




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			:





				'''simple docstring'''



				__a							: Dict										=BlenderbotConfig
				__a							: Dict										={}
				__a							: Dict										="""gelu"""



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=7       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=99       , UpperCAmelCase_=32       , UpperCAmelCase_=2       , UpperCAmelCase_=4       , UpperCAmelCase_=37       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=2       , UpperCAmelCase_=1       , UpperCAmelCase_=0       , ):
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		seq_length
											lowerCAmelCase   =		is_training
											lowerCAmelCase   =		use_labels
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		hidden_size
											lowerCAmelCase   =		num_hidden_layers
											lowerCAmelCase   =		num_attention_heads
											lowerCAmelCase   =		intermediate_size

											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		max_position_embeddings
											lowerCAmelCase   =		eos_token_id
											lowerCAmelCase   =		pad_token_id
											lowerCAmelCase   =		bos_token_id



				def       __snake_case		( self						):
											lowerCAmelCase   =		ids_tensor([self.batch_size, self.seq_length - 1]       , self.vocab_size						)
											lowerCAmelCase   =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       , 1						)
											lowerCAmelCase   =		tf.concat([input_ids, eos_tensor]       , axis=1						)

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

											lowerCAmelCase   =		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   =		prepare_blenderbot_inputs_dict(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)
											return config, inputs_dict






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		TFBlenderbotModel(config=UpperCAmelCase_						).get_decoder()
											lowerCAmelCase   =		inputs_dict['''input_ids''']

											lowerCAmelCase   =		input_ids[:1, :]
											lowerCAmelCase   =		inputs_dict['''attention_mask'''][:1, :]
											lowerCAmelCase   =		inputs_dict['''head_mask''']
											lowerCAmelCase   =		1

											# first forward pass
											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , head_mask=UpperCAmelCase_       , use_cache=UpperCAmelCase_						)

											lowerCAmelCase   ,     lowerCAmelCase   =		outputs.to_tuple()

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

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

											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_						)[0]
											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , past_key_values=UpperCAmelCase_						)[0]

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

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

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




def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, ):
							if attention_mask is None:
														lowerCAmelCase   =		tf.cast(tf.math.not_equal(_snake_case						, config.pad_token_id							)						, tf.inta							)
							if decoder_attention_mask is None:
														lowerCAmelCase   =		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   =		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCAmelCase   =		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCAmelCase   =		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			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



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



				def       __snake_case		( self						):
											lowerCAmelCase   =		TFBlenderbotModelTester(self						)
											lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_						)



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






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_						)




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





				'''simple docstring'''



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



				@cached_property
				def       __snake_case		( self						):
											return BlenderbotTokenizer.from_pretrained(self.model_name						)



				@cached_property
				def       __snake_case		( self						):
											lowerCAmelCase   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											return model






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









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



