



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	List[Any]    =ProphetNetTokenizer
							__lowerCamelCase	:	Dict    =False





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											super().setUp()

											__a       =		[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											__a       =		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 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		"""UNwant\u00E9d,running"""
											__a       =		"""unwanted, running"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.tokenizer_class(self.vocab_file	)

											__a       =		tokenizer.tokenize("""UNwant\u00E9d,running"""	)
											self.assertListEqual(__lowercase      ,				["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				[9, 6, 7, 12, 10, 11]	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""	)      ,				["""ah""", """\u535A""", """\u63A8""", """zz"""]	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """	)      ,				["""hello""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hällo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""h\u00E9llo"""]	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				never_split=["""[UNK]"""]	)

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

											__a       =		{}
											for i, token in enumerate(__lowercase	):
															__a       =		i
											__a       =		WordpieceTokenizer(vocab=__lowercase      ,				unk_token="""[UNK]"""	)

											self.assertListEqual(tokenizer.tokenize(""""""	)      ,				[]	)

											self.assertListEqual(tokenizer.tokenize("""unwanted running"""	)      ,				["""un""", """##want""", """##ed""", """runn""", """##ing"""]	)

											self.assertListEqual(tokenizer.tokenize("""unwantedX running"""	)      ,				["""[UNK]""", """runn""", """##ing"""]	)





							@require_torch
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

											__a       =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											__a       =		[1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
											__a       =		tokenizer(__lowercase      ,				padding=__lowercase      ,				return_tensors="""pt"""	)
											self.assertIsInstance(__lowercase      ,				__lowercase	)
											__a       =		list(batch.input_ids.numpy()[0]	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											self.assertEqual((2, 9)      ,				batch.input_ids.shape	)
											self.assertEqual((2, 9)      ,				batch.attention_mask.shape	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											self.assertTrue(_is_whitespace(""" """	)	)
											self.assertTrue(_is_whitespace("""\t"""	)	)
											self.assertTrue(_is_whitespace("""\r"""	)	)
											self.assertTrue(_is_whitespace("""\n"""	)	)
											self.assertTrue(_is_whitespace("""\u00A0"""	)	)

											self.assertFalse(_is_whitespace("""A"""	)	)
											self.assertFalse(_is_whitespace("""-"""	)	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											self.assertTrue(_is_control("""\u0005"""	)	)

											self.assertFalse(_is_control("""A"""	)	)
											self.assertFalse(_is_control(""" """	)	)
											self.assertFalse(_is_control("""\t"""	)	)
											self.assertFalse(_is_control("""\r"""	)	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											self.assertTrue(_is_punctuation("""-"""	)	)
											self.assertTrue(_is_punctuation("""$"""	)	)
											self.assertTrue(_is_punctuation("""`"""	)	)
											self.assertTrue(_is_punctuation("""."""	)	)

											self.assertFalse(_is_punctuation("""A"""	)	)
											self.assertFalse(_is_punctuation(""" """	)	)







							@slow
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

											__a       =		tokenizer.encode("""sequence builders"""      ,				add_special_tokens=__lowercase	)
											__a       =		tokenizer.encode("""multi-sequence build"""      ,				add_special_tokens=__lowercase	)

											__a       =		tokenizer.build_inputs_with_special_tokens(__lowercase	)
											__a       =		tokenizer.build_inputs_with_special_tokens(__lowercase      ,				__lowercase	)

											assert encoded_sentence == text + [102]
											assert encoded_pair == text + [102] + text_a + [102]





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
