
'''simple docstring'''





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 						a   :
				_snake_case		    = BlenderbotSmallConfig
				_snake_case		    = {}
				_snake_case		    = '''gelu'''




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

										snake_case       :     Tuple =    hidden_dropout_prob
										snake_case       :     Tuple =    attention_probs_dropout_prob
										snake_case       :     Optional[int] =    max_position_embeddings
										snake_case       :     Dict =    eos_token_id
										snake_case       :     Any =    pad_token_id
										snake_case       :     Optional[Any] =    bos_token_id




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

										snake_case       :     List[Any] =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

										snake_case       :     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       :     str =    prepare_blenderbot_small_inputs_dict(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return config, inputs_dict




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Any =    TFBlenderbotSmallModel(config=SCREAMING_SNAKE_CASE_      ).get_decoder()
										snake_case       :     Dict =    inputs_dict['''input_ids''']

										snake_case       :     int =    input_ids[:1, :]
										snake_case       :     str =    inputs_dict['''attention_mask'''][:1, :]
										snake_case       :     int =    inputs_dict['''head_mask''']
										snake_case       :     Dict =    1

										# first forward pass
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    head_mask=SCREAMING_SNAKE_CASE_,    use_cache=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[int] =    outputs.to_tuple()

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

										# append to next input_ids and
										snake_case       :     List[Any] =    tf.concat([input_ids, next_tokens],    axis=-1      )
										snake_case       :     Optional[Any] =    tf.concat([attention_mask, next_attn_mask],    axis=-1      )

										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )[0]
										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    past_key_values=SCREAMING_SNAKE_CASE_      )[0]

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

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

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




def      A (       A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[int]=None    ,							A_							:					List[str]=None    ,							A_							:					int=None    ,							A_							:					Optional[Any]=None    ,							A_							:					Union[str, Any]=None    ,							):
						if attention_mask is None:
												snake_case       :     Optional[Any] =    tf.cast(tf.math.not_equal(A_    ,							config.pad_token_id       )    ,							tf.inta       )
						if decoder_attention_mask is None:
												snake_case       :     int =    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       :     str =    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
						if decoder_head_mask is None:
												snake_case       :     Union[str, Any] =    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
						if cross_attn_head_mask is None:
												snake_case       :     str =    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 						a   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				_snake_case		    = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				_snake_case		    = (
				    {
				        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotSmallModel,
				        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
				        '''translation''': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case		    = True
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Union[str, Any] =    TFBlenderbotSmallModelTester(self      )
										snake_case       :     int =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_      )




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




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     int =    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_      )




@require_tokenizers
@require_tf
class 						a   (				unittest.TestCase    ):
				_snake_case		    = [
				    '''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?'''
				]
				_snake_case		    = '''facebook/blenderbot_small-90M'''




				@cached_property
				def 	__snake_case							(							self	:				List[Any]      ):
										# use "old" tokenizer here because of bug when downloading new tokenizer
										return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )




				@cached_property
				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     List[str] =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
										return model




				@slow
				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Union[str, Any] =    self.tokenizer(self.src_text,    return_tensors='''tf'''      )
										snake_case       :     int =    self.model.generate(
										    model_inputs.input_ids,    attention_mask=model_inputs.attention_mask,    num_beams=2,    use_cache=SCREAMING_SNAKE_CASE_,    )
										snake_case       :     int =    self.tokenizer.batch_decode(generated_ids.numpy(),    skip_special_tokens=SCREAMING_SNAKE_CASE_      )[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.",
										)



'''simple docstring'''





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class 						a   (				__magic_name__    ):
				_snake_case		    = ''''''
				_snake_case		    = (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				_snake_case		    = None  # compression type in fsspec. ex: "gzip"
				_snake_case		    = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str = "",    SCREAMING_SNAKE_CASE_	:				Optional[str] = None,    SCREAMING_SNAKE_CASE_	:				Optional[dict] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										super().__init__(self,    **SCREAMING_SNAKE_CASE_      )
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										snake_case       :     List[Any] =    fsspec.open(
										    SCREAMING_SNAKE_CASE_,    mode='''rb''',    protocol=SCREAMING_SNAKE_CASE_,    compression=self.compression,    client_kwargs={
										        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
										        '''trust_env''': True,  # Enable reading proxy env variables.
										        **(target_options or {}).pop('''client_kwargs''',    {}      ),  # To avoid issues if it was already passed.
										    },    **(target_options or {}),    )
										snake_case       :     Tuple =    os.path.basename(self.file.path.split('''::'''      )[0]      )
										snake_case       :     Optional[Any] =    (
										    self.compressed_name[: self.compressed_name.rindex('''.'''      )]
										    if '''.''' in self.compressed_name
										    else self.compressed_name
										)
										snake_case       :     str =    None




				@classmethod
				def 	__snake_case							(							cls	:				Any,    SCREAMING_SNAKE_CASE_	:				int      ):
										# compressed file paths are always relative to the archive root
										return super()._strip_protocol(SCREAMING_SNAKE_CASE_      ).lstrip('''/'''      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										if self.dir_cache is None:
																snake_case       :     Optional[int] =    {**self.file.fs.info(self.file.path      ), '''name''': self.uncompressed_name}
																snake_case       :     Tuple =    {f['''name''']: f}




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.file.open().read()




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str = "rb",    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Any,    ):
										snake_case       :     int =    self._strip_protocol(SCREAMING_SNAKE_CASE_      )
										if mode != "rb":
																raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""      )
										return self.file.open()




class 						a   (				__magic_name__    ):
				_snake_case		    = '''bz2'''
				_snake_case		    = '''bz2'''
				_snake_case		    = '''.bz2'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''gzip'''
				_snake_case		    = '''gzip'''
				_snake_case		    = '''.gz'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''lz4'''
				_snake_case		    = '''lz4'''
				_snake_case		    = '''.lz4'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''xz'''
				_snake_case		    = '''xz'''
				_snake_case		    = '''.xz'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''zstd'''
				_snake_case		    = '''zstd'''
				_snake_case		    = '''.zst'''




				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str = "rb",    SCREAMING_SNAKE_CASE_	:				Optional[str] = None,    SCREAMING_SNAKE_CASE_	:				Optional[dict] = None,    SCREAMING_SNAKE_CASE_	:				int = DEFAULT_BLOCK_SIZE,    **SCREAMING_SNAKE_CASE_	:				Optional[Any],    ):
										super().__init__(
										    fo=SCREAMING_SNAKE_CASE_,    mode=SCREAMING_SNAKE_CASE_,    target_protocol=SCREAMING_SNAKE_CASE_,    target_options=SCREAMING_SNAKE_CASE_,    block_size=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
										#
										# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
										#     out.close = close
										# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
										#
										# see https://github.com/intake/filesystem_spec/issues/725
										snake_case       :     List[Any] =    self.file.__enter__

										class 						a   :




														def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Dict      ):
																				snake_case       :     Dict =    file_




														def __enter__(							self	:				Optional[Any]      ):
																				self._file.__enter__()
																				return self




														def __exit__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
																				self._file.__exit__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




														def __iter__(							self	:				Union[str, Any]      ):
																				return iter(self._file      )




														def 	__snake_case							(							self	:				Union[str, Any]      ):
																				return next(self._file      )




														def __getattr__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
																				return getattr(self._file,    SCREAMING_SNAKE_CASE_      )

										def fixed_enter(*SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				List[Any]      ):
																return WrappedFile(_enter(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )      )

										snake_case       :     Union[str, Any] =    fixed_enter

