import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A_      =      logging.get_logger("transformers.models.speecht5")

A_      =      {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
A_      =      {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
A_      =      {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
A_      =      {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
A_      =      {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
A_      =      {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
A_      =      {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
A_      =      {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
A_      =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A_      =      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =      []
A_      =      [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			for attribute in key.split('''.'''       ):
						lowercase							=		getattr(UpperCAmelCase,		UpperCAmelCase       )

			if weight_type is not None:
						lowercase							=		getattr(UpperCAmelCase,		UpperCAmelCase       ).shape
			else:
						lowercase							=		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":
						lowercase							=		value
			elif weight_type == "weight_g":
						lowercase							=		value
			elif weight_type == "weight_v":
						lowercase							=		value
			elif weight_type == "bias":
						lowercase							=		value
			elif weight_type == "running_mean":
						lowercase							=		value
			elif weight_type == "running_var":
						lowercase							=		value
			elif weight_type == "num_batches_tracked":
						lowercase							=		value
			else:
						lowercase							=		value

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			for key in ignore_keys:
						if key.endswith('''.*'''       ):
									if name.startswith(key[:-1]       ):
												return True
						elif ".*." in key:
									lowercase		,lowercase							=		key.split('''.*.'''       )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		[]

			if task == "s2t":
						lowercase							=		hf_model.speechta.encoder.prenet.feature_encoder
						lowercase							=		MAPPING_S2T
						lowercase							=		IGNORE_KEYS_S2T
			elif task == "t2s":
						lowercase							=		None
						lowercase							=		MAPPING_T2S
						lowercase							=		IGNORE_KEYS_T2S
			elif task == "s2s":
						lowercase							=		hf_model.speechta.encoder.prenet.feature_encoder
						lowercase							=		MAPPING_S2S
						lowercase							=		IGNORE_KEYS_S2S
			else:
						raise ValueError(f'Unsupported task: {task}'       )

			for name, value in fairseq_dict.items():
						if should_ignore(UpperCAmelCase,		UpperCAmelCase       ):
									logger.info(f'{name} was ignored'       )
									continue

						lowercase							=		False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		hf_model.config.feat_extract_norm == '''group''',		)
									lowercase							=		True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															lowercase		,lowercase							=		key.split('''.*.'''       )
															if prefix in name and suffix in name:
																		lowercase							=		suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															lowercase							=		True
															if "*" in mapped_key:
																		lowercase							=		name.split(UpperCAmelCase       )[0].split('''.'''       )[-2]
																		lowercase							=		mapped_key.replace('''*''',		UpperCAmelCase       )
															if "weight_g" in name:
																		lowercase							=		'''weight_g'''
															elif "weight_v" in name:
																		lowercase							=		'''weight_v'''
															elif "bias" in name:
																		lowercase							=		'''bias'''
															elif "weight" in name:
																		lowercase							=		'''weight'''
															elif "running_mean" in name:
																		lowercase							=		'''running_mean'''
															elif "running_var" in name:
																		lowercase							=		'''running_var'''
															elif "num_batches_tracked" in name:
																		lowercase							=		'''num_batches_tracked'''
															else:
																		lowercase							=		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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		full_name.split('''conv_layers.'''       )[-1]
			lowercase							=		name.split('''.'''       )
			lowercase							=		int(items[0]       )
			lowercase							=		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.'       )
									lowercase							=		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.'       )
									lowercase							=		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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'       )
									lowercase							=		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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'       )
									lowercase							=		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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		)->       Optional[int]:



			"""simple docstring"""







			if config_path is not None:
						lowercase							=		SpeechTaConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		SpeechTaConfig()

			if task == "s2t":
						lowercase							=		config.max_text_positions
						lowercase							=		SpeechTaForSpeechToText(UpperCAmelCase       )
			elif task == "t2s":
						lowercase							=		1876
						lowercase							=		600
						lowercase							=		config.max_speech_positions
						lowercase							=		SpeechTaForTextToSpeech(UpperCAmelCase       )
			elif task == "s2s":
						lowercase							=		1876
						lowercase							=		config.max_speech_positions
						lowercase							=		SpeechTaForSpeechToSpeech(UpperCAmelCase       )
			else:
						raise ValueError(f'Unknown task name: {task}'       )

			if vocab_path:
						lowercase							=		SpeechTaTokenizer(UpperCAmelCase,		model_max_length=config.max_text_positions       )

						# Mask token behaves like a normal word, i.e. include the space before it
						lowercase							=		AddedToken('''<mask>''',		lstrip=UpperCAmelCase,		rstrip=UpperCAmelCase       )
						lowercase							=		mask_token
						tokenizer.add_special_tokens({'''mask_token''': mask_token}       )
						tokenizer.add_tokens(['''<ctc_blank>''']       )

			lowercase							=		SpeechTaFeatureExtractor()
			lowercase							=		SpeechTaProcessor(tokenizer=UpperCAmelCase,		feature_extractor=UpperCAmelCase       )
			processor.save_pretrained(UpperCAmelCase       )

			lowercase							=		torch.load(UpperCAmelCase       )
			recursively_load_weights(fairseq_checkpoint['''model'''],		UpperCAmelCase,		UpperCAmelCase       )

			model.save_pretrained(UpperCAmelCase       )

			if repo_id:
						print('''Pushing to the hub...'''       )
						processor.push_to_hub(UpperCAmelCase       )
						model.push_to_hub(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						parser.add_argument(
						    "--task",
						    default="s2t",
						    type=str,
						    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
						)
						parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
						parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						parser.add_argument(
						    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
						)

						A_      =      parser.parse_args()
						convert_speechta_checkpoint(
						    args.task,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.vocab_path,
						    args.push_to_hub,
						)






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					 =     LEDConfig
						lowercase					 =     {}
						lowercase					 =     'gelu'

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


									'''simple docstring'''

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

									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		max_position_embeddings
									lowercase							=		eos_token_id
									lowercase							=		pad_token_id
									lowercase							=		bos_token_id
									lowercase							=		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
									lowercase							=		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
									lowercase							=		(
									    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
									)

						def        __a		(	self				:      Optional[int]   )							->		Tuple:


									'''simple docstring'''

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

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

									lowercase							=		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							,							)
									lowercase							=		prepare_led_inputs_dict(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		tf.concat(
									    [tf.zeros_like(__lowerCamelCase   )[:, :-1], tf.ones_like(__lowerCamelCase   )[:, -1:]]							,							axis=-1							,							)
									lowercase							=		global_attention_mask
									return config, inputs_dict



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		TFLEDModel(config=__lowerCamelCase   ).get_decoder()
									lowercase							=		inputs_dict['''input_ids''']

									lowercase							=		input_ids[:1, :]
									lowercase							=		inputs_dict['''attention_mask'''][:1, :]
									lowercase							=		1

									# first forward pass
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							use_cache=__lowerCamelCase   )

									lowercase		,lowercase							=		outputs.to_tuple()

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

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

									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase   )[0]
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							past_key_values=__lowerCamelCase   )[0]

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

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

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		)->       str:



			"""simple docstring"""







			if attention_mask is None:
						lowercase							=		tf.cast(tf.math.not_equal(UpperCAmelCase,		config.pad_token_id       ),		tf.inta       )
			if decoder_attention_mask is None:
						lowercase							=		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:
						lowercase							=		tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
			if decoder_head_mask is None:
						lowercase							=		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					 =     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						lowercase					 =     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
						lowercase					 =     (
						    {
						        'conversational': TFLEDForConditionalGeneration,
						        'feature-extraction': TFLEDModel,
						        'summarization': TFLEDForConditionalGeneration,
						        'text2text-generation': TFLEDForConditionalGeneration,
						        'translation': TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowercase					 =     True
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Dict   )							->		int:


									'''simple docstring'''

									lowercase							=		TFLEDModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase   )

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


									'''simple docstring'''

									self.config_tester.run_common_tests()

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


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase   )

						def        __a		(	self				:      List[Any]   )							->		Tuple:


									'''simple docstring'''

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

									lowercase							=		True
									lowercase							=		self.model_tester.seq_length
									lowercase							=		self.model_tester.encoder_seq_length

									def check_decoder_attentions_output(__lowerCamelCase				:      int   ):
												lowercase							=		outputs.decoder_attentions
												self.assertEqual(len(__lowerCamelCase   )							,							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(__lowerCamelCase				:      Optional[Any]   ):
												lowercase							=		[t.numpy() for t in outputs.encoder_attentions]
												lowercase							=		[t.numpy() for t in outputs.encoder_global_attentions]
												self.assertEqual(len(__lowerCamelCase   )							,							self.model_tester.num_hidden_layers   )
												self.assertEqual(len(__lowerCamelCase   )							,							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:
												lowercase							=		True
												lowercase							=		False
												lowercase							=		False
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
												lowercase							=		len(__lowerCamelCase   )
												self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
												check_encoder_attentions_output(__lowerCamelCase   )

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

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

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

						@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
						def        __a		(	self				:      Dict   )							->		str:


									'''simple docstring'''

									pass



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


									'''simple docstring'''

									pass






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			return tf.constant(UpperCAmelCase,		dtype=tf.intaa       )


A_      =      1e-4






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

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


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, 7_68)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3   )



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


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, model.config.vocab_size)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3							,							rtol=1E-3   )





