


'''simple docstring'''



import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
		import bitsandbytes as bnb

from copy import deepcopy


__A		=logging.getLogger(__name__)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = None	, UpperCamelCase__ = None	, UpperCamelCase__ = None	, UpperCamelCase__ = None	, UpperCamelCase__ = None	, UpperCamelCase__ = False	, ):
			UpperCAmelCase__					:   str       				=       bnb_quantization_config.load_in_abit
			UpperCAmelCase__					:   str       				=       bnb_quantization_config.load_in_abit

			if load_in_abit and not is_abit_bnb_available():
						raise ImportError(
						    """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
						    """ make sure you have the latest version of `bitsandbytes` installed."""   )
			if load_in_abit and not is_abit_bnb_available():
						raise ValueError(
						    """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
						    """make sure you have the latest version of `bitsandbytes` installed."""   )

			UpperCAmelCase__					:   List[Any]       				=       []
			# custom device map
			if isinstance(UpperCamelCase__	, UpperCamelCase__   ) and len(device_map.keys()   ) > 1:
						UpperCAmelCase__					:   Dict       				=       [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

			# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
			if bnb_quantization_config.skip_modules is None:
						UpperCAmelCase__					:   Any       				=       get_keys_to_not_convert(UpperCamelCase__   )

			# add cpu modules to skip modules only for 4-bit modules
			if load_in_abit:
						bnb_quantization_config.skip_modules.extend(UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       bnb_quantization_config.skip_modules

			# We add the modules we want to keep in full precision
			if bnb_quantization_config.keep_in_fpaa_modules is None:
						UpperCAmelCase__					:   List[Any]       				=       []
			UpperCAmelCase__					:   int       				=       bnb_quantization_config.keep_in_fpaa_modules
			modules_to_not_convert.extend(UpperCamelCase__   )

			# compatibility with peft
			UpperCAmelCase__					:   Optional[int]       				=       load_in_abit
			UpperCAmelCase__					:   List[Any]       				=       load_in_abit

			UpperCAmelCase__					:   Dict       				=       get_parameter_device(UpperCamelCase__   )
			if model_device.type != "meta":
						# quantization of an already loaded model
						logger.warning(
						    """It is not recommended to quantize a loaded model. """
						    """The model should be instantiated under the `init_empty_weights` context manager."""   )
						UpperCAmelCase__					:   Optional[int]       				=       replace_with_bnb_layers(UpperCamelCase__	, UpperCamelCase__	, modules_to_not_convert=UpperCamelCase__   )
						# convert param to the right dtype
						UpperCAmelCase__					:   str       				=       bnb_quantization_config.torch_dtype
						for name, param in model.state_dict().items():
									if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules   ):
												param.to(torch.floataa   )
												if param.dtype != torch.floataa:
															UpperCAmelCase__					:   List[str]       				=       name.replace(""".weight"""	, """"""   ).replace(""".bias"""	, """"""   )
															UpperCAmelCase__					:   int       				=       getattr(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
															if param is not None:
																		param.to(torch.floataa   )
									elif torch.is_floating_point(UpperCamelCase__   ):
												param.to(UpperCamelCase__   )
						if model_device.type == "cuda":
									# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
									model.cuda(torch.cuda.current_device()   )
									torch.cuda.empty_cache()
						elif torch.cuda.is_available():
									model.to(torch.cuda.current_device()   )
						else:
									raise RuntimeError("""No GPU found. A GPU is needed for quantization."""   )
						logger.info(
						    f'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
						    """We move the model to cuda."""   )
						return model

			elif weights_location is None:
						raise RuntimeError(
						    f'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '''   )

			else:
						with init_empty_weights():
									UpperCAmelCase__					:   Tuple       				=       replace_with_bnb_layers(
									    UpperCamelCase__	, UpperCamelCase__	, modules_to_not_convert=UpperCamelCase__   )

						UpperCAmelCase__					:   Any       				=       get_quantized_model_device_map(
						    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, max_memory=UpperCamelCase__	, no_split_module_classes=UpperCamelCase__	, )
						if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
									UpperCAmelCase__					:   Dict       				=       True

						UpperCAmelCase__					:   Any       				=       any(x in list(device_map.values()   ) for x in ["""cpu""", """disk"""]   )

						load_checkpoint_in_model(
						    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, dtype=bnb_quantization_config.torch_dtype	, offload_folder=UpperCamelCase__	, offload_state_dict=UpperCamelCase__	, keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules	, offload_abit_bnb=load_in_abit and offload	, )
						return dispatch_model(UpperCamelCase__	, device_map=UpperCamelCase__	, offload_dir=UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None   ):
			if device_map is None:
						if torch.cuda.is_available():
									UpperCAmelCase__					:   Any       				=       {"""""": torch.cuda.current_device()}
						else:
									raise RuntimeError("""No GPU found. A GPU is needed for quantization."""   )
						logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""   )

			if isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
									raise ValueError(
									    """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
									    """'sequential'."""   )

						UpperCAmelCase__					:   List[Any]       				=       {}
						special_dtypes.update(
						    {
						        name: bnb_quantization_config.torch_dtype
						        for name, _ in model.named_parameters()
						        if any(m in name for m in bnb_quantization_config.skip_modules   )
						    }   )
						special_dtypes.update(
						    {
						        name: torch.floataa
						        for name, _ in model.named_parameters()
						        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules   )
						    }   )

						UpperCAmelCase__					:   Optional[Any]       				=       {}
						UpperCAmelCase__					:   Union[str, Any]       				=       special_dtypes
						UpperCAmelCase__					:   Optional[int]       				=       no_split_module_classes
						UpperCAmelCase__					:   Optional[Any]       				=       bnb_quantization_config.target_dtype

						# get max_memory for each device.
						if device_map != "sequential":
									UpperCAmelCase__					:   Optional[int]       				=       get_balanced_memory(
									    UpperCamelCase__	, low_zero=(device_map == """balanced_low_0""")	, max_memory=UpperCamelCase__	, **UpperCamelCase__	, )

						UpperCAmelCase__					:   str       				=       max_memory
						UpperCAmelCase__					:   Any       				=       infer_auto_device_map(UpperCamelCase__	, **UpperCamelCase__   )

			if isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						# check if don't have any quantized module on the cpu
						UpperCAmelCase__					:   Optional[int]       				=       bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

						UpperCAmelCase__					:   List[Any]       				=       {
						    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
						}
						for device in ["cpu", "disk"]:
									if device in device_map_without_some_modules.values():
												if bnb_quantization_config.load_in_abit:
															raise ValueError(
															    """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """   )
												else:
															logger.info(
															    """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""   )
						del device_map_without_some_modules
			return device_map







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None   ):

			if modules_to_not_convert is None:
						UpperCAmelCase__					:   Any       				=       []

			UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       _replace_with_bnb_layers(
			    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			if not has_been_replaced:
						logger.warning(
						    """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
						    """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
						    """ Please double check your model architecture, or submit an issue on github if you think this is"""
						    """ a bug."""   )
			return model







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, ):
			UpperCAmelCase__					:   List[str]       				=       False
			for name, module in model.named_children():
						if current_key_name is None:
									UpperCAmelCase__					:   Dict       				=       []
						current_key_name.append(UpperCamelCase__   )
						if isinstance(UpperCamelCase__	, nn.Linear   ) and name not in modules_to_not_convert:
									# Check if the current key is not in the `modules_to_not_convert`
									UpperCAmelCase__					:   List[str]       				=       """.""".join(UpperCamelCase__   )
									UpperCAmelCase__					:   List[Any]       				=       True
									for key in modules_to_not_convert:
												if (
												    (key in current_key_name_str) and (key + "." in current_key_name_str)
												) or key == current_key_name_str:
															UpperCAmelCase__					:   Union[str, Any]       				=       False
															break
									if proceed:
												# Load bnb module with empty weight and replace ``nn.Linear` module
												if bnb_quantization_config.load_in_abit:
															UpperCAmelCase__					:   Optional[Any]       				=       bnb.nn.LinearabitLt(
															    module.in_features	, module.out_features	, module.bias is not None	, has_fpaa_weights=UpperCamelCase__	, threshold=bnb_quantization_config.llm_inta_threshold	, )
												elif bnb_quantization_config.load_in_abit:
															UpperCAmelCase__					:   Dict       				=       bnb.nn.Linearabit(
															    module.in_features	, module.out_features	, module.bias is not None	, bnb_quantization_config.bnb_abit_compute_dtype	, compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant	, quant_type=bnb_quantization_config.bnb_abit_quant_type	, )
												else:
															raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""   )
												UpperCAmelCase__					:   int       				=       module.weight.data
												if module.bias is not None:
															UpperCAmelCase__					:   Dict       				=       module.bias.data
												bnb_module.requires_grad_(UpperCamelCase__   )
												setattr(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
												UpperCAmelCase__					:   Union[str, Any]       				=       True
						if len(list(module.children()   )   ) > 0:
									UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       _replace_with_bnb_layers(
									    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
									UpperCAmelCase__					:   Any       				=       has_been_replaced | _has_been_replaced
						# Remove the last key for recursion
						current_key_name.pop(-1   )
			return model, has_been_replaced







def       _UpperCamelCase (						UpperCamelCase__   ):
			# Create a copy of the model
			with init_empty_weights():
						UpperCAmelCase__					:   Optional[int]       				=       deepcopy(UpperCamelCase__   )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

			UpperCAmelCase__					:   Any       				=       find_tied_parameters(UpperCamelCase__   )
			# For compatibility with Accelerate < 0.18
			if isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						UpperCAmelCase__					:   Optional[Any]       				=       sum(list(tied_params.values()   )	, []   ) + list(tied_params.keys()   )
			else:
						UpperCAmelCase__					:   str       				=       sum(UpperCamelCase__	, []   )
			UpperCAmelCase__					:   int       				=       len(UpperCamelCase__   ) > 0

			# Check if it is a base model
			UpperCAmelCase__					:   int       				=       False
			if hasattr(UpperCamelCase__	, """base_model_prefix"""   ):
						UpperCAmelCase__					:   Tuple       				=       not hasattr(UpperCamelCase__	, model.base_model_prefix   )

			# Ignore this for base models (BertModel, GPT2Model, etc.)
			if (not has_tied_params) and is_base_model:
						return []

			# otherwise they have an attached head
			UpperCAmelCase__					:   Optional[Any]       				=       list(model.named_children()   )
			UpperCAmelCase__					:   int       				=       [list_modules[-1][0]]

			# add last module together with tied weights
			UpperCAmelCase__					:   Optional[int]       				=       set(UpperCamelCase__   ) - set(UpperCamelCase__   )
			UpperCAmelCase__					:   Any       				=       list(set(UpperCamelCase__   )   ) + list(UpperCamelCase__   )

			# remove ".weight" from the keys
			UpperCAmelCase__					:   int       				=       [""".weight""", """.bias"""]
			UpperCAmelCase__					:   str       				=       []
			for name in list_untouched:
						for name_to_remove in names_to_remove:
									if name_to_remove in name:
												UpperCAmelCase__					:   List[Any]       				=       name.replace(UpperCamelCase__	, """"""   )
						filtered_module_names.append(UpperCamelCase__   )

			return filtered_module_names







def       _UpperCamelCase (						UpperCamelCase__   ):
			for m in model.modules():
						if isinstance(UpperCamelCase__	, bnb.nn.Linearabit   ):
									return True
			return False







def       _UpperCamelCase (						UpperCamelCase__   ):
			return next(parameter.parameters()   ).device







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
			if fpaa_statistics is None:
						set_module_tensor_to_device(UpperCamelCase__	, UpperCamelCase__	, 0	, dtype=UpperCamelCase__	, value=UpperCamelCase__   )
						UpperCAmelCase__					:   Any       				=       param_name
						UpperCAmelCase__					:   Dict       				=       model
						if "." in tensor_name:
									UpperCAmelCase__					:   List[Any]       				=       tensor_name.split("""."""   )
									for split in splits[:-1]:
												UpperCAmelCase__					:   Optional[Any]       				=       getattr(UpperCamelCase__	, UpperCamelCase__   )
												if new_module is None:
															raise ValueError(f'''{module} has no attribute {split}.'''   )
												UpperCAmelCase__					:   List[str]       				=       new_module
									UpperCAmelCase__					:   Dict       				=       splits[-1]
						# offload weights
						UpperCAmelCase__					:   Any       				=       False
						offload_weight(module._parameters[tensor_name]	, UpperCamelCase__	, UpperCamelCase__	, index=UpperCamelCase__   )
						if hasattr(module._parameters[tensor_name]	, """SCB"""   ):
									offload_weight(
									    module._parameters[tensor_name].SCB	, param_name.replace("""weight"""	, """SCB"""   )	, UpperCamelCase__	, index=UpperCamelCase__	, )
			else:
						offload_weight(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, index=UpperCamelCase__   )
						offload_weight(UpperCamelCase__	, param_name.replace("""weight"""	, """SCB"""   )	, UpperCamelCase__	, index=UpperCamelCase__   )

			set_module_tensor_to_device(UpperCamelCase__	, UpperCamelCase__	, """meta"""	, dtype=UpperCamelCase__	, value=torch.empty(*param.size()   )   )



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 						_snake_case   :
					lowerCAmelCase :str						=   PegasusConfig
					lowerCAmelCase :Optional[int]						=   {}
					lowerCAmelCase :Dict						=   '''gelu'''




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=40							,							_lowerCamelCase=2							,							_lowerCamelCase=1							,							_lowerCamelCase=0							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   Any       				=       batch_size
								UpperCAmelCase__					:   Union[str, Any]       				=       seq_length
								UpperCAmelCase__					:   str       				=       is_training
								UpperCAmelCase__					:   Dict       				=       use_labels
								UpperCAmelCase__					:   Optional[int]       				=       vocab_size
								UpperCAmelCase__					:   int       				=       hidden_size
								UpperCAmelCase__					:   Union[str, Any]       				=       num_hidden_layers
								UpperCAmelCase__					:   str       				=       num_attention_heads
								UpperCAmelCase__					:   Any       				=       intermediate_size

								UpperCAmelCase__					:   int       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Optional[Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[str]       				=       max_position_embeddings
								UpperCAmelCase__					:   Union[str, Any]       				=       eos_token_id
								UpperCAmelCase__					:   Any       				=       pad_token_id
								UpperCAmelCase__					:   Dict       				=       bos_token_id




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size)
								UpperCAmelCase__					:   str       				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,							1)
								UpperCAmelCase__					:   Union[str, Any]       				=       tf.concat([input_ids, eos_tensor]							,							axis=1)

								UpperCAmelCase__					:   int       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Any       				=       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							,							**self.config_updates							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       prepare_pegasus_inputs_dict(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								return config, inputs_dict




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       TFPegasusModel(config=_lowerCamelCase).get_decoder()
								UpperCAmelCase__					:   Optional[int]       				=       inputs_dict["""input_ids"""]

								UpperCAmelCase__					:   Any       				=       input_ids[:1, :]
								UpperCAmelCase__					:   List[str]       				=       inputs_dict["""attention_mask"""][:1, :]
								UpperCAmelCase__					:   int       				=       inputs_dict["""head_mask"""]
								UpperCAmelCase__					:   int       				=       1

								# first forward pass
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							head_mask=_lowerCamelCase							,							use_cache=_lowerCamelCase)

								UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       outputs.to_tuple()

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

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

								UpperCAmelCase__					:   Union[str, Any]       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase)[0]
								UpperCAmelCase__					:   Any       				=       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
								UpperCAmelCase__					:   Optional[Any]       				=       int(ids_tensor((1,)							,							output_from_past.shape[-1]))
								UpperCAmelCase__					:   int       				=       output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase__					:   List[str]       				=       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	, UpperCamelCase__=None	, ):
			if attention_mask is None:
						UpperCAmelCase__					:   Any       				=       tf.cast(tf.math.not_equal(UpperCamelCase__	, config.pad_token_id   )	, tf.inta   )
			if decoder_attention_mask is None:
						UpperCAmelCase__					:   Optional[int]       				=       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:
						UpperCAmelCase__					:   Tuple       				=       tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						UpperCAmelCase__					:   Dict       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						UpperCAmelCase__					:   Tuple       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}


@require_tf
class 						_snake_case   (       a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :str						=   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					lowerCAmelCase :Any						=   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase :List[str]						=   (
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase :int						=   True
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :Dict						=   False




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       TFPegasusModelTester(self)
								UpperCAmelCase__					:   Dict       				=       ConfigTester(self							,							config_class=_lowerCamelCase)




					def 			snake_case__  ( self):
								self.config_tester.run_common_tests()




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase)


@require_sentencepiece
@require_tokenizers
@require_tf
class 						_snake_case   (       unittest.TestCase		):
					lowerCAmelCase :Dict						=   [
					    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
					    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
					]
					lowerCAmelCase :Any						=   [
					    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
					    ''' reduce the risk of wildfires.''',
					    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
					]  # differs slightly from pytorch, likely due to numerical differences in linear layers
					lowerCAmelCase :Tuple						=   '''google/pegasus-xsum'''




					@cached_property
					def 			snake_case__  ( self):
								return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
								return model




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       self.translate_src_text(**_lowerCamelCase)
								assert self.expected_text == generated_words




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       self.tokenizer(self.src_text							,							**_lowerCamelCase							,							padding=_lowerCamelCase							,							return_tensors="""tf""")
								UpperCAmelCase__					:   Dict       				=       self.model.generate(
								    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2							,							use_cache=_lowerCamelCase							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=_lowerCamelCase)
								return generated_words




					@slow
					def 			snake_case__  ( self):
								self._assert_generated_batch_equal_expected()