

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _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 (
					    MODEL_MAPPING,
					    DataaVecVisionForImageClassification,
					    DataaVecVisionForSemanticSegmentation,
					    DataaVecVisionModel,
					)
					from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor

class __UpperCamelCase    :






					'''simple docstring'''



					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_0_0							,			lowerCamelCase__=1_3							,			lowerCamelCase__=3_0							,			lowerCamelCase__=2							,			lowerCamelCase__=3							,			lowerCamelCase__=True							,			lowerCamelCase__=True							,			lowerCamelCase__=3_2							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=3_7							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=1_0							,			lowerCamelCase__=0.02							,			lowerCamelCase__=3							,			lowerCamelCase__=None							,			lowerCamelCase__=[0, 1, 2, 3]							,			):
						UpperCAmelCase__:  Optional[Any]		       =   parent
						UpperCAmelCase__:  List[str]		       =   1_0_0
						UpperCAmelCase__:  List[str]		       =   batch_size
						UpperCAmelCase__:  str		       =   image_size
						UpperCAmelCase__:  Union[str, Any]		       =   patch_size
						UpperCAmelCase__:  List[str]		       =   num_channels
						UpperCAmelCase__:  List[Any]		       =   is_training
						UpperCAmelCase__:  str		       =   use_labels
						UpperCAmelCase__:  Dict		       =   hidden_size
						UpperCAmelCase__:  Any		       =   num_hidden_layers
						UpperCAmelCase__:  Any		       =   num_attention_heads
						UpperCAmelCase__:  List[Any]		       =   intermediate_size
						UpperCAmelCase__:  List[str]		       =   hidden_act
						UpperCAmelCase__:  Dict		       =   hidden_dropout_prob
						UpperCAmelCase__:  Optional[Any]		       =   attention_probs_dropout_prob
						UpperCAmelCase__:  int		       =   type_sequence_label_size
						UpperCAmelCase__:  Optional[Any]		       =   initializer_range
						UpperCAmelCase__:  List[str]		       =   scope
						UpperCAmelCase__:  List[Any]		       =   out_indices
						UpperCAmelCase__:  List[str]		       =   num_labels

						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						UpperCAmelCase__:  Dict		       =   (image_size // patch_size) ** 2
						UpperCAmelCase__:  List[Any]		       =   num_patches + 1





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						UpperCAmelCase__:  Dict		       =   None
						UpperCAmelCase__:  Optional[int]		       =   None
						if self.use_labels:
							UpperCAmelCase__:  List[Any]		       =   ids_tensor([self.batch_size]							,			self.type_sequence_label_size	)
							UpperCAmelCase__:  Tuple		       =   ids_tensor([self.batch_size, self.image_size, self.image_size]							,			self.num_labels	)

						UpperCAmelCase__:  List[Any]		       =   self.get_config()

						return config, pixel_values, labels, pixel_labels





					def       _UpperCAmelCase						(							self	):
						return DataaVecVisionConfig(
						    vocab_size=self.vocab_size							,			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							,			out_indices=self.out_indices							,			)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						UpperCAmelCase__:  Dict		       =   DataaVecVisionModel(config=lowerCamelCase__	)
						model.to(lowerCamelCase__	)
						model.eval()
						UpperCAmelCase__:  int		       =   model(lowerCamelCase__	)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						UpperCAmelCase__:  Optional[Any]		       =   (self.image_size // self.patch_size) ** 2
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, num_patches + 1, self.hidden_size)	)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						UpperCAmelCase__:  Optional[int]		       =   self.type_sequence_label_size
						UpperCAmelCase__:  Optional[Any]		       =   DataaVecVisionForImageClassification(lowerCamelCase__	)
						model.to(lowerCamelCase__	)
						model.eval()
						UpperCAmelCase__:  str		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__	)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)	)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						UpperCAmelCase__:  Union[str, Any]		       =   self.num_labels
						UpperCAmelCase__:  Optional[Any]		       =   DataaVecVisionForSemanticSegmentation(lowerCamelCase__	)
						model.to(lowerCamelCase__	)
						model.eval()
						UpperCAmelCase__:  int		       =   model(lowerCamelCase__	)
						self.parent.assertEqual(
						    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
						UpperCAmelCase__:  Optional[Any]		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__	)
						self.parent.assertEqual(
						    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   self.prepare_config_and_inputs()
						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   config_and_inputs
						UpperCAmelCase__:  Union[str, Any]		       =   {"pixel_values": pixel_values}
						return config, inputs_dict

@require_torch
class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__     			=      (
					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					__magic_name__     			=      (
					    {
					        "feature-extraction": DataaVecVisionModel,
					        "image-classification": DataaVecVisionForImageClassification,
					        "image-segmentation": DataaVecVisionForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					__magic_name__     			=      False
					__magic_name__     			=      False
					__magic_name__     			=      False





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   DataaVecVisionModelTester(self	)
						UpperCAmelCase__:  int		       =   ConfigTester(
						    self							,			config_class=lowerCamelCase__							,			has_text_modality=lowerCamelCase__							,			hidden_size=3_7	)





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





					def       _UpperCAmelCase						(							self	):
						# Data2VecVision does not use inputs_embeds
						pass





					@require_torch_multi_gpu
					@unittest.skip(
					    reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"	)
					def       _UpperCAmelCase						(							self	):
						pass





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
							self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)	)
							UpperCAmelCase__:  Optional[int]		       =   model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCamelCase__							,			nn.Linear	)	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()

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

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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_segmentation(*lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						if not self.model_tester.is_training:
							return

						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase__:  Optional[Any]		       =   True

						for model_class in self.all_model_classes:
							if model_class in [*get_values(lowerCamelCase__	)]:
								continue

							UpperCAmelCase__:  List[str]		       =   model_class(lowerCamelCase__	)
							model.to(lowerCamelCase__	)
							model.train()
							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							UpperCAmelCase__:  int		       =   model(**lowerCamelCase__	).loss
							loss.backward()





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
							return

						UpperCAmelCase__:  str		       =   False
						UpperCAmelCase__:  Dict		       =   True

						for model_class in self.all_model_classes:
							if model_class in [*get_values(lowerCamelCase__	)] or not model_class.supports_gradient_checkpointing:
								continue
							# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
							# this can then be incorporated into _prepare_for_class in test_modeling_common.py
							elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
								UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   inputs_dict["pixel_values"].shape
								UpperCAmelCase__:  Tuple		       =   torch.zeros(
								    [self.model_tester.batch_size, height, width]							,			device=lowerCamelCase__	).long()
							UpperCAmelCase__:  Any		       =   model_class(lowerCamelCase__	)
							model.gradient_checkpointing_enable()
							model.to(lowerCamelCase__	)
							model.train()
							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							UpperCAmelCase__:  str		       =   model(**lowerCamelCase__	).loss
							loss.backward()





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase__:  List[str]		       =   _config_zero_init(lowerCamelCase__	)
						for model_class in self.all_model_classes:
							UpperCAmelCase__:  Union[str, Any]		       =   model_class(config=lowerCamelCase__	)
							for name, param in model.named_parameters():
								# we skip lambda parameters as these require special initial values
								# determined by config.layer_scale_init_value
								if "lambda" in name:
									continue
								if 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"							,			)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__=2e-4							,			lowerCamelCase__="outputs"							,			lowerCamelCase__=None	):
						# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
						super().check_pt_tf_outputs(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)





					@slow
					def       _UpperCAmelCase						(							self	):
						for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase__:  List[str]		       =   DataaVecVisionModel.from_pretrained(lowerCamelCase__	)
							self.assertIsNotNone(lowerCamelCase__	)



def  _A  (			):
	UpperCAmelCase__:  List[Any]		       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
	return image

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






					'''simple docstring'''



					@cached_property
					def       _UpperCAmelCase						(							self	):
						return (
						    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"	) if is_vision_available() else None
						)





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"	).to(
						    lowerCamelCase__	)

						UpperCAmelCase__:  str		       =   self.default_image_processor
						UpperCAmelCase__:  Any		       =   prepare_img()
						UpperCAmelCase__:  Optional[int]		       =   image_processor(images=lowerCamelCase__							,			return_tensors="pt"	).to(lowerCamelCase__	)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase__:  Dict		       =   model(**lowerCamelCase__	)
						UpperCAmelCase__:  Optional[int]		       =   outputs.logits

						# verify the logits
						UpperCAmelCase__:  str		       =   torch.Size((1, 1_0_0_0)	)
						self.assertEqual(logits.shape							,			lowerCamelCase__	)

						UpperCAmelCase__:  Optional[Any]		       =   torch.tensor([0.3_277, -0.1_395, 0.0_911]	).to(lowerCamelCase__	)

						self.assertTrue(torch.allclose(logits[0, :3]							,			lowerCamelCase__							,			atol=1e-4	)	)

						UpperCAmelCase__:  str		       =   [model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
						self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()							,			lowerCamelCase__	)


def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)

	UpperCAmelCase__:  Any		       =   [
	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
	]
	UpperCAmelCase__:  List[Any]		       =   [
	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
	]

	for i in range(1  ,len_source_seq + 1			):
		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"

	for i in range(1  ,len_destination_seq + 1			):
		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"

	for i in range(1  ,len_source_seq + 1			):
		for j in range(1  ,len_destination_seq + 1			):
			if source_seq[i - 1] == destination_seq[j - 1]:
				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
			else:
				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)

			if costs[i - 1][j] + delete_cost < costs[i][j]:
				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"

			if costs[i][j - 1] + insert_cost < costs[i][j]:
				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"

	return costs, ops



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if i == 0 and j == 0:
		return []
	else:
		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
			seq.append(ops[i][j]			)
			return seq
		elif ops[i][j][0] == "D":
			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
			seq.append(ops[i][j]			)
			return seq
		else:
			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
			seq.append(ops[i][j]			)
			return seq


if __name__ == "__main__":
					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

					_lowerCAmelCase     :			List[Any]    =len(operations)
					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)

					_lowerCAmelCase     :			int    =list("""Python""")
					_lowerCAmelCase     :			Optional[int]    =0
					_lowerCAmelCase     :			List[Any]    =0

					with open("""min_cost.txt""", """w""") as file:
										for op in sequence:
															print("""""".join(string))

															if op[0] == "C":
																				file.write("""%-16s""" % """Copy %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost -= 1
															elif op[0] == "R":
																				_lowerCAmelCase     :			Tuple    =op[2]

																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
																				file.write("""\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 1
															elif op[0] == "D":
																				string.pop(i)

																				file.write("""%-16s""" % """Delete %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 2
															else:
																				string.insert(i, op[1])

																				file.write("""%-16s""" % """Insert %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 2

															i += 1

										print("""""".join(string))
										print("""Cost: """, cost)

										file.write("""\r\nMinimum cost: """ + str(cost))