from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class 					snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Any											=      BlenderbotConfig
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      {}
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      """gelu"""





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

							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    max_position_embeddings
							a					=    eos_token_id
							a					=    pad_token_id
							a					=    bos_token_id





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

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

							a					=    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      ,   )
							a					=    prepare_blenderbot_inputs_dict(__a      ,   __a      ,   __a							)
							return config, inputs_dict





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Dict:
							a					=    TFBlenderbotModel(config=__a							).get_decoder()
							a					=    inputs_dict["input_ids"]

							a					=    input_ids[:1, :]
							a					=    inputs_dict["attention_mask"][:1, :]
							a					=    inputs_dict["head_mask"]
							a					=    1

							# first forward pass
							a					=    model(__a      ,   attention_mask=__a      ,   head_mask=__a      ,   use_cache=__a							)

							a					,	a					=    outputs.to_tuple()

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

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

							a					=    model(__a      ,   attention_mask=__a							)[0]
							a					=    model(__a      ,   attention_mask=__a      ,   past_key_values=__a							)[0]

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

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

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




def     __magic_name__       (				A	:      Any,						A	:      List[Any],						A	:      Dict,						A	:      int=None,						A	:      str=None,						A	:      int=None,						A	:      Union[str, Any]=None,						A	:      Optional[int]=None,						):






			'''simple docstring'''







			if attention_mask is None:
						a					=    tf.cast(tf.math.not_equal(__snake_case,						config.pad_token_id     ),						tf.inta     )
			if decoder_attention_mask is None:
						a					=    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:
						a					=    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						a					=    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						a					=    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__							(_UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_ :							Tuple											=      (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_ :							str											=      (
				    {
				        """conversational""": TFBlenderbotForConditionalGeneration,
				        """feature-extraction""": TFBlenderbotModel,
				        """summarization""": TFBlenderbotForConditionalGeneration,
				        """text2text-generation""": TFBlenderbotForConditionalGeneration,
				        """translation""": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_ :							List[str]											=      True
				SCREAMING_SNAKE_CASE_ :							Tuple											=      False
				SCREAMING_SNAKE_CASE_ :							Any											=      False





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[int]:
							a					=    TFBlenderbotModelTester(self							)
							a					=    ConfigTester(self      ,   config_class=__a							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  int:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Tuple:
							a					=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__a							)




@require_tokenizers
@require_tf
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Dict											=      ["""My friends are cool but they eat too many carbs."""]
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      """facebook/blenderbot-400M-distill"""





				@cached_property
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							return BlenderbotTokenizer.from_pretrained(self.model_name							)





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





				@slow
				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[Any]:
							a					=    self.tokenizer(self.src_text      ,   return_tensors="tf"							)
							a					=    self.model.generate(
							    model_inputs.input_ids      ,   )
							a					=    self.tokenizer.batch_decode(generated_ids.numpy()      ,   skip_special_tokens=__a							)[0]
							assert (
							    generated_words
							    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
							)












import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCAmelCase	:       List[Any]     =	logging.getLogger(__name__)




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(
			    description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."     )
			parser.add_argument(
			    "--dataset_name",						type=A,						default="wikitext",						help="Name of the training. Explore datasets at: hf.co/datasets.",						)
			parser.add_argument(
			    "--dataset_config",						type=A,						default="wikitext-103-raw-v1",						help="Configuration name of the dataset."     )
			parser.add_argument(
			    "--tokenizer_name_or_path",						type=A,						default="sayakpaul/unigram-tokenizer-wikitext",						help="Tokenizer identifier. Can be a local filepath or a Hub identifier.",						)
			parser.add_argument(
			    "--shard_size",						type=A,						default=1000,						help="Number of entries to go in a single shard.",						)
			parser.add_argument("--split",						type=A,						default="train",						choices=["train", "test", "validation"]     )
			parser.add_argument(
			    "--limit",						default=A,						type=A,						help="Limit the number of shards (used for debugging).",						)
			parser.add_argument(
			    "--max_length",						type=A,						default=512,						help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
			    " sequence length that is a multiple of 8.",						)
			parser.add_argument(
			    "--output_dir",						default="tf-tpu",						type=A,						help="Output directory where the TFRecord shards will be saved. If the"
			    " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
			    " shards will be directly saved to a Google Cloud Storage bucket.",						)

			a					=    parser.parse_args()
			return args




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			def fn(A	:      Tuple     ):
						return tokenizer(examples["text"]     )

			return fn




def     __magic_name__       (				A	:      Any     ):






			'''simple docstring'''







			a					=    []
			for i in range(len(tokenized_data["input_ids"]     )     ):
						a					=    {
						    "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]     )     ),
						    "attention_mask": tf.train.Feature(
						        intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]     )     ),
						}
						a					=    tf.train.Features(feature=A     )
						a					=    tf.train.Example(features=A     )
						a					=    example.SerializeToString()
						records.append(A     )
			return records




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    datasets.load_dataset(args.dataset_name,						args.dataset_config,						split=args.split     )

			if args.limit is not None:
						a					=    min(len(A     ),						args.limit     )
						a					=    dataset.select(range(A     )     )
						print(F"""Limiting the dataset to {args.limit} entries."""     )

			a					=    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path     )

			# Handle output directory creation.
			# For serializing into a Google Cloud Storage Bucket, one needs to first
			# create a bucket.
			if "gs" not in args.output_dir:
						if not os.path.exists(args.output_dir     ):
									os.makedirs(args.output_dir     )
						a					=    os.path.join(args.output_dir,						args.split     )
						if not os.path.exists(A     ):
									os.makedirs(A     )
			else:
						a					=    os.path.join(args.output_dir,						args.split     )

			# Tokenize the whole dataset at once.
			a					=    tokenize_function(A     )
			a					=    dataset.map(A,						batched=A,						num_proc=4,						remove_columns=["text"]     )

			# We need to concatenate all our texts together, and then split the result
			# into chunks of a fixed size, which we will call block_size. To do this, we
			# will use the map method again, with the option batched=True. When we use batched=True,
			# the function we pass to map() will be passed multiple inputs at once, allowing us
			# to group them into more or fewer examples than we had in the input.
			# This allows us to create our new fixed-length samples. The advantage of this
			# method is that we don't lose a whole lot of content from the dataset compared to the
			# case where we simply tokenize with a pre-defined max_length.

			def group_texts(A	:      List[Any]     ):
						# Concatenate all texts.
						a					=    {k: sum(examples[k],						[]     ) for k in examples.keys()}
						a					=    len(concatenated_examples[list(examples.keys()     )[0]]     )
						# We drop the small remainder, though you could add padding instead if the model supports it
						# In this, as in all things, we advise you to follow your heart 🫀
						a					=    (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						a					=    {
						    k: [t[i : i + args.max_length] for i in range(0,						A,						args.max_length     )]
						    for k, t in concatenated_examples.items()
						}
						return result

			a					=    dataset_tokenized.map(A,						batched=A,						batch_size=1000,						num_proc=4     )

			a					=    0
			a					=    0
			for shard in range(0,						len(A     ),						args.shard_size     ):
						a					=    grouped_dataset[shard : shard + args.shard_size]
						a					=    len(dataset_snapshot["input_ids"]     )
						a					=    os.path.join(A,						F"""dataset-{shard_count}-{records_containing}.tfrecord"""     )
						a					=    get_serialized_examples(A     )

						with tf.io.TFRecordWriter(A     ) as out_file:
									for i in range(len(A     )     ):
												a					=    serialized_examples[i]
												out_file.write(A     )
									print("Wrote file {} containing {} records".format(A,						A     )     )

						shard_count += 1
						total_records += records_containing

			with open(F"""split-{args.split}-records-count.txt""",						"w"     ) as f:
						print(F"""Total {args.split} records: {total_records}""",						file=A     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Optional[int]     =	parse_args()
		main(args)






