from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.swin.modeling_tf_swin import (
						    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSwinForImageClassification,
						    TFSwinForMaskedImageModeling,
						    TFSwinModel,
						)


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=[1, 2, 1]       ,  lowerCAmelCase_=[2, 2, 4]       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=2.0       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0.0       ,  lowerCAmelCase_=0.0       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_="gelu"       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0.0_2       ,  lowerCAmelCase_=1e-5       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=10       ,  lowerCAmelCase_=8       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =image_size
		_lowercase   =patch_size
		_lowercase   =num_channels
		_lowercase   =embed_dim
		_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


	def __lowerCAmelCase      (   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 __lowerCAmelCase      (   self       ):
		return SwinConfig(
		    image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  embed_dim=self.embed_dim       ,  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       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =TFSwinModel(config=lowerCAmelCase_       )
		_lowercase   =model(lowerCAmelCase_       )

		_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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =TFSwinForMaskedImageModeling(config=lowerCAmelCase_       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(
		    result.logits.shape       ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =TFSwinForMaskedImageModeling(lowerCAmelCase_       )

		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, 1, self.image_size, self.image_size)       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =self.type_sequence_label_size
		_lowercase   =TFSwinForImageClassification(lowerCAmelCase_       )
		_lowercase   =model(lowerCAmelCase_       ,  labels=lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =TFSwinForImageClassification(lowerCAmelCase_       )
		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )


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

@require_tf
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE              =					(
	    {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =TFSwinModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       ,  embed_dim=37       )


	def __lowerCAmelCase      (   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 __lowerCAmelCase      (   self       ):
		return


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_       )


	@unittest.skip(reason="Swin does not use inputs_embeds"       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertIsInstance(model.get_input_embeddings()       ,  tf.keras.layers.Layer       )
			_lowercase   =model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(lowerCAmelCase_       ,  tf.keras.layers.Dense       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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

			_lowercase   =["pixel_values"]
			self.assertListEqual(arg_names[:1]       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		for model_class in self.all_model_classes:
			_lowercase   =True
			_lowercase   =False
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.attentions
			_lowercase   =len(self.model_tester.depths       )
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			_lowercase   =True
			_lowercase   =config.window_size**2
			_lowercase   =model_class(lowerCAmelCase_       )
			_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			self.assertListEqual(
			    list(attentions[0].shape[-3:]       )       ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,  )
			_lowercase   =len(lowerCAmelCase_       )

			# Check attention is always last and order is fine
			_lowercase   =True
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

			if hasattr(self.model_tester       ,  "num_hidden_states_types"       ):
				_lowercase   =self.model_tester.num_hidden_states_types
			else:
				# also another +1 for reshaped_hidden_states
				_lowercase   =2
			self.assertEqual(out_len + added_hidden_states       ,  len(lowerCAmelCase_       )       )

			_lowercase   =outputs.attentions

			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]       )       ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =model_class(lowerCAmelCase_       )
		_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
		_lowercase   =outputs.hidden_states

		_lowercase   =getattr(
		    self.model_tester       ,  "expected_num_hidden_layers"       ,  len(self.model_tester.depths       ) + 1       )
		self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

		# Swin has a different seq_length
		_lowercase   =to_atuple(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(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

		_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =reshaped_hidden_states[0].shape

		_lowercase   =tf.reshape(reshaped_hidden_states[0]       ,  (batch_size, num_channels, height * width)       )
		_lowercase   =tf.transpose(lowerCAmelCase_       ,  (0, 2, 1)       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =to_atuple(self.model_tester.image_size       )

		for model_class in self.all_model_classes:
			_lowercase   =True
			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

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

			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =3

		_lowercase   =to_atuple(self.model_tester.image_size       )
		_lowercase   =to_atuple(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:
			_lowercase   =True
			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  (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(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  (padded_height, padded_width)       )


	@slow
	def __lowerCAmelCase      (   self       ):
		for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			_lowercase   =TFSwinModel.from_pretrained(lowerCAmelCase_       )
			self.assertIsNotNone(lowerCAmelCase_       )

@require_vision
@require_tf
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	@cached_property
	def __lowerCAmelCase      (   self       ):
		return (
		    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"       )
		    if is_vision_available()
		    else None
		)


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"       )
		_lowercase   =self.default_image_processor

		_lowercase   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="tf"       )

		# forward pass
		_lowercase   =model(lowerCAmelCase_       )

		# verify the logits
		_lowercase   =tf.TensorShape((1, 1000)       )
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )
		_lowercase   =tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]       )
		self.assertTrue(np.allclose(outputs.logits[0, :3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =act_info[0] == "gated"

		if len(lowerCAmelCase_       ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_       ) > 2:
			raise ValueError(
			    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
			    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
			    "'gated-gelu' or 'relu'"       )

		# for backwards compatibility
		if feed_forward_proj == "gated-gelu":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

		if self.use_past:
			self.fill_with_past_key_values_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




