

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 			a					:






						"""simple docstring"""




						_lowercase        =     BlenderbotSmallConfig
						_lowercase        =     {}
						_lowercase        =     """gelu"""


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=True  ,   A_=False  ,   A_=99  ,   A_=32  ,   A_=2  ,   A_=4  ,   A_=37  ,   A_=0.1  ,   A_=0.1  ,   A_=20  ,   A_=2  ,   A_=1  ,   A_=0  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      parent
										_UpperCAmelCase    :						Dict							=      batch_size
										_UpperCAmelCase    :						int							=      seq_length
										_UpperCAmelCase    :						Union[str, Any]							=      is_training
										_UpperCAmelCase    :						int							=      use_labels
										_UpperCAmelCase    :						Dict							=      vocab_size
										_UpperCAmelCase    :						Tuple							=      hidden_size
										_UpperCAmelCase    :						Optional[Any]							=      num_hidden_layers
										_UpperCAmelCase    :						Tuple							=      num_attention_heads
										_UpperCAmelCase    :						Optional[Any]							=      intermediate_size

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length - 1]  ,   self.vocab_size		)
										_UpperCAmelCase    :						Optional[int]							=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)  ,   1		)
										_UpperCAmelCase    :						List[str]							=      tf.concat([input_ids, eos_tensor]  ,   axis=1		)

										_UpperCAmelCase    :						Optional[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    :						Optional[int]							=      prepare_blenderbot_small_inputs_dict(__lowerCAmelCase  ,   __lowerCAmelCase  ,   __lowerCAmelCase		)
										return config, inputs_dict





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      TFBlenderbotSmallModel(config=__lowerCAmelCase		).get_decoder()
										_UpperCAmelCase    :						Optional[Any]							=      inputs_dict["input_ids"]

										_UpperCAmelCase    :						Union[str, Any]							=      input_ids[:1, :]
										_UpperCAmelCase    :						Dict							=      inputs_dict["attention_mask"][:1, :]
										_UpperCAmelCase    :						str							=      inputs_dict["head_mask"]
										_UpperCAmelCase    :						int							=      1

										# first forward pass
										_UpperCAmelCase    :						Any							=      model(__lowerCAmelCase  ,   attention_mask=__lowerCAmelCase  ,   head_mask=__lowerCAmelCase  ,   use_cache=__lowerCAmelCase		)

										_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_UpperCAmelCase    :						Tuple							=      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    :						int							=      tf.concat([attention_mask, next_attn_mask]  ,   axis=-1		)

										_UpperCAmelCase    :						List[str]							=      model(__lowerCAmelCase  ,   attention_mask=__lowerCAmelCase		)[0]
										_UpperCAmelCase    :						Dict							=      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    :						str							=      output_from_no_past[:, -3:, random_slice_idx]
										_UpperCAmelCase    :						Any							=      output_from_past[:, :, random_slice_idx]

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







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					List[str]=None					, lowerCAmelCase:					Optional[int]=None					, lowerCAmelCase:					Union[str, Any]=None					, lowerCAmelCase:					Optional[Any]=None					, lowerCAmelCase:					int=None					, )				->    List[Any]:





				if attention_mask is None:
								_UpperCAmelCase    :						List[str]							=      tf.cast(tf.math.not_equal(a__					, config.pad_token_id	)					, tf.inta	)
				if decoder_attention_mask is None:
								_UpperCAmelCase    :						Union[str, Any]							=      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    :						Dict							=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
				if decoder_head_mask is None:
								_UpperCAmelCase    :						Any							=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
				if cross_attn_head_mask is None:
								_UpperCAmelCase    :						Union[str, Any]							=      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 			a					(		_a							,	_a							,	unittest.TestCase			):






						"""simple docstring"""




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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      TFBlenderbotSmallModelTester(self		)
										_UpperCAmelCase    :						List[str]							=      ConfigTester(self  ,   config_class=__lowerCAmelCase		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase		)







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






						"""simple docstring"""




						_lowercase        =     [
						    """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?"""
						]
						_lowercase        =     """facebook/blenderbot_small-90M"""


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
										return model





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.tokenizer(self.src_text  ,   return_tensors="tf"		)
										_UpperCAmelCase    :						Optional[int]							=      self.model.generate(
										    model_inputs.input_ids  ,   attention_mask=model_inputs.attention_mask  ,   num_beams=2  ,   use_cache=__lowerCAmelCase  ,   )
										_UpperCAmelCase    :						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.",
										)





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


