num_labels"] > 1:
													lowerCAmelCase        =  inputs["""labels"""].unsqueeze(1).repeat(1	,   problem_type["""num_labels"""])

											lowerCAmelCase        =  inputs["""labels"""].to(problem_type["""dtype"""])

											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
											# they have the same size." which is a symptom something in wrong for the regression problem.
											# See https://github.com/huggingface/transformers/issues/11780
											with warnings.catch_warnings(record=__lowerCAmelCase) as warning_list:
													lowerCAmelCase        =  model(**__lowerCAmelCase).loss
											for w in warning_list:
													if "Using a target size that is different to the input size" in str(w.message):
															raise ValueError(
															    f"Something is going wrong in the regression problem: intercepted {w.message}")

											loss.backward()



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase        =  EfficientFormerModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase        =  True

					lowerCAmelCase        =  getattr(self.model_tester	,   """seq_length"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  getattr(self.model_tester	,   """encoder_seq_length"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  getattr(self.model_tester	,   """key_length"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  getattr(self.model_tester	,   """chunk_length"""	,   __lowerCAmelCase)

					if chunk_length is not None and hasattr(self.model_tester	,   """num_hashes"""):
							lowerCAmelCase        =  encoder_seq_length * self.model_tester.num_hashes

					for model_class in self.all_model_classes:
							lowerCAmelCase        =  True
							lowerCAmelCase        =  False
							lowerCAmelCase        =  True
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							with torch.no_grad():
									lowerCAmelCase        =  model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase))
							lowerCAmelCase        =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(__lowerCAmelCase)	,   self.model_tester.num_attention_outputs)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase        =  True
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							with torch.no_grad():
									lowerCAmelCase        =  model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase))
							lowerCAmelCase        =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(__lowerCAmelCase)	,   self.model_tester.num_attention_outputs)

							if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:])	,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]	,   )
							else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:])	,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]	,   )







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

		'''simple docstring'''


		lowerCAmelCase        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
		return image






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


			'''simple docstring'''






			@cached_property
			def 							a_	(       self):




					"""simple docstring"""


					return (
					    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""")
					    if is_vision_available()
					    else None
					)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""").to(
					    __lowerCAmelCase)

					lowerCAmelCase        =  self.default_image_processor
					lowerCAmelCase        =  prepare_img()
					lowerCAmelCase        =  image_processor(images=__lowerCAmelCase	,   return_tensors="""pt""").to(__lowerCAmelCase)

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

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

					lowerCAmelCase        =  torch.tensor([-0.0555, 0.4825, -0.0852]).to(__lowerCAmelCase)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]	,   __lowerCAmelCase	,   atol=1E-4))







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  EfficientFormerForImageClassificationWithTeacher.from_pretrained(
					    """snap-research/efficientformer-l1-300""").to(__lowerCAmelCase)

					lowerCAmelCase        =  self.default_image_processor
					lowerCAmelCase        =  prepare_img()
					lowerCAmelCase        =  image_processor(images=__lowerCAmelCase	,   return_tensors="""pt""").to(__lowerCAmelCase)

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

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

					lowerCAmelCase        =  torch.tensor([-0.1312, 0.4353, -1.0499]).to(__lowerCAmelCase)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]	,   __lowerCAmelCase	,   atol=1E-4))





'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=14	,   __lowerCAmelCase=7	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=99	,   __lowerCAmelCase=32	,   __lowerCAmelCase=4	,   __lowerCAmelCase=4	,   __lowerCAmelCase=[[["global", "local"], 2]]	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=7	,   __lowerCAmelCase=0.02	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  seq_length
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_input_mask
					lowerCAmelCase        =  use_token_type_ids
					lowerCAmelCase        =  use_labels
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  hidden_size
					lowerCAmelCase        =  num_hidden_layers
					lowerCAmelCase        =  num_attention_heads
					lowerCAmelCase        =  attention_types
					lowerCAmelCase        =  intermediate_size
					lowerCAmelCase        =  hidden_act
					lowerCAmelCase        =  hidden_dropout_prob
					lowerCAmelCase        =  attention_probs_dropout_prob
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  window_size
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  None
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size)

					lowerCAmelCase        =  None
					if self.use_input_mask:
							lowerCAmelCase        =  random_attention_mask([self.batch_size, self.seq_length])

					lowerCAmelCase        =  GPTNeoConfig(
					    vocab_size=self.vocab_size	,   hidden_size=self.hidden_size	,   num_layers=self.num_hidden_layers	,   num_heads=self.num_attention_heads	,   max_position_embeddings=self.max_position_embeddings	,   use_cache=__lowerCAmelCase	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   window_size=self.window_size	,   attention_types=self.attention_types	,   )

					return (config, input_ids, input_mask)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  config_and_inputs
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.ones((input_ids.shape[0], max_decoder_length)	,   dtype="""i4""")

					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))
					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   attention_mask=__lowerCAmelCase	,   past_key_values=outputs_cache.past_key_values	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase)

					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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

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

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))

					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )
					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   past_key_values=outputs_cache.past_key_values	,   attention_mask=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase)

					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__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			UpperCAmelCase_      :					Optional[Any]								=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  FlaxGPTNeoModelTester(self)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  GPTaTokenizer.from_pretrained("""gpt2"""	,   pad_token="""<|endoftext|>"""	,   padding_side="""left""")
					lowerCAmelCase        =  tokenizer(["""Hello this is a long string""", """Hey"""]	,   return_tensors="""np"""	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase)

					lowerCAmelCase        =  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
					lowerCAmelCase        =  False
					lowerCAmelCase        =  model.config.eos_token_id

					lowerCAmelCase        =  jax.jit(model.generate)

					lowerCAmelCase        =  jit_generate(
					    inputs["""input_ids"""]	,   attention_mask=inputs["""attention_mask"""]	,   pad_token_id=tokenizer.pad_token_id).sequences

					lowerCAmelCase        =  tokenizer.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase)

					lowerCAmelCase        =  [
					    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
					    """Hey, I'm a little late to the party. I'm going to""",
					]

					self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					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__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   __lowerCAmelCase)
									lowerCAmelCase        =  fx_state

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  model_class.from_pretrained(__lowerCAmelCase	,   from_pt=__lowerCAmelCase)

									lowerCAmelCase        =  fx_model_loaded(**__lowerCAmelCase).to_tuple()
									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output_loaded, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output_loaded[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					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__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  load_flax_weights_in_pytorch_model(__lowerCAmelCase	,   fx_model.params)
									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  pt_model_class.from_pretrained(__lowerCAmelCase	,   from_flax=__lowerCAmelCase)

									with torch.no_grad():
											lowerCAmelCase        =  pt_model_loaded(**__lowerCAmelCase).to_tuple()

									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase        =  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
							lowerCAmelCase        =  model(np.ones((1, 1)))
							self.assertIsNotNone(__lowerCAmelCase)




