



"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowercase				:				Dict   		=		{
    "sample_size": 3_2,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [3_2, 6_4],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowercase				:				List[str]   		=		{
    "sample_size": 6_4,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    "attention_head_dim": 6_4,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowercase				:				Any   		=		{
    "sample_size": 2_5_6,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    "attention_head_dim": 6_4,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowercase				:				Union[str, Any]   		=		{
    "num_train_timesteps": 4_0,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowercase				:				List[str]   		=		{
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowercase				:				List[Any]   		=		{
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def   snake_case__     (	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			if isinstance(__lowerCamelCase							,	__lowerCamelCase			):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError('''boolean value expected'''			)






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					Tuple=False			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=checkpoint[f'''{old_prefix}.in_layers.0.weight''']
			lowerCamelCase__	:	str				=checkpoint[f'''{old_prefix}.in_layers.0.bias''']
			lowerCamelCase__	:	Optional[int]				=checkpoint[f'''{old_prefix}.in_layers.2.weight''']
			lowerCamelCase__	:	Optional[Any]				=checkpoint[f'''{old_prefix}.in_layers.2.bias''']
			lowerCamelCase__	:	Optional[Any]				=checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
			lowerCamelCase__	:	Union[str, Any]				=checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
			lowerCamelCase__	:	Any				=checkpoint[f'''{old_prefix}.out_layers.0.weight''']
			lowerCamelCase__	:	Optional[Any]				=checkpoint[f'''{old_prefix}.out_layers.0.bias''']
			lowerCamelCase__	:	Any				=checkpoint[f'''{old_prefix}.out_layers.3.weight''']
			lowerCamelCase__	:	int				=checkpoint[f'''{old_prefix}.out_layers.3.bias''']

			if has_skip:
						lowerCamelCase__	:	List[str]				=checkpoint[f'''{old_prefix}.skip_connection.weight''']
						lowerCamelCase__	:	Dict				=checkpoint[f'''{old_prefix}.skip_connection.bias''']

			return new_checkpoint






def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str=None			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3							,	dim=0			)
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3							,	dim=0			)

			lowerCamelCase__	:	str				=checkpoint[f'''{old_prefix}.norm.weight''']
			lowerCamelCase__	:	List[str]				=checkpoint[f'''{old_prefix}.norm.bias''']

			lowerCamelCase__	:	Any				=weight_q.squeeze(-1			).squeeze(-1			)
			lowerCamelCase__	:	Dict				=bias_q.squeeze(-1			).squeeze(-1			)
			lowerCamelCase__	:	str				=weight_k.squeeze(-1			).squeeze(-1			)
			lowerCamelCase__	:	Dict				=bias_k.squeeze(-1			).squeeze(-1			)
			lowerCamelCase__	:	Any				=weight_v.squeeze(-1			).squeeze(-1			)
			lowerCamelCase__	:	str				=bias_v.squeeze(-1			).squeeze(-1			)

			lowerCamelCase__	:	str				=(
			    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1			).squeeze(-1			)
			)
			lowerCamelCase__	:	List[Any]				=checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1			).squeeze(-1			)

			return new_checkpoint






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Optional[int]			):
			"""simple docstring"""
			lowerCamelCase__	:	List[str]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)
			lowerCamelCase__	:	int				={}

			lowerCamelCase__	:	Union[str, Any]				=checkpoint['''time_embed.0.weight''']
			lowerCamelCase__	:	Any				=checkpoint['''time_embed.0.bias''']
			lowerCamelCase__	:	List[Any]				=checkpoint['''time_embed.2.weight''']
			lowerCamelCase__	:	str				=checkpoint['''time_embed.2.bias''']

			if unet_config["num_class_embeds"] is not None:
						lowerCamelCase__	:	Optional[Any]				=checkpoint['''label_emb.weight''']

			lowerCamelCase__	:	int				=checkpoint['''input_blocks.0.0.weight''']
			lowerCamelCase__	:	Any				=checkpoint['''input_blocks.0.0.bias''']

			lowerCamelCase__	:	int				=unet_config['''down_block_types''']
			lowerCamelCase__	:	str				=unet_config['''layers_per_block''']
			lowerCamelCase__	:	Optional[Any]				=unet_config['''attention_head_dim''']
			lowerCamelCase__	:	str				=unet_config['''block_out_channels''']
			lowerCamelCase__	:	List[Any]				=1
			lowerCamelCase__	:	int				=channels_list[0]

			for i, layer_type in enumerate(__lowerCamelCase			):
						lowerCamelCase__	:	Tuple				=channels_list[i]
						lowerCamelCase__	:	str				=current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(__lowerCamelCase			):
												lowerCamelCase__	:	List[Any]				=f'''down_blocks.{i}.resnets.{j}'''
												lowerCamelCase__	:	str				=f'''input_blocks.{current_layer}.0'''
												lowerCamelCase__	:	int				=True if j == 0 and downsample_block_has_skip else False
												lowerCamelCase__	:	List[Any]				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	has_skip=__lowerCamelCase			)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(__lowerCamelCase			):
												lowerCamelCase__	:	Any				=f'''down_blocks.{i}.resnets.{j}'''
												lowerCamelCase__	:	Any				=f'''input_blocks.{current_layer}.0'''
												lowerCamelCase__	:	Union[str, Any]				=True if j == 0 and downsample_block_has_skip else False
												lowerCamelCase__	:	Optional[int]				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	has_skip=__lowerCamelCase			)
												lowerCamelCase__	:	Dict				=f'''down_blocks.{i}.attentions.{j}'''
												lowerCamelCase__	:	int				=f'''input_blocks.{current_layer}.1'''
												lowerCamelCase__	:	str				=convert_attention(
												    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
												current_layer += 1

						if i != len(__lowerCamelCase			) - 1:
									lowerCamelCase__	:	Optional[Any]				=f'''down_blocks.{i}.downsamplers.0'''
									lowerCamelCase__	:	List[str]				=f'''input_blocks.{current_layer}.0'''
									lowerCamelCase__	:	Any				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
									current_layer += 1

						lowerCamelCase__	:	Optional[Any]				=current_channels

			# hardcoded the mid-block for now
			lowerCamelCase__	:	int				='''mid_block.resnets.0'''
			lowerCamelCase__	:	Optional[Any]				='''middle_block.0'''
			lowerCamelCase__	:	Dict				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
			lowerCamelCase__	:	Any				='''mid_block.attentions.0'''
			lowerCamelCase__	:	str				='''middle_block.1'''
			lowerCamelCase__	:	List[Any]				=convert_attention(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
			lowerCamelCase__	:	int				='''mid_block.resnets.1'''
			lowerCamelCase__	:	Optional[Any]				='''middle_block.2'''
			lowerCamelCase__	:	str				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

			lowerCamelCase__	:	Optional[Any]				=0
			lowerCamelCase__	:	str				=unet_config['''up_block_types''']

			for i, layer_type in enumerate(__lowerCamelCase			):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1			):
												lowerCamelCase__	:	List[str]				=f'''up_blocks.{i}.resnets.{j}'''
												lowerCamelCase__	:	str				=f'''output_blocks.{current_layer}.0'''
												lowerCamelCase__	:	List[str]				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	has_skip=__lowerCamelCase			)
												current_layer += 1

									if i != len(__lowerCamelCase			) - 1:
												lowerCamelCase__	:	Dict				=f'''up_blocks.{i}.upsamplers.0'''
												lowerCamelCase__	:	str				=f'''output_blocks.{current_layer-1}.1'''
												lowerCamelCase__	:	int				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1			):
												lowerCamelCase__	:	List[Any]				=f'''up_blocks.{i}.resnets.{j}'''
												lowerCamelCase__	:	Any				=f'''output_blocks.{current_layer}.0'''
												lowerCamelCase__	:	Union[str, Any]				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	has_skip=__lowerCamelCase			)
												lowerCamelCase__	:	Tuple				=f'''up_blocks.{i}.attentions.{j}'''
												lowerCamelCase__	:	Tuple				=f'''output_blocks.{current_layer}.1'''
												lowerCamelCase__	:	str				=convert_attention(
												    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
												current_layer += 1

									if i != len(__lowerCamelCase			) - 1:
												lowerCamelCase__	:	Tuple				=f'''up_blocks.{i}.upsamplers.0'''
												lowerCamelCase__	:	List[str]				=f'''output_blocks.{current_layer-1}.2'''
												lowerCamelCase__	:	Optional[Any]				=convert_resnet(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

			lowerCamelCase__	:	List[Any]				=checkpoint['''out.0.weight''']
			lowerCamelCase__	:	Optional[int]				=checkpoint['''out.0.bias''']
			lowerCamelCase__	:	List[Any]				=checkpoint['''out.2.weight''']
			lowerCamelCase__	:	Optional[Any]				=checkpoint['''out.2.bias''']

			return new_checkpoint


if __name__ == "__main__":
		_lowercase				:				Optional[Any]   		=		argparse.ArgumentParser()

		parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
		parser.add_argument(
		    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
		)
		parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

		_lowercase				:				Dict   		=		parser.parse_args()
		_lowercase				:				Dict   		=		strabool(args.class_cond)

		_lowercase				:				str   		=		os.path.basename(args.unet_path)
		print(f'Checkpoint: {ckpt_name}')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				_lowercase				:				int   		=		IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowercase				:				Dict   		=		LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				_lowercase				:				Optional[Any]   		=		TEST_UNET_CONFIG
		else:
				raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

		if not args.class_cond:
				_lowercase				:				Dict   		=		None

		_lowercase				:				Tuple   		=		con_pt_to_diffuser(args.unet_path, unet_config)

		_lowercase				:				Dict   		=		UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				_lowercase				:				Any   		=		CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				_lowercase				:				Tuple   		=		CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowercase				:				int   		=		CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

		_lowercase				:				Optional[Any]   		=		CMStochasticIterativeScheduler(**scheduler_config)

		_lowercase				:				Union[str, Any]   		=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)









"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

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



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

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

								lowerCamelCase__	:	int				=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,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

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

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

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

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, 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
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	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   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=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:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)




