



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			:
				UpperCamelCase__				:					List[str]     		=BlenderbotConfig
				UpperCamelCase__				:					Dict     		={}
				UpperCamelCase__				:					Optional[int]     		="""gelu"""




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

							lowercase__					  =				hidden_dropout_prob
							lowercase__					  =				attention_probs_dropout_prob
							lowercase__					  =				max_position_embeddings
							lowercase__					  =				eos_token_id
							lowercase__					  =				pad_token_id
							lowercase__					  =				bos_token_id




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

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

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




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Any,					__lowercase					:						int						):
							lowercase__					  =				TFBlenderbotModel(config=__a						).get_decoder()
							lowercase__					  =				inputs_dict["input_ids"]

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

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

							lowercase__  ,       lowercase__					  =				outputs.to_tuple()

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

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

							lowercase__					  =				model(__a,					attention_mask=__a						)[0]
							lowercase__					  =				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
							lowercase__					  =				int(ids_tensor((1,),					output_from_past.shape[-1]						)						)
							lowercase__					  =				output_from_no_past[:, -3:, random_slice_idx]
							lowercase__					  =				output_from_past[:, :, random_slice_idx]

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



def __SCREAMING_SNAKE_CASE (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  ):





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




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				TFBlenderbotModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__a						)




				def 							A__      (						self					:						Union[str, Any]						):
							self.config_tester.run_common_tests()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				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):
				UpperCamelCase__				:					int     		=["""My friends are cool but they eat too many carbs."""]
				UpperCamelCase__				:					Optional[int]     		="""facebook/blenderbot-400M-distill"""




				@cached_property
				def 							A__      (						self					:						List[Any]						):
							return BlenderbotTokenizer.from_pretrained(self.model_name						)




				@cached_property
				def 							A__      (						self					:						int						):
							lowercase__					  =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
							return model




				@slow
				def 							A__      (						self					:						str						):
							lowercase__					  =				self.tokenizer(self.src_text,					return_tensors="tf"						)
							lowercase__					  =				self.model.generate(
							    model_inputs.input_ids,					)
							lowercase__					  =				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 os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Dict     		=TransfoXLTokenizer
				UpperCamelCase__				:					List[Any]     		=False
				UpperCamelCase__				:					List[Any]     		=False




				def 							A__      (						self					:						Union[str, Any]						):
							super().setUp()

							lowercase__					  =				[
							    "<unk>",
							    "[CLS]",
							    "[SEP]",
							    "want",
							    "unwanted",
							    "wa",
							    "un",
							    "running",
							    ",",
							    "low",
							    "l",
							]
							lowercase__					  =				os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["vocab_file"]						)
							with open(self.vocab_file,					"w",					encoding="utf-8"						) as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)




				def 							A__      (						self					:						Union[str, Any],					**__lowercase					:						Any						):
							lowercase__					  =				True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname,					**__lowercase						)




				def 							A__      (						self					:						Tuple,					__lowercase					:						Optional[int]						):
							lowercase__					  =				"<unk> UNwanted , running"
							lowercase__					  =				"<unk> unwanted, running"
							return input_text, output_text




				def 							A__      (						self					:						str						):
							lowercase__					  =				TransfoXLTokenizer(vocab_file=self.vocab_file,					lower_case=__lowercase						)

							lowercase__					  =				tokenizer.tokenize("<unk> UNwanted , running"						)
							self.assertListEqual(__lowercase,					["<unk>", "unwanted", ",", "running"]						)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						),					[0, 4, 8, 7]						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "						),					["hello", "!", "how", "are", "you", "?"]						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "						),					["HeLLo", "!", "how", "Are", "yoU", "?"]						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)
							lowercase__					  =				"Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
							lowercase__					  =				[
							    "Hello",
							    "(",
							    "bracket",
							    ")",
							    "and",
							    "side",
							    "@-@",
							    "scrolled",
							    "[",
							    "and",
							    "]",
							    "Henry",
							    "'s",
							    "$",
							    "5",
							    "@,@",
							    "000",
							    "with",
							    "3",
							    "@.@",
							    "34",
							    "m",
							    ".",
							    "What",
							    "'s",
							    "up",
							    "!",
							    "?",
							]

							self.assertListEqual(tokenizer.tokenize(__lowercase						),					__lowercase						)

							self.assertEqual(tokenizer.convert_tokens_to_string(__lowercase						),					__lowercase						)




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.get_tokenizer()
							lowercase__					  =				len(__lowercase						)

							tokenizer.add_tokens(["new1", "new2"]						)
							tokenizer.move_added_token("new1",					1						)

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(__lowercase						),					original_len + 2						)
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode("new1"						),					[1]						)
							self.assertEqual(tokenizer.decode([1]						),					"new1"						)




