import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)

_lowerCamelCase   :				str     			=						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
_lowerCamelCase   :				Any     			=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							List[str]					):
					for attribute in key.split("."					):
										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					)

					if weight_type is not None:
										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					).shape
					else:
										SCREAMING_SNAKE_CASE           =    hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
										    F" {value.shape} for {full_name}"					)

					if weight_type == "weight":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE           =    value
					else:
										SCREAMING_SNAKE_CASE           =    value

					logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Union[str, Any]					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE           =    hf_model.unispeech_sat.feature_extractor

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE           =    False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	hf_model.config.feat_extract_norm == "group" ,	)
															SCREAMING_SNAKE_CASE           =    True
										else:
															for key, mapped_key in MAPPING.items():
																				SCREAMING_SNAKE_CASE           =    "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																				if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
																									if "layer_norm_for_extract" in name and (".".join(name.split("."					)[:-1]					) != key):
																														# special case since naming is very similar
																														continue
																									SCREAMING_SNAKE_CASE           =    True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE           =    name.split(UpperCAmelCase__					)[0].split("."					)[-2]
																														SCREAMING_SNAKE_CASE           =    mapped_key.replace("*" ,	UpperCAmelCase__					)
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_g"
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_v"
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE           =    "bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														SCREAMING_SNAKE_CASE           =    "weight"
																									else:
																														SCREAMING_SNAKE_CASE           =    None
																									set_recursively(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
																				continue
										if not is_used:
															unused_weights.append(UpperCAmelCase__					)

					logger.warning(F"Unused weights: {unused_weights}"					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    full_name.split("conv_layers."					)[-1]
					SCREAMING_SNAKE_CASE           =    name.split("."					)
					SCREAMING_SNAKE_CASE           =    int(items[0]					)
					SCREAMING_SNAKE_CASE           =    int(items[1]					)

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
					else:
										unused_weights.append(UpperCAmelCase__					)





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Dict=None ,	UpperCAmelCase__       :							Tuple=None ,	UpperCAmelCase__       :							Optional[Any]=True					):
					if config_path is not None:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatConfig.from_pretrained(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatConfig()

					SCREAMING_SNAKE_CASE           =    ""

					if is_finetuned:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatForCTC(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatForPreTraining(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path] ,	arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
					SCREAMING_SNAKE_CASE           =    model[0].eval()

					recursively_load_weights(UpperCAmelCase__ ,	UpperCAmelCase__					)

					hf_wavavec.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
							_lowerCamelCase   :				Optional[int]     			=						argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							_lowerCamelCase   :				Union[str, Any]     			=						parser.parse_args()
							convert_unispeech_sat_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class   lowercase      :
							lowercase__  :    Dict									=							LEDConfig
							lowercase__  :    List[str]									=							{}
							lowercase__  :    Union[str, Any]									=							"""gelu"""




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict=13		,							_UpperCamelCase				:   Optional[int]=7		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   List[Any]=False		,							_UpperCamelCase				:   Dict=99		,							_UpperCamelCase				:   Optional[Any]=32		,							_UpperCamelCase				:   Any=2		,							_UpperCamelCase				:   List[str]=4		,							_UpperCamelCase				:   Union[str, Any]=37		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   List[Any]=0.1		,							_UpperCamelCase				:   Union[str, Any]=20		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   Optional[Any]=1		,							_UpperCamelCase				:   Optional[int]=0		,							_UpperCamelCase				:   int=4		,							)   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    seq_length
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    intermediate_size

												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    max_position_embeddings
												SCREAMING_SNAKE_CASE           =    eos_token_id
												SCREAMING_SNAKE_CASE           =    pad_token_id
												SCREAMING_SNAKE_CASE           =    bos_token_id
												SCREAMING_SNAKE_CASE           =    attention_window

												# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
												# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
												# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
												# because its local attention only attends to `self.attention_window` and one before and one after
												SCREAMING_SNAKE_CASE           =    self.attention_window + 2

												# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
												# the `test_attention_outputs` and `test_hidden_states_output` tests
												SCREAMING_SNAKE_CASE           =    (
												    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
												)




							def 		__snake_case(  self				:   int )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.seq_length - 1]		,							self.vocab_size )
												SCREAMING_SNAKE_CASE           =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )		,							1 )
												SCREAMING_SNAKE_CASE           =    tf.concat([input_ids, eos_tensor]		,							axis=1 )

												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size )

												SCREAMING_SNAKE_CASE           =    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		,							attention_window=self.attention_window		,							**self.config_updates		,							)
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.concat(
												    [tf.zeros_like(_UpperCamelCase )[:, :-1], tf.ones_like(_UpperCamelCase )[:, -1:]]		,							axis=-1		,							)
												SCREAMING_SNAKE_CASE           =    global_attention_mask
												return config, inputs_dict





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModel(config=_UpperCamelCase ).get_decoder()
												SCREAMING_SNAKE_CASE           =    inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE           =    input_ids[:1, :]
												SCREAMING_SNAKE_CASE           =    inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE           =    1

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												SCREAMING_SNAKE_CASE           =    ids_tensor((self.batch_size, 3)		,							config.vocab_size )
												SCREAMING_SNAKE_CASE           =    tf.cast(ids_tensor((self.batch_size, 3)		,							2 )		,							tf.inta )

												# append to next input_ids and
												SCREAMING_SNAKE_CASE           =    tf.concat([input_ids, next_tokens]		,							axis=-1 )
												SCREAMING_SNAKE_CASE           =    tf.concat([attention_mask, next_attn_mask]		,							axis=-1 )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[0]

												self.parent.assertEqual(next_tokens.shape[1]		,							output_from_past.shape[1] )

												# select random slice
												SCREAMING_SNAKE_CASE           =    int(ids_tensor((1,)		,							output_from_past.shape[-1] ) )
												SCREAMING_SNAKE_CASE           =    output_from_no_past[:, -3:, random_slice_idx]
												SCREAMING_SNAKE_CASE           =    output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(_UpperCamelCase		,							_UpperCamelCase		,							rtol=1e-3 )





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape ,	dtype=tf.inta					),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,	config.pad_token_id					) ,	tf.inta					),
										    ] ,	axis=-1 ,	)
					if head_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					    "decoder_input_ids": decoder_input_ids,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					}


@require_tf
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Optional[int]									=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
							lowercase__  :    int									=							(
							    {
							        """conversational""": TFLEDForConditionalGeneration,
							        """feature-extraction""": TFLEDModel,
							        """summarization""": TFLEDForConditionalGeneration,
							        """text2text-generation""": TFLEDForConditionalGeneration,
							        """translation""": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__  :    List[Any]									=							True
							lowercase__  :    List[str]									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Union[str, Any]									=							False




							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE           =    tf.zeros_like(inputs_dict["attention_mask"] )
												SCREAMING_SNAKE_CASE           =    2
												SCREAMING_SNAKE_CASE           =    tf.where(
												    tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices		,							1		,							inputs_dict["global_attention_mask"]		,							)

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    self.model_tester.seq_length
												SCREAMING_SNAKE_CASE           =    self.model_tester.encoder_seq_length

												def check_decoder_attentions_output(_UpperCamelCase				:   Dict ):
																	SCREAMING_SNAKE_CASE           =    outputs.decoder_attentions
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )
																	self.assertListEqual(
																	    list(decoder_attentions[0].shape[-3:] )		,							[self.model_tester.num_attention_heads, seq_length, seq_length]		,							)

												def check_encoder_attentions_output(_UpperCamelCase				:   Optional[Any] ):
																	SCREAMING_SNAKE_CASE           =    [t.numpy() for t in outputs.encoder_attentions]
																	SCREAMING_SNAKE_CASE           =    [t.numpy() for t in outputs.encoder_global_attentions]
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:] )		,							[self.model_tester.num_attention_heads, seq_length, seq_length]		,							)
																	self.assertListEqual(
																	    list(global_attentions[0].shape[-3:] )		,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]		,							)

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase )
																	self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																	check_encoder_attentions_output(_UpperCamelCase )

																	if self.is_encoder_decoder:
																						SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																						self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																						check_decoder_attentions_output(_UpperCamelCase )

																	# Check that output attentions can also be changed via the config
																	del inputs_dict["output_attentions"]
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																	check_encoder_attentions_output(_UpperCamelCase )

																	# Check attention is always last and order is fine
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,							len(_UpperCamelCase ) )
																	self.assertEqual(model.config.output_hidden_states		,							_UpperCamelCase )
																	check_encoder_attentions_output(_UpperCamelCase )




							@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												pass





							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												pass





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


_lowerCamelCase   :				str     			=						1e-4


@slow
@require_tf
class   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   int )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, 768)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3 )





							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, model.config.vocab_size)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3		,							rtol=1e-3 )




