






"""simple docstring"""

import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)






def _lowerCAmelCase	(  __lowerCamelCase:Any   ):

						'''simple docstring'''






						if model.config.model_type == "gpt2":
												return model.transformer.h[0].mlp.c_fc
						return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
			import torch
			import torch.nn as nn

			class 						A_							(      nn.Module  ):







							def __init__(							self							:						str						,    __lowerCamelCase							:						nn.Module						,    __lowerCamelCase							:						int	)				->					str:
													super().__init__()
													__magic_name__            =     module
													__magic_name__            =     nn.Sequential(
													    nn.Linear(module.in_features						,    __lowerCamelCase						,    bias=__lowerCamelCase	)						,    nn.Linear(__lowerCamelCase						,    module.out_features						,    bias=__lowerCamelCase	)						,    )
													__magic_name__            =     (2.0 / (5 * min(module.in_features						,    module.out_features	))) ** 0.5
													nn.init.normal_(self.adapter[0].weight						,    std=__lowerCamelCase	)
													nn.init.zeros_(self.adapter[1].weight	)
													self.adapter.to(module.weight.device	)







							def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						List[Any]						,    *__lowerCamelCase							:						Optional[int]						,    **__lowerCamelCase							:						Dict	)				->					str:
													return self.module(__lowerCamelCase						,    *__lowerCamelCase						,    **__lowerCamelCase	) + self.adapter(__lowerCamelCase	)

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						A_							(      unittest.TestCase  ):
				# We keep the constants inside the init function and model loading inside setUp function

				# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
				# Therefore here we use only bloom-1b3 to test our module
				UpperCAmelCase__        =				"""bigscience/bloom-1b7"""

				# Constant values
				UpperCAmelCase__        =				2.109_659_552_692_574

				UpperCAmelCase__        =				"""Hello my name is"""
				UpperCAmelCase__        =				set()
				EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''  )
				EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''  )
				EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''  )
				UpperCAmelCase__        =				1_0







				def     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     AutoTokenizer.from_pretrained(self.model_name	)





class 						A_							(      snake_case_  ):







				def     _snake_case					(							self							:						List[str]	)				->					int:
										super().setUp()

										# Models and tokenizer
										__magic_name__            =     AutoModelForCausalLM.from_pretrained(
										    self.model_name						,    torch_dtype=torch.floataa						,    device_map="auto"	)
										__magic_name__            =     AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)







				def     _snake_case					(							self							:						Any	)				->					Tuple:
										del self.model_fpaa
										del self.model_abit

										gc.collect()
										torch.cuda.empty_cache()







				def     _snake_case					(							self							:						str	)				->					Any:
										__magic_name__            =     self.model_abit.config

										self.assertTrue(hasattr(__lowerCamelCase						,    "quantization_config"	)	)

										__magic_name__            =     config.to_dict()
										__magic_name__            =     config.to_diff_dict()

										__magic_name__            =     config.to_json_string()







				def     _snake_case					(							self							:						str	)				->					Dict:
										from bitsandbytes.nn import Paramsabit

										__magic_name__            =     self.model_fpaa.get_memory_footprint()
										__magic_name__            =     self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit						,    self.EXPECTED_RELATIVE_DIFFERENCE	)
										__magic_name__            =     get_some_linear_layer(self.model_abit	)
										self.assertTrue(linear.weight.__class__ == Paramsabit	)







				def     _snake_case					(							self							:						Tuple	)				->					Any:
										from transformers import TaPreTrainedModel

										self.model_fpaa.get_memory_footprint()
										self.model_abit.get_memory_footprint()

										for name, module in self.model_abit.named_modules():
																if isinstance(__lowerCamelCase						,    torch.nn.Linear	):
																						if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																												# 4-bit parameters are packed in uint8 variables
																												self.assertTrue(module.weight.dtype == torch.uinta	)







				def     _snake_case					(							self							:						List[str]	)				->					Optional[int]:
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	)
										__magic_name__            =     self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0	)						,    max_new_tokens=1_0	)

										self.assertIn(self.tokenizer.decode(output_sequences[0]						,    skip_special_tokens=__lowerCamelCase	)						,    self.EXPECTED_OUTPUTS	)







				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										__magic_name__            =     BitsAndBytesConfig()
										__magic_name__            =     True

										__magic_name__            =     AutoModelForCausalLM.from_pretrained(
										    self.model_name						,    quantization_config=__lowerCamelCase						,    device_map="auto"	)

										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	)
										__magic_name__            =     model_abit_from_config.generate(
										    input_ids=encoded_input["input_ids"].to(0	)						,    max_new_tokens=1_0	)

										self.assertIn(self.tokenizer.decode(output_sequences[0]						,    skip_special_tokens=__lowerCamelCase	)						,    self.EXPECTED_OUTPUTS	)







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

										with self.assertRaises(__lowerCamelCase	), tempfile.TemporaryDirectory() as tmpdirname:
																self.model_abit.save_pretrained(__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					str:
										__magic_name__            =     BitsAndBytesConfig()

										with self.assertRaises(__lowerCamelCase	):
																__magic_name__            =     AutoModelForCausalLM.from_pretrained(
																    self.model_name						,    quantization_config=__lowerCamelCase						,    load_in_abit=__lowerCamelCase						,    device_map="auto"						,    bnb_abit_quant_type="nf4"						,    )







				def     _snake_case					(							self							:						List[str]	)				->					Dict:

										with self.assertRaises(__lowerCamelCase	):
																# Tries with `str`
																self.model_abit.to("cpu"	)

										with self.assertRaises(__lowerCamelCase	):
																# Tries with a `dtype``
																self.model_abit.to(torch.floataa	)

										with self.assertRaises(__lowerCamelCase	):
																# Tries with a `device`
																self.model_abit.to(torch.device("cuda:0"	)	)

										with self.assertRaises(__lowerCamelCase	):
																# Tries with a `device`
																self.model_abit.float()

										with self.assertRaises(__lowerCamelCase	):
																# Tries with a `device`
																self.model_abit.half()

										# Test if we did not break anything
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	)

										__magic_name__            =     self.model_fpaa.to(torch.floataa	)
										__magic_name__            =     self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0	)						,    max_new_tokens=1_0	)

										# Check this does not throw an error
										__magic_name__            =     self.model_fpaa.to("cpu"	)

										# Check this does not throw an error
										__magic_name__            =     self.model_fpaa.half()

										# Check this does not throw an error
										__magic_name__            =     self.model_fpaa.float()







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     AutoModelForSeqaSeqLM.from_pretrained("t5-small"						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa	)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						A_							(      unittest.TestCase  ):







				@classmethod
				def     _snake_case					(							cls							:						Dict	)				->					Any:
										__magic_name__            =     "t5-small"
										__magic_name__            =     "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
										__magic_name__            =     AutoTokenizer.from_pretrained(cls.model_name	)
										__magic_name__            =     "Translate in German: Hello, my dog is cute"







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										gc.collect()
										torch.cuda.empty_cache()







				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										from transformers import TaForConditionalGeneration

										__magic_name__            =     TaForConditionalGeneration._keep_in_fpaa_modules
										__magic_name__            =     None

										# test with `t5-small`
										__magic_name__            =     TaForConditionalGeneration.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	).to(0	)
										__magic_name__            =     model.generate(**__lowerCamelCase	)

										# test with `flan-t5-small`
										__magic_name__            =     TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	).to(0	)
										__magic_name__            =     model.generate(**__lowerCamelCase	)
										__magic_name__            =     modules







				def     _snake_case					(							self							:						Dict	)				->					Union[str, Any]:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										__magic_name__            =     TaForConditionalGeneration.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)

										# there was a bug with decoders - this test checks that it is fixed
										self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q						,    bnb.nn.Linearabit	)	)

										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	).to(0	)
										__magic_name__            =     model.generate(**__lowerCamelCase	)

										# test with `flan-t5-small`
										__magic_name__            =     TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	).to(0	)
										__magic_name__            =     model.generate(**__lowerCamelCase	)





class 						A_							(      snake_case_  ):







				def     _snake_case					(							self							:						Optional[int]	)				->					Optional[int]:
										super().setUp()
										# model_name
										__magic_name__            =     "bigscience/bloom-560m"
										__magic_name__            =     "t5-small"

										# Different types of model

										__magic_name__            =     AutoModel.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										# Sequence classification model
										__magic_name__            =     AutoModelForSequenceClassification.from_pretrained(
										    self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										# CausalLM model
										__magic_name__            =     AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)
										# Seq2seq model
										__magic_name__            =     AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name						,    load_in_abit=__lowerCamelCase						,    device_map="auto"	)







				def     _snake_case					(							self							:						int	)				->					str:
										del self.base_model
										del self.sequence_model
										del self.model_abit
										del self.seq_to_seq_model

										gc.collect()
										torch.cuda.empty_cache()







				def     _snake_case					(							self							:						Any	)				->					List[Any]:
										from bitsandbytes.nn import Paramsabit

										self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit	)

										# Other heads should be nn.Parameter
										self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter	)
										self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter	)
										self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter	)





class 						A_							(      snake_case_  ):







				def     _snake_case					(							self							:						int	)				->					List[str]:
										super().setUp()







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										del self.pipe

										gc.collect()
										torch.cuda.empty_cache()







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										__magic_name__            =     pipeline(
										    "text-generation"						,    model=self.model_name						,    model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}						,    max_new_tokens=self.MAX_NEW_TOKENS						,    )

										# Real second forward pass
										__magic_name__            =     self.pipe(self.input_text	)
										self.assertIn(pipeline_output[0]["generated_text"]						,    self.EXPECTED_OUTPUTS	)





@require_torch_multi_gpu
class 						A_							(      snake_case_  ):







				def     _snake_case					(							self							:						Dict	)				->					List[Any]:
										super().setUp()







				def     _snake_case					(							self							:						List[Any]	)				->					Union[str, Any]:
										__magic_name__            =     AutoModelForCausalLM.from_pretrained(
										    self.model_name						,    load_in_abit=__lowerCamelCase						,    device_map="balanced"	)

										# Check correct device map
										self.assertEqual(set(model_parallel.hf_device_map.values()	)						,    {0, 1}	)

										# Check that inference pass works on the model
										__magic_name__            =     self.tokenizer(self.input_text						,    return_tensors="pt"	)

										# Second real batch
										__magic_name__            =     model_parallel.generate(input_ids=encoded_input["input_ids"].to(0	)						,    max_new_tokens=1_0	)
										self.assertIn(self.tokenizer.decode(output_parallel[0]						,    skip_special_tokens=__lowerCamelCase	)						,    self.EXPECTED_OUTPUTS	)





class 						A_							(      snake_case_  ):







				def     _snake_case					(							self							:						Tuple	)				->					Union[str, Any]:
										__magic_name__            =     "facebook/opt-350m"
										super().setUp()







				def     _snake_case					(							self							:						Optional[int]	)				->					Dict:

										if version.parse(importlib.metadata.version("bitsandbytes"	)	) < version.parse("0.37.0"	):
																return

										# Step 1: freeze all parameters
										__magic_name__            =     AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__lowerCamelCase	)

										self.assertEqual(set(model.hf_device_map.values()	)						,    {torch.cuda.current_device()}	)

										for param in model.parameters():
																__magic_name__            =     False  # freeze the model - train adapters later
																if param.ndim == 1:
																						# cast the small parameters (e.g. layernorm) to fp32 for stability
																						__magic_name__            =     param.data.to(torch.floataa	)

        # Step 2: add adapters
										for _, module in model.named_modules():
																if "OPTAttention" in repr(type(__lowerCamelCase	)	):
																						__magic_name__            =     LoRALayer(module.q_proj						,    rank=1_6	)
																						__magic_name__            =     LoRALayer(module.k_proj						,    rank=1_6	)
																						__magic_name__            =     LoRALayer(module.v_proj						,    rank=1_6	)

        # Step 3: dummy batch
										__magic_name__            =     self.tokenizer("Test batch "						,    return_tensors="pt"	).to(0	)

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
																__magic_name__            =     model.forward(**__lowerCamelCase	)
																out.logits.norm().backward()

										for module in model.modules():
																if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																						self.assertTrue(module.adapter[1].weight.grad is not None	)
																						self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0	)
																elif isinstance(__lowerCamelCase						,    nn.Embedding	):
																						self.assertTrue(module.weight.grad is None	)





class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				"""gpt2-xl"""
				UpperCAmelCase__        =				3.3_191_854_854_152_187











"""simple docstring"""

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 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

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







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size	)
										__magic_name__            =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)						,    1	)
										__magic_name__            =     tf.concat([input_ids, eos_tensor]						,    axis=1	)

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

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







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

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

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

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

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

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

										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)[0]
										__magic_name__            =     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
										__magic_name__            =     int(ids_tensor((1,)						,    output_from_past.shape[-1]	)	)
										__magic_name__            =     output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__            =     output_from_past[:, :, random_slice_idx]

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






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






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







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Dict:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

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

																if self.is_encoder_decoder:
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     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"]
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     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
																__magic_name__            =     True
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     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     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int	)				->					str:
										# TODO: Head-masking not yet implement
										pass






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






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


lowercase    			=       1e-4


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







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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	)



