"""simple docstring"""







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 		UpperCamelCase      :
				UpperCAmelCase    :				Union[str, Any]   =		BlenderbotConfig
				UpperCAmelCase    :				List[Any]   =		{}
				UpperCAmelCase    :				int   =		"""gelu"""



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

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



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

					__snake_case      :							Dict						=			ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size)

					__snake_case      :							Optional[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     ,  )
					__snake_case      :							List[Any]						=			prepare_blenderbot_inputs_dict(_A     ,  _A     ,  _A)
					return config, inputs_dict






				def  _lowercase					(self    :				int     ,  _A    :				Optional[Any]     ,  _A    :				int)   ->     int:
					__snake_case      :							List[Any]						=			TFBlenderbotModel(config=_A).get_decoder()
					__snake_case      :							Tuple						=			inputs_dict['input_ids']

					__snake_case      :							List[Any]						=			input_ids[:1, :]
					__snake_case      :							int						=			inputs_dict['attention_mask'][:1, :]
					__snake_case      :							int						=			inputs_dict['head_mask']
					__snake_case      :							Optional[int]						=			1

					# first forward pass
					__snake_case      :							int						=			model(_A     ,  attention_mask=_A     ,  head_mask=_A     ,  use_cache=_A)

					__snake_case		,    __snake_case      :							Optional[Any]						=			outputs.to_tuple()

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

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

					__snake_case      :							str						=			model(_A     ,  attention_mask=_A)[0]
					__snake_case      :							List[Any]						=			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
					__snake_case      :							int						=			int(ids_tensor((1,)     ,  output_from_past.shape[-1]))
					__snake_case      :							Dict						=			output_from_no_past[:, -3:, random_slice_idx]
					__snake_case      :							Union[str, Any]						=			output_from_past[:, :, random_slice_idx]

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







def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Optional[int]=None      ,							UpperCAmelCase_   :    Any=None      ,							UpperCAmelCase_   :    int=None      ,							UpperCAmelCase_   :    Any=None      ,							UpperCAmelCase_   :    Dict=None      ,							)							->      str:

	'''simple docstring'''





	if attention_mask is None:
		__snake_case      :							Optional[int]						=			tf.cast(tf.math.not_equal(UpperCAmelCase_      ,							config.pad_token_id  )      ,							tf.inta  )
	if decoder_attention_mask is None:
		__snake_case      :							Dict						=			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:
		__snake_case      :							List[Any]						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
	if decoder_head_mask is None:
		__snake_case      :							Optional[Any]						=			tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
	if cross_attn_head_mask is None:
		__snake_case      :							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 		UpperCamelCase      (   _lowerCamelCase				,     _lowerCamelCase				,     unittest.TestCase						):
				UpperCAmelCase    :				List[Any]   =		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				UpperCAmelCase    :				Optional[Any]   =		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				UpperCAmelCase    :				Union[str, Any]   =		(
				    {
				        """conversational""": TFBlenderbotForConditionalGeneration,
				        """feature-extraction""": TFBlenderbotModel,
				        """summarization""": TFBlenderbotForConditionalGeneration,
				        """text2text-generation""": TFBlenderbotForConditionalGeneration,
				        """translation""": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase    :				Optional[int]   =		True
				UpperCAmelCase    :				Tuple   =		False
				UpperCAmelCase    :				str   =		False



				def  _lowercase					(self    :				Dict)   ->     Tuple:
					__snake_case      :							str						=			TFBlenderbotModelTester(self)
					__snake_case      :							List[Any]						=			ConfigTester(self     ,  config_class=_A)



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






				def  _lowercase					(self    :				str)   ->     List[str]:
					__snake_case      :							List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_A)







@require_tokenizers
@require_tf
class 		UpperCamelCase      (   unittest.TestCase						):
				UpperCAmelCase    :				Tuple   =		["""My friends are cool but they eat too many carbs."""]
				UpperCAmelCase    :				Optional[Any]   =		"""facebook/blenderbot-400M-distill"""



				@cached_property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					return BlenderbotTokenizer.from_pretrained(self.model_name)



				@cached_property
				def  _lowercase					(self    :				List[str])   ->     Optional[Any]:
					__snake_case      :							List[str]						=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
					return model






				@slow
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					__snake_case      :							Optional[int]						=			self.tokenizer(self.src_text     ,  return_tensors='tf')
					__snake_case      :							Union[str, Any]						=			self.model.generate(
					    model_inputs.input_ids     ,  )
					__snake_case      :							str						=			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?"
					)






"""simple docstring"""







import itertools
import string
from collections.abc import Generator, Iterable
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Iterable[str]      ,							UpperCAmelCase_   :    int  )							->      Generator[tuple[str, ...], None, None]:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			iter(UpperCAmelCase_  )
	while True:
		__snake_case      :							Optional[int]						=			tuple(itertools.islice(UpperCAmelCase_      ,							UpperCAmelCase_  )  )
		if not chunk:
			return
		yield chunk
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							Any						=			''.join([c.upper() for c in dirty if c in string.ascii_letters]  )
	__snake_case      :							Union[str, Any]						=			''

	if len(UpperCAmelCase_  ) < 2:
		return dirty

	for i in range(len(UpperCAmelCase_  ) - 1  ):
		clean += dirty[i]

		if dirty[i] == dirty[i + 1]:
			clean += "X"

	clean += dirty[-1]

	if len(UpperCAmelCase_  ) & 1:
		clean += "X"

	return clean
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      list[str]:

	'''simple docstring'''





	__snake_case      :							List[str]						=			'ABCDEFGHIKLMNOPQRSTUVWXYZ'
	# we're using a list instead of a '2d' array because it makes the math
	# for setting up the table and doing the actual encoding/decoding simpler
	__snake_case      :							Optional[int]						=			[]

	# copy key chars into the table if they are in `alphabet` ignoring duplicates
	for char in key.upper():
		if char not in table and char in alphabet:
			table.append(UpperCAmelCase_  )

    # fill the rest of the table in with the remaining alphabet chars
	for char in alphabet:
		if char not in table:
			table.append(UpperCAmelCase_  )

	return table
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							str						=			generate_table(UpperCAmelCase_  )
	__snake_case      :							Union[str, Any]						=			prepare_input(UpperCAmelCase_  )
	__snake_case      :							Tuple						=			''

	# https://en.wikipedia.org/wiki/Playfair_cipher#Description
	for chara, chara in chunker(UpperCAmelCase_      ,							2  ):
		__snake_case		,    __snake_case      :							Any						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )
		__snake_case		,    __snake_case      :							Tuple						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )

		if rowa == rowa:
			ciphertext += table[rowa * 5 + (cola + 1) % 5]
			ciphertext += table[rowa * 5 + (cola + 1) % 5]
		elif cola == cola:
			ciphertext += table[((rowa + 1) % 5) * 5 + cola]
			ciphertext += table[((rowa + 1) % 5) * 5 + cola]
		else:  # rectangle
			ciphertext += table[rowa * 5 + cola]
			ciphertext += table[rowa * 5 + cola]

	return ciphertext

def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			generate_table(UpperCAmelCase_  )
	__snake_case      :							Any						=			''

	# https://en.wikipedia.org/wiki/Playfair_cipher#Description
	for chara, chara in chunker(UpperCAmelCase_      ,							2  ):
		__snake_case		,    __snake_case      :							Union[str, Any]						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )
		__snake_case		,    __snake_case      :							Tuple						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )

		if rowa == rowa:
			plaintext += table[rowa * 5 + (cola - 1) % 5]
			plaintext += table[rowa * 5 + (cola - 1) % 5]
		elif cola == cola:
			plaintext += table[((rowa - 1) % 5) * 5 + cola]
			plaintext += table[((rowa - 1) % 5) * 5 + cola]
		else:  # rectangle
			plaintext += table[rowa * 5 + cola]
			plaintext += table[rowa * 5 + cola]

	return plaintext





