
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     lowercase		:




							def __init__(    self       ,A__       ,A__=1_3       ,A__=3_2       ,A__=2       ,A__=3       ,A__=1_6       ,A__=[3_2, 6_4, 1_2_8]       ,A__=[1, 2, 1]       ,A__=[2, 2, 4]       ,A__=2       ,A__=2.0       ,A__=True       ,A__=0.0       ,A__=0.0       ,A__=0.1       ,A__="gelu"       ,A__=False       ,A__=True       ,A__=0.02       ,A__=1E-5       ,A__=True       ,A__=None       ,A__=True       ,A__=1_0       ,A__=8       ,A__=["stage1", "stage2"]       ,A__=[1, 2]       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						image_size
												lowercase						     =						patch_size
												lowercase						     =						num_channels
												lowercase						     =						embed_dim
												lowercase						     =						hidden_sizes
												lowercase						     =						depths
												lowercase						     =						num_heads
												lowercase						     =						window_size
												lowercase						     =						mlp_ratio
												lowercase						     =						qkv_bias
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						drop_path_rate
												lowercase						     =						hidden_act
												lowercase						     =						use_absolute_embeddings
												lowercase						     =						patch_norm
												lowercase						     =						layer_norm_eps
												lowercase						     =						initializer_range
												lowercase						     =						is_training
												lowercase						     =						scope
												lowercase						     =						use_labels
												lowercase						     =						type_sequence_label_size
												lowercase						     =						encoder_stride
												lowercase						     =						out_features
												lowercase						     =						out_indices




							def 			A__				(    self):
												lowercase						     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

												lowercase						     =						self.get_config()

												return config, pixel_values, labels




							def 			A__				(    self):
												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       ,A__       ,A__       ,A__):
												lowercase						     =						FocalNetModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__)

												lowercase						     =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
												lowercase						     =						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       ,A__       ,A__       ,A__):
												lowercase						     =						FocalNetBackbone(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						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
												lowercase						     =						None
												lowercase						     =						FocalNetBackbone(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						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       ,A__       ,A__       ,A__):
												lowercase						     =						FocalNetForMaskedImageModeling(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__)
												self.parent.assertEqual(
												    result.reconstruction.shape       ,(self.batch_size, self.num_channels, self.image_size, self.image_size))

												# test greyscale images
												lowercase						     =						1
												lowercase						     =						FocalNetForMaskedImageModeling(A__)
												model.to(A__)
												model.eval()

												lowercase						     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
												lowercase						     =						model(A__)
												self.parent.assertEqual(result.reconstruction.shape       ,(self.batch_size, 1, self.image_size, self.image_size))




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						self.type_sequence_label_size
												lowercase						     =						FocalNetForImageClassification(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,labels=A__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size))

												# test greyscale images
												lowercase						     =						1
												lowercase						     =						FocalNetForImageClassification(A__)
												model.to(A__)
												model.eval()

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





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()

												lowercase     ,						lowercase     ,						lowercase						     =						config_and_inputs
												lowercase						     =						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Tuple	 =(
							    (
							        FocalNetModel,
							        FocalNetForImageClassification,
							        FocalNetForMaskedImageModeling,
							        FocalNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_	:	Dict	 =(
							    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowercase_	:	Any	 =False

							lowercase_	:	Optional[Any]	 =False
							lowercase_	:	Optional[Any]	 =False
							lowercase_	:	List[Any]	 =False
							lowercase_	:	Optional[Any]	 =False




							def 			A__				(    self):
												lowercase						     =						FocalNetModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=A__       ,embed_dim=3_7       ,has_text_modality=A__)




							def 			A__				(    self):
												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):
												return




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*A__)




							def 			A__				(    self):
												lowercase						     =						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):
												pass




							@unittest.skip(reason='''FocalNet does not use feedforward chunking''')
							def 			A__				(    self):
												pass




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

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




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase						     =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,A__)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						model_class(A__)
												model.to(A__)
												model.eval()

												with torch.no_grad():
																	lowercase						     =						model(**self._prepare_for_class(A__       ,A__))

												lowercase						     =						outputs.hidden_states

												lowercase						     =						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
												lowercase						     =						(
												    config.patch_size
												    if isinstance(config.patch_size       ,collections.abc.Iterable)
												    else (config.patch_size, config.patch_size)
												)

												lowercase						     =						(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]       ,)

												lowercase						     =						outputs.reshaped_hidden_states
												self.assertEqual(len(A__)       ,A__)

												lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						reshaped_hidden_states[0].shape
												lowercase						     =						(
												    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):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												lowercase						     =						(
												    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]:
																	lowercase						     =						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"]
																	lowercase						     =						True

																	self.check_hidden_states_output(A__       ,A__       ,A__       ,A__)




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()
												lowercase						     =						3

												lowercase						     =						(
												    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)
												)
												lowercase						     =						(
												    config.patch_size
												    if isinstance(config.patch_size       ,collections.abc.Iterable)
												    else (config.patch_size, config.patch_size)
												)

												lowercase						     =						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
												lowercase						     =						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

												for model_class in self.all_model_classes[:-1]:
																	lowercase						     =						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"]
																	lowercase						     =						True
																	self.check_hidden_states_output(A__       ,A__       ,A__       ,(padded_height, padded_width))




							@slow
							def 			A__				(    self):
												for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						FocalNetModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)





							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												lowercase						     =						_config_zero_init(A__)
												for model_class in self.all_model_classes:
																	lowercase						     =						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     lowercase		( unittest.TestCase		):




							@cached_property
							def 			A__				(    self):
												# TODO update organization
												return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''') if is_vision_available() else None





							@slow
							def 			A__				(    self):
												lowercase						     =						FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''').to(A__)
												lowercase						     =						self.default_image_processor

												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												lowercase						     =						image_processor(images=A__       ,return_tensors='''pt''').to(A__)

												# forward pass
												with torch.no_grad():
																	lowercase						     =						model(**A__)

												# verify the logits
												lowercase						     =						torch.Size((1, 1_0_0_0))
												self.assertEqual(outputs.logits.shape       ,A__)
												lowercase						     =						torch.tensor([0.2166, -0.4368, 0.2191]).to(A__)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,A__       ,atol=1E-4))
												self.assertTrue(outputs.logits.argmax(dim=-1).item()       ,2_8_1)







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	str	 =(FocalNetBackbone,) if is_torch_available() else ()
							lowercase_	:	Union[str, Any]	 =FocalNetConfig

							lowercase_	:	Union[str, Any]	 =False




							def 			A__				(    self):
												lowercase						     =						FocalNetModelTester(self)






import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class     lowercase		:




							def __init__(    self       ,A__       ,A__=9_9       ,A__=1_3       ,A__=1_6       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=False       ,A__=True       ,A__=2       ,A__=3_2       ,A__=4       ,A__=4       ,A__=3_0       ,A__=0       ,A__=1       ,A__=2       ,A__=None       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						decoder_seq_length
												# For common tests
												lowercase						     =						self.decoder_seq_length
												lowercase						     =						is_training
												lowercase						     =						use_attention_mask
												lowercase						     =						use_labels

												lowercase						     =						vocab_size
												lowercase						     =						d_model
												lowercase						     =						d_model
												lowercase						     =						decoder_layers
												lowercase						     =						decoder_layers
												lowercase						     =						decoder_ffn_dim
												lowercase						     =						decoder_attention_heads
												lowercase						     =						decoder_attention_heads
												lowercase						     =						eos_token_id
												lowercase						     =						bos_token_id
												lowercase						     =						pad_token_id
												lowercase						     =						decoder_start_token_id
												lowercase						     =						use_cache
												lowercase						     =						max_position_embeddings

												lowercase						     =						None
												lowercase						     =						decoder_seq_length
												lowercase						     =						2
												lowercase						     =						1




							def 			A__				(    self):
												lowercase						     =						ids_tensor([self.batch_size, self.decoder_seq_length]       ,self.vocab_size)

												lowercase						     =						None
												if self.use_attention_mask:
																	lowercase						     =						ids_tensor([self.batch_size, self.decoder_seq_length]       ,vocab_size=2)

												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size, self.decoder_seq_length]       ,self.vocab_size)

												lowercase						     =						TrOCRConfig(
												    vocab_size=self.vocab_size       ,d_model=self.d_model       ,decoder_layers=self.decoder_layers       ,decoder_ffn_dim=self.decoder_ffn_dim       ,decoder_attention_heads=self.decoder_attention_heads       ,eos_token_id=self.eos_token_id       ,bos_token_id=self.bos_token_id       ,use_cache=self.use_cache       ,pad_token_id=self.pad_token_id       ,decoder_start_token_id=self.decoder_start_token_id       ,max_position_embeddings=self.max_position_embeddings       ,)

												return (config, input_ids, attention_mask, lm_labels)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,):
												lowercase						     =						True
												lowercase						     =						TrOCRDecoder(config=A__).to(A__).eval()
												lowercase						     =						input_ids[:2]

												input_ids[input_ids == 0] += 1
												# first forward pass
												lowercase						     =						model(A__       ,use_cache=A__)
												lowercase						     =						model(A__)
												lowercase						     =						model(A__       ,use_cache=A__)

												self.parent.assertTrue(len(A__) == len(A__))
												self.parent.assertTrue(len(A__) == len(A__) + 1)

												lowercase						     =						outputs['''past_key_values''']

												# create hypothetical next token and extent to next_input_ids
												lowercase						     =						ids_tensor((2, 1)       ,config.vocab_size - 1) + 1

												# append to next input_ids and
												lowercase						     =						torch.cat([input_ids, next_tokens]       ,dim=-1)

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						model(A__       ,past_key_values=A__)['''last_hidden_state''']

												# select random slice
												lowercase						     =						ids_tensor((1,)       ,output_from_past.shape[-1]).item()
												lowercase						     =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
												lowercase						     =						output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												assert torch.allclose(A__       ,A__       ,atol=1E-3)





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						config_and_inputs

												lowercase						     =						{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Any	 =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
							lowercase_	:	Dict	 =(TrOCRForCausalLM,) if is_torch_available() else ()
							lowercase_	:	int	 ={'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
							lowercase_	:	List[Any]	 =True
							lowercase_	:	int	 =False




							def 			A__				(    self):
												lowercase						     =						TrOCRStandaloneDecoderModelTester(self       ,is_training=A__)
												lowercase						     =						ConfigTester(self       ,config_class=A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												self.config_tester.run_common_tests()




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past(*A__)




							def 			A__				(    self):
												return





							@unittest.skip('''The model doesn\'t support left padding''')  # and it's not used enough to be worth fixing :)
							def 			A__				(    self):
												pass




