





"""simple docstring"""


import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
    __lowerCamelCase      :List[str]	    =		'platform'
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class 			A__	:


     """simple docstring"""
     snake_case__	:					List[str]													=PegasusConfig
     snake_case__	:					Dict													={}
     snake_case__	:					Optional[Any]													="""gelu"""

     def __init__(	self:						List[Any]     ,		__a:						Optional[int]     ,		__a:						str=13     ,		__a:						Dict=7     ,		__a:						Dict=True     ,		__a:						List[str]=False     ,		__a:						List[Any]=99     ,		__a:						Union[str, Any]=32     ,		__a:						List[str]=5     ,		__a:						str=4     ,		__a:						Any=37     ,		__a:						int=0.1     ,		__a:						Optional[Any]=0.1     ,		__a:						Union[str, Any]=20     ,		__a:						Dict=2     ,		__a:						Optional[Any]=1     ,		__a:						Tuple=0     ,		)->    Optional[int]:
           lowerCamelCase					:					Union[str, Any]        =	parent
           lowerCamelCase					:					Dict        =	batch_size
           lowerCamelCase					:					Optional[int]        =	seq_length
           lowerCamelCase					:					Optional[Any]        =	is_training
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					Optional[int]        =	vocab_size
           lowerCamelCase					:					int        =	hidden_size
           lowerCamelCase					:					str        =	num_hidden_layers
           lowerCamelCase					:					Optional[Any]        =	num_attention_heads
           lowerCamelCase					:					Union[str, Any]        =	intermediate_size

           lowerCamelCase					:					Tuple        =	hidden_dropout_prob
           lowerCamelCase					:					List[Any]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[str]        =	max_position_embeddings
           lowerCamelCase					:					int        =	eos_token_id
           lowerCamelCase					:					Any        =	pad_token_id
           lowerCamelCase					:					int        =	bos_token_id

     def 		a__						(	self:						int	)->    Any:
           lowerCamelCase					:					Tuple        =	ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size	).clip(3     ,		self.vocab_size	)
           lowerCamelCase					:					int        =	np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)     ,		1	)
           lowerCamelCase					:					List[Any]        =	np.concatenate([input_ids, eos_tensor]     ,		axis=1	)

           lowerCamelCase					:					Tuple        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

           lowerCamelCase					:					Optional[int]        =	self.config_cls(
               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_ids=[2]     ,		bos_token_id=self.bos_token_id     ,		pad_token_id=self.pad_token_id     ,		decoder_start_token_id=self.pad_token_id     ,		**self.config_updates     ,		)
           lowerCamelCase					:					Tuple        =	prepare_pegasus_inputs_dict(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_	)
           return config, inputs_dict

     def 		a__						(	self:						Optional[int]     ,		__a:						Any     ,		__a:						int     ,		__a:						str	)->    str:
           lowerCamelCase					:					Optional[int]        =	20
           lowerCamelCase					:					Union[str, Any]        =	model_class_name(UpperCAmelCase_	)

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

           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

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

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

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

           lowerCamelCase					:					Tuple        =	model.decode(UpperCAmelCase_     ,		UpperCAmelCase_	)

           lowerCamelCase					:					Optional[Any]        =	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:						List[str]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[Any]	)->    Dict:
           lowerCamelCase					:					str        =	20
           lowerCamelCase					:					Union[str, Any]        =	model_class_name(UpperCAmelCase_	)

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

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

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

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

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

           lowerCamelCase					:					Union[str, Any]        =	model.decode(UpperCAmelCase_     ,		UpperCAmelCase_     ,		decoder_attention_mask=UpperCAmelCase_	)

           lowerCamelCase					:					Tuple        =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
           self.parent.assertTrue(diff < 1e-3     ,		msg=f'Max diff is {diff}'	)




def   snake_case		(     UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Any=None			,				UpperCamelCase__       :						Optional[int]=None			,				)   ->      Dict:
      if attention_mask is None:
            lowerCamelCase					:					int        =	np.not_equal(_SCREAMING_SNAKE_CASE			,				config.pad_token_id    ).astype(np.inta    )
      if decoder_attention_mask is None:
            lowerCamelCase					:					Optional[int]        =	np.concatenate(
                [
                    np.ones(decoder_input_ids[:, :1].shape			,				dtype=np.inta    ),
                    np.not_equal(decoder_input_ids[:, 1:]			,				config.pad_token_id    ).astype(np.inta    ),
                ]			,				axis=-1			,				)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }






@require_flax
class 			A__	(						snake_case__ ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[int]													=(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     snake_case__	:					Union[str, Any]													=(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     snake_case__	:					List[str]													=True
     snake_case__	:					str													=False
     snake_case__	:					Optional[int]													=False
     snake_case__	:					List[str]													=False

     def 		a__						(	self:						Tuple	)->    int:
           lowerCamelCase					:					List[Any]        =	FlaxPegasusModelTester(self	)
           lowerCamelCase					:					str        =	ConfigTester(self     ,		config_class=UpperCAmelCase_	)

     def 		a__						(	self:						Union[str, Any]	)->    Optional[Any]:
           self.config_tester.run_common_tests()

     def 		a__						(	self:						Tuple	)->    Dict:
           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_	)

     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           lowerCamelCase     ,			lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_	)

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase     ,			lowerCamelCase					:					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__	):
                       lowerCamelCase					:					Dict        =	self._prepare_for_class(UpperCAmelCase_     ,		UpperCAmelCase_	)
                       lowerCamelCase					:					Union[str, Any]        =	model_class(UpperCAmelCase_	)

                       @jax.jit
                       def encode_jitted(__a:						Optional[int]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[Any]	):
                             return model.encode(input_ids=UpperCAmelCase_     ,		attention_mask=UpperCAmelCase_	)

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

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

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

     def 		a__						(	self:						List[Any]	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                       lowerCamelCase					:					Union[str, Any]        =	model_class(UpperCAmelCase_	)
                       lowerCamelCase					:					Dict        =	model.encode(inputs_dict["""input_ids"""]     ,		inputs_dict["""attention_mask"""]	)

                       lowerCamelCase					:					List[Any]        =	{
                           """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:						Optional[int]     ,		__a:						Dict     ,		__a:						List[Any]	):
                             return model.decode(
                                 decoder_input_ids=UpperCAmelCase_     ,		decoder_attention_mask=UpperCAmelCase_     ,		encoder_outputs=UpperCAmelCase_     ,		)

                       with self.subTest("""JIT Enabled"""	):
                             lowerCamelCase					:					Optional[int]        =	decode_jitted(**UpperCAmelCase_	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   lowerCamelCase					:					Optional[Any]        =	decode_jitted(**UpperCAmelCase_	).to_tuple()

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

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



           for model_class_name in self.all_model_classes:
                 lowerCamelCase					:					int        =	model_class_name.from_pretrained("""google/pegasus-large"""     ,		from_pt=UpperCAmelCase_	)
                 lowerCamelCase					:					List[Any]        =	np.ones((1, 1)	)
                 lowerCamelCase					:					Any        =	model(UpperCAmelCase_	)
                 self.assertIsNotNone(UpperCAmelCase_	)







     @slow
     def 		a__						(	self:						Dict	)->    Tuple:
           lowerCamelCase					:					Optional[Any]        =	FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""	)
           lowerCamelCase					:					Optional[int]        =	PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""	)

           lowerCamelCase					:					str        =	[
               """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
               """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
           ]

           lowerCamelCase					:					Optional[int]        =	[
               """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
               """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
           ]

           lowerCamelCase					:					str        =	tokenizer(UpperCAmelCase_     ,		return_tensors="""np"""     ,		truncation=UpperCAmelCase_     ,		max_length=512     ,		padding=UpperCAmelCase_	)
           lowerCamelCase					:					Union[str, Any]        =	model.generate(**UpperCAmelCase_     ,		num_beams=2	).sequences
           lowerCamelCase					:					str        =	tokenizer.batch_decode(UpperCAmelCase_     ,		skip_special_tokens=UpperCAmelCase_	)
           assert tgt_text == decoded








"""simple docstring"""


import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						Optional[int]=13     ,		__a:						List[str]=32     ,		__a:						int=2     ,		__a:						List[str]=3     ,		__a:						Union[str, Any]=16     ,		__a:						int=[32, 64, 128]     ,		__a:						Optional[Any]=[1, 2, 1]     ,		__a:						Optional[int]=[2, 2, 4]     ,		__a:						Tuple=2     ,		__a:						Dict=2.0     ,		__a:						List[str]=True     ,		__a:						Optional[Any]=0.0     ,		__a:						Any=0.0     ,		__a:						List[Any]=0.1     ,		__a:						List[str]="gelu"     ,		__a:						Tuple=False     ,		__a:						Union[str, Any]=True     ,		__a:						Optional[int]=0.02     ,		__a:						Tuple=1e-5     ,		__a:						int=True     ,		__a:						List[Any]=None     ,		__a:						Optional[int]=True     ,		__a:						Dict=10     ,		__a:						List[str]=8     ,		__a:						Any=["stage1", "stage2"]     ,		__a:						Union[str, Any]=[1, 2]     ,		)->    Dict:
           lowerCamelCase					:					Dict        =	parent
           lowerCamelCase					:					Optional[Any]        =	batch_size
           lowerCamelCase					:					Union[str, Any]        =	image_size
           lowerCamelCase					:					Optional[int]        =	patch_size
           lowerCamelCase					:					Any        =	num_channels
           lowerCamelCase					:					Any        =	embed_dim
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					List[Any]        =	depths
           lowerCamelCase					:					Tuple        =	num_heads
           lowerCamelCase					:					List[Any]        =	window_size
           lowerCamelCase					:					str        =	mlp_ratio
           lowerCamelCase					:					str        =	qkv_bias
           lowerCamelCase					:					str        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Tuple        =	drop_path_rate
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					Tuple        =	use_absolute_embeddings
           lowerCamelCase					:					List[str]        =	patch_norm
           lowerCamelCase					:					List[str]        =	layer_norm_eps
           lowerCamelCase					:					str        =	initializer_range
           lowerCamelCase					:					Tuple        =	is_training
           lowerCamelCase					:					int        =	scope
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	type_sequence_label_size
           lowerCamelCase					:					str        =	encoder_stride
           lowerCamelCase					:					List[str]        =	out_features
           lowerCamelCase					:					Optional[int]        =	out_indices

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					str        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[str]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					str        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           return FocalNetConfig(
               image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		num_heads=self.num_heads     ,		window_size=self.window_size     ,		mlp_ratio=self.mlp_ratio     ,		qkv_bias=self.qkv_bias     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		drop_path_rate=self.drop_path_rate     ,		hidden_act=self.hidden_act     ,		use_absolute_embeddings=self.use_absolute_embeddings     ,		path_norm=self.patch_norm     ,		layer_norm_eps=self.layer_norm_eps     ,		initializer_range=self.initializer_range     ,		encoder_stride=self.encoder_stride     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		)

     def 		a__						(	self:						Tuple     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Tuple        =	model(__a	)

           lowerCamelCase					:					Any        =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
           lowerCamelCase					:					List[Any]        =	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, expected_seq_len, expected_dim)	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Dict     ,		__a:						Tuple     ,		__a:						List[Any]	)->    int:
           lowerCamelCase					:					List[Any]        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Optional[Any]        =	model(__a	)

           # 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.image_size, 8, 8]	)

           # 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
           lowerCamelCase					:					Dict        =	None
           lowerCamelCase					:					Dict        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size * 2, 4, 4]	)

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

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetForMaskedImageModeling(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a	)
           self.parent.assertEqual(
               result.reconstruction.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

           # test greyscale images
           lowerCamelCase					:					List[str]        =	1
           lowerCamelCase					:					Any        =	FocalNetForMaskedImageModeling(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					str        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Tuple        =	model(__a	)
           self.parent.assertEqual(result.reconstruction.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)	)

     def 		a__						(	self:						str     ,		__a:						Optional[Any]     ,		__a:						Optional[Any]     ,		__a:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	self.type_sequence_label_size
           lowerCamelCase					:					Optional[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)

           # test greyscale images
           lowerCamelCase					:					int        =	1
           lowerCamelCase					:					List[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)







     def 		a__						(	self:						int	)->    Optional[int]:
           lowerCamelCase					:					str        =	self.prepare_config_and_inputs()

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	config_and_inputs
           lowerCamelCase					:					List[Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Optional[int]													=(
         {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     snake_case__	:					Tuple													=False

     snake_case__	:					Dict													=False
     snake_case__	:					Dict													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Optional[int]													=False

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	FocalNetModelTester(self	)
           lowerCamelCase					:					Optional[Any]        =	ConfigTester(self     ,		config_class=__a     ,		embed_dim=37     ,		has_text_modality=__a	)

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

     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[Any]	)->    Dict:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						List[Any]	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__a	)

     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a	)

     @unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
     def 		a__						(	self:						Optional[Any]	)->    str:
           pass

     @unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
     def 		a__						(	self:						Optional[Any]	)->    Dict:
           pass

     def 		a__						(	self:						Optional[Any]	)->    Dict:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)	)
                 lowerCamelCase					:					Dict        =	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__a     ,		nn.Linear	)	)

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					int        =	model_class(__a	)
                 lowerCamelCase					:					int        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					Any        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[Any]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[str]	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	model_class(__a	)
           model.to(__a	)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase					:					List[str]        =	model(**self._prepare_for_class(__a     ,		__a	)	)

           lowerCamelCase					:					List[str]        =	outputs.hidden_states

           lowerCamelCase					:					Tuple        =	getattr(
               self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths	) + 1	)
           self.assertEqual(len(__a	)     ,		__a	)

           # FocalNet has a different seq_length
           lowerCamelCase					:					Tuple        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Dict        =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

           lowerCamelCase					:					Optional[Any]        =	outputs.reshaped_hidden_states
           self.assertEqual(len(__a	)     ,		__a	)

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	reshaped_hidden_states[0].shape
           lowerCamelCase					:					Tuple        =	(
               reshaped_hidden_states[0].view(__a     ,		__a     ,		height * width	).permute(0     ,		2     ,		1	)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

     def 		a__						(	self:						Any	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Union[str, Any]        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					List[str]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					List[Any]        =	True

                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

     def 		a__						(	self:						str	)->    Union[str, Any]:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase					:					List[str]        =	3

           lowerCamelCase					:					Any        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           lowerCamelCase					:					Optional[int]        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Optional[Any]        =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCamelCase					:					List[str]        =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					str        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					Union[str, Any]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

     @slow
     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					List[str]        =	FocalNetModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)







     def 		a__						(	self:						str	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					int        =	_config_zero_init(__a	)
           for model_class in self.all_model_classes:
                 lowerCamelCase					:					int        =	model_class(config=__a	)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized'     ,		)


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


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__a	)
           lowerCamelCase					:					Any        =	self.default_image_processor

           lowerCamelCase					:					Union[str, Any]        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
           lowerCamelCase					:					int        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Tuple        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)
           lowerCamelCase					:					List[str]        =	torch.tensor([0.21_66, -0.43_68, 0.21_91]	).to(__a	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__a     ,		atol=1e-4	)	)
           self.assertTrue(outputs.logits.argmax(dim=-1	).item()     ,		281	)


@require_torch
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=(FocalNetBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[int]													=FocalNetConfig

     snake_case__	:					str													=False

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




