





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   UpperCamelCase_       :
							lowerCamelCase_           =					BlenderbotSmallConfig
							lowerCamelCase_           =					{}
							lowerCamelCase_           =					"gelu"


							def __init__(							self    :Optional[Any]							,				__A    :Optional[int]							,				__A    :Union[str, Any]=13							,				__A    :Tuple=7							,				__A    :Optional[int]=True							,				__A    :int=False							,				__A    :Optional[int]=99							,				__A    :List[Any]=32							,				__A    :Dict=2							,				__A    :List[str]=4							,				__A    :List[Any]=37							,				__A    :List[Any]=0.1							,				__A    :Union[str, Any]=0.1							,				__A    :Union[str, Any]=20							,				__A    :Optional[int]=2							,				__A    :Dict=1							,				__A    :Optional[Any]=0							,				)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      parent
									SCREAMING_SNAKE_CASE__			=      batch_size
									SCREAMING_SNAKE_CASE__			=      seq_length
									SCREAMING_SNAKE_CASE__			=      is_training
									SCREAMING_SNAKE_CASE__			=      use_labels
									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      intermediate_size

									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      max_position_embeddings
									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      pad_token_id
									SCREAMING_SNAKE_CASE__			=      bos_token_id


							def      _snake_case  (							self    :Optional[int]		)   ->   Any:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__			=      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							,				)
									SCREAMING_SNAKE_CASE__			=      prepare_blenderbot_small_inputs_dict(__A							,				__A							,				__A		)
									return config, inputs_dict


							def      _snake_case  (							self    :Dict							,				__A    :int							,				__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFBlenderbotSmallModel(config=__A		).get_decoder()
									SCREAMING_SNAKE_CASE__			=      inputs_dict["""input_ids"""]

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

									# first forward pass
									SCREAMING_SNAKE_CASE__			=      model(__A							,				attention_mask=__A							,				head_mask=__A							,				use_cache=__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      outputs.to_tuple()

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

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

									SCREAMING_SNAKE_CASE__			=      model(__A							,				attention_mask=__A		)[0]
									SCREAMING_SNAKE_CASE__			=      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
									SCREAMING_SNAKE_CASE__			=      int(ids_tensor((1,)							,				output_from_past.shape[-1]		)		)
									SCREAMING_SNAKE_CASE__			=      output_from_no_past[:, -3:, random_slice_idx]
									SCREAMING_SNAKE_CASE__			=      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__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Union[str, Any]=None					,   UpperCamelCase__:							int=None					,   UpperCamelCase__:							Union[str, Any]=None					,   UpperCamelCase__:							List[str]=None					,   UpperCamelCase__:							Optional[int]=None					,   ):
		if attention_mask is None:
				SCREAMING_SNAKE_CASE__			=      tf.cast(tf.math.not_equal(UpperCamelCase__					,   config.pad_token_id    )					,   tf.inta    )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE__			=      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:
				SCREAMING_SNAKE_CASE__			=      tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE__			=      tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE__			=      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    ):
							lowerCamelCase_           =					(
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							lowerCamelCase_           =					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							lowerCamelCase_           =					(
							    {
							        "conversational": TFBlenderbotSmallForConditionalGeneration,
							        "feature-extraction": TFBlenderbotSmallModel,
							        "summarization": TFBlenderbotSmallForConditionalGeneration,
							        "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
							        "translation": TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase_           =					True
							lowerCamelCase_           =					False
							lowerCamelCase_           =					False


							def      _snake_case  (							self    :List[str]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFBlenderbotSmallModelTester(self		)
									SCREAMING_SNAKE_CASE__			=      ConfigTester(self							,				config_class=__A		)


							def      _snake_case  (							self    :Optional[Any]		)   ->   int:



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def      _snake_case  (							self    :Dict		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      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    ):
							lowerCamelCase_           =					[
							    "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?"
							]
							lowerCamelCase_           =					"facebook/blenderbot_small-90M"


							@cached_property
							def      _snake_case  (							self    :List[Any]		)   ->   Dict:



									"""simple docstring"""



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


							@cached_property
							def      _snake_case  (							self    :Optional[int]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
									return model


							@slow
							def      _snake_case  (							self    :List[Any]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer(self.src_text							,				return_tensors="""tf"""		)
									SCREAMING_SNAKE_CASE__			=      self.model.generate(
									    model_inputs.input_ids							,				attention_mask=model_inputs.attention_mask							,				num_beams=2							,				use_cache=__A							,				)
									SCREAMING_SNAKE_CASE__			=      self.tokenizer.batch_decode(generated_ids.numpy()							,				skip_special_tokens=__A		)[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.",
									)






from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		return {key.lstrip("""-"""    ): value for key, value in zip(unknown_args[::2]					,   unknown_args[1::2]    )}






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      ArgumentParser(
		    """HuggingFace Datasets CLI tool"""					,   usage="""datasets-cli <command> [<args>]"""					,   allow_abbrev=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      parser.add_subparsers(help="""datasets-cli command helpers"""    )
		set_verbosity_info()

		# Register commands
		ConvertCommand.register_subcommand(UpperCamelCase__    )
		EnvironmentCommand.register_subcommand(UpperCamelCase__    )
		TestCommand.register_subcommand(UpperCamelCase__    )
		RunBeamCommand.register_subcommand(UpperCamelCase__    )
		DummyDataCommand.register_subcommand(UpperCamelCase__    )

		# Parse args
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      parser.parse_known_args()
		if not hasattr(UpperCamelCase__					,   """func"""    ):
				parser.print_help()
				exit(1    )
		SCREAMING_SNAKE_CASE__			=      parse_unknown_args(UpperCamelCase__    )

		# Run
		SCREAMING_SNAKE_CASE__			=      args.func(UpperCamelCase__					,   **UpperCamelCase__    )
		service.run()


if __name__ == "__main__":
	main()