import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType
@require_torch
class 	_a  :



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					()
	__SCREAMING_SNAKE_CASE              =					True


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.config_class

		# test default config
		_lowercase   =config_class()
		self.assertIsNotNone(lowerCAmelCase_       )
		_lowercase   =["stem"] + [F'''stage{idx}''' for idx in range(1       ,  len(config.depths       ) + 1       )]
		self.assertEqual(config.stage_names       ,  lowerCAmelCase_       )
		self.assertTrue(set(config.out_features       ).issubset(set(config.stage_names       )       )       )

		# Test out_features and out_indices are correctly set
		# out_features and out_indices both None
		_lowercase   =config_class(out_features=lowerCAmelCase_       ,  out_indices=lowerCAmelCase_       )
		self.assertEqual(config.out_features       ,  [config.stage_names[-1]]       )
		self.assertEqual(config.out_indices       ,  [len(config.stage_names       ) - 1]       )

		# out_features and out_indices both set
		_lowercase   =config_class(out_features=["stem", "stage1"]       ,  out_indices=[0, 1]       )
		self.assertEqual(config.out_features       ,  ["stem", "stage1"]       )
		self.assertEqual(config.out_indices       ,  [0, 1]       )

		# Only out_features set
		_lowercase   =config_class(out_features=["stage1", "stage3"]       )
		self.assertEqual(config.out_features       ,  ["stage1", "stage3"]       )
		self.assertEqual(config.out_indices       ,  [1, 3]       )

		# Only out_indices set
		_lowercase   =config_class(out_indices=[0, 2]       )
		self.assertEqual(config.out_features       ,  [config.stage_names[0], config.stage_names[2]]       )
		self.assertEqual(config.out_indices       ,  [0, 2]       )

		# Error raised when out_indices do not correspond to out_features
		with self.assertRaises(lowerCAmelCase_       ):
			_lowercase   =config_class(out_features=["stage1", "stage2"]       ,  out_indices=[0, 2]       )


	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.forward       )
			# 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()

		for model_class in self.all_model_classes:
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertEqual(len(model.channels       )       ,  len(config.out_features       )       )
			_lowercase   =model.num_features
			_lowercase   =[config.stage_names.index(lowerCAmelCase_       ) for feat in config.out_features]
			_lowercase   =[num_features[idx] for idx in out_indices]
			self.assertListEqual(model.channels       ,  lowerCAmelCase_       )

			_lowercase   =copy.deepcopy(lowerCAmelCase_       )
			_lowercase   =None
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertEqual(len(model.channels       )       ,  1       )
			self.assertListEqual(model.channels       ,  [num_features[-1]]       )

			_lowercase   =copy.deepcopy(lowerCAmelCase_       )
			_lowercase   =None
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertEqual(len(model.channels       )       ,  1       )
			self.assertListEqual(model.channels       ,  [num_features[-1]]       )


	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_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			_lowercase   =model(**lowerCAmelCase_       )

			self.assertEqual(len(result.feature_maps       )       ,  len(config.out_features       )       )
			self.assertEqual(len(model.channels       )       ,  len(config.out_features       )       )
			self.assertEqual(len(result.feature_maps       )       ,  len(config.out_indices       )       )
			self.assertEqual(len(model.channels       )       ,  len(config.out_indices       )       )

			# Check output of last stage is taken if out_features=None, out_indices=None
			_lowercase   =copy.deepcopy(lowerCAmelCase_       )
			_lowercase   =None
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			_lowercase   =model(**lowerCAmelCase_       )

			self.assertEqual(len(result.feature_maps       )       ,  1       )
			self.assertEqual(len(model.channels       )       ,  1       )

			_lowercase   =copy.deepcopy(lowerCAmelCase_       )
			_lowercase   =None
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			_lowercase   =model(**lowerCAmelCase_       )

			self.assertEqual(len(result.feature_maps       )       ,  1       )
			self.assertEqual(len(model.channels       )       ,  1       )

			# Check backbone can be initialized with fresh weights
			_lowercase   =copy.deepcopy(lowerCAmelCase_       )
			_lowercase   =False
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			_lowercase   =model(**lowerCAmelCase_       )


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

		for backbone_class in self.all_model_classes:
			_lowercase   =backbone_class(lowerCAmelCase_       )

			self.assertTrue(hasattr(lowerCAmelCase_       ,  "backbone_type"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "stage_names"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "num_features"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "out_indices"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "out_features"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "out_feature_channels"       )       )
			self.assertTrue(hasattr(lowerCAmelCase_       ,  "channels"       )       )

			self.assertIsInstance(backbone.backbone_type       ,  lowerCAmelCase_       )
			# Verify num_features has been initialized in the backbone init
			self.assertIsNotNone(backbone.num_features       )
			self.assertTrue(len(backbone.channels       ) == len(backbone.out_indices       )       )
			self.assertTrue(len(backbone.stage_names       ) == len(backbone.num_features       )       )
			self.assertTrue(len(backbone.channels       ) <= len(backbone.num_features       )       )
			self.assertTrue(len(backbone.out_feature_channels       ) == len(backbone.stage_names       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =inputs_dict["pixel_values"].shape[0]

		for backbone_class in self.all_model_classes:
			_lowercase   =backbone_class(lowerCAmelCase_       )
			backbone.to(lowerCAmelCase_       )
			backbone.eval()

			_lowercase   =backbone(**lowerCAmelCase_       )

			# Test default outputs and verify feature maps
			self.assertIsInstance(outputs.feature_maps       ,  lowerCAmelCase_       )
			self.assertTrue(len(outputs.feature_maps       ) == len(backbone.channels       )       )
			for feature_map, n_channels in zip(outputs.feature_maps       ,  backbone.channels       ):
				self.assertTrue(feature_map.shape[:2]       ,  (batch_size, n_channels)       )
			self.assertIsNone(outputs.hidden_states       )
			self.assertIsNone(outputs.attentions       )

			# Test output_hidden_states=True
			_lowercase   =backbone(**lowerCAmelCase_       ,  output_hidden_states=lowerCAmelCase_       )
			self.assertIsNotNone(outputs.hidden_states       )
			self.assertTrue(len(outputs.hidden_states       )       ,  len(backbone.stage_names       )       )
			for hidden_state, n_channels in zip(outputs.hidden_states       ,  backbone.channels       ):
				self.assertTrue(hidden_state.shape[:2]       ,  (batch_size, n_channels)       )

			# Test output_attentions=True
			if self.has_attentions:
				_lowercase   =backbone(**lowerCAmelCase_       ,  output_attentions=lowerCAmelCase_       )
				self.assertIsNotNone(outputs.attentions       )





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




