

import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def 						UpperCamelCase (lowercase_:			Union[dict, list, tuple, torch.Tensor] )					->				Any:
	A__							:   List[Any]					       =		[]
	if isinstance(UpperCamelCase__   ,				UpperCamelCase__ ):
		for v in tree.values():
			shapes.extend(_fetch_dims(UpperCamelCase__ ) )
	elif isinstance(UpperCamelCase__   ,				(list, tuple) ):
		for t in tree:
			shapes.extend(_fetch_dims(UpperCamelCase__ ) )
	elif isinstance(UpperCamelCase__   ,				torch.Tensor ):
		shapes.append(tree.shape )
	else:
		raise ValueError("""Not supported""" )

	return shapes

@torch.jit.ignore
def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Tuple[int, ...] )					->				Dict:
	A__							:   int					       =		[]
	for d in reversed(UpperCamelCase__ ):
		idx.append(flat_idx % d )
		A__							:   str					       =		flat_idx // d

	return tuple(reversed(UpperCamelCase__ ) )

@torch.jit.ignore
def 						UpperCamelCase (lowercase_:			Sequence[int]   ,				lowercase_:			Sequence[int]   ,				lowercase_:			Sequence[int]   ,				lowercase_:			Optional[Sequence[bool]] = None   ,				lowercase_:			Optional[Sequence[bool]] = None   ,				)					->				Optional[Any]:

	# start_edges and end_edges both indicate whether, starting from any given
	# dimension, the start/end index is at the top/bottom edge of the
	# corresponding tensor, modeled as a tree
	def reduce_edge_list(lowercase_:			List[bool] ) -> None:
		A__							:   List[str]					       =		True
		for i in range(len(UpperCamelCase__ ) ):
			A__							:   Dict					       =		-1 * (i + 1)
			l[reversed_idx] &= tally
			A__							:   List[str]					       =		l[reversed_idx]

	if start_edges is None:
		A__							:   Optional[int]					       =		[s == 0 for s in start]
		reduce_edge_list(UpperCamelCase__ )
	if end_edges is None:
		A__							:   int					       =		[e == (d - 1) for e, d in zip(UpperCamelCase__   ,				UpperCamelCase__ )]
		reduce_edge_list(UpperCamelCase__ )

	# Base cases. Either start/end are empty and we're done, or the final,
	# one-dimensional tensor can be simply sliced
	if len(UpperCamelCase__ ) == 0:
		return [()]
	elif len(UpperCamelCase__ ) == 1:
		return [(slice(start[0]   ,				end[0] + 1 ),)]

	A__							:   List[Tuple[slice, ...]]					       =		[]
	A__							:   List[slice]					       =		[]

	# Dimensions common to start and end can be selected directly
	for s, e in zip(UpperCamelCase__   ,				UpperCamelCase__ ):
		if s == e:
			path_list.append(slice(UpperCamelCase__   ,				s + 1 ) )
		else:
			break

	A__							:   Tuple[slice, ...]					       =		tuple(UpperCamelCase__ )
	A__							:   int					       =		len(UpperCamelCase__ )

	# start == end, and we're done
	if divergence_idx == len(UpperCamelCase__ ):
		return [path]

	def upper() -> Tuple[Tuple[slice, ...], ...]:
		assert start_edges is not None
		assert end_edges is not None

		A__							:   List[str]					       =		start[divergence_idx]
		return tuple(
		    path + (slice(UpperCamelCase__   ,				sdi + 1 ),) + s
		    for s in _get_minimal_slice_set(
		        start[divergence_idx + 1 :]   ,				[d - 1 for d in dims[divergence_idx + 1 :]]   ,				dims[divergence_idx + 1 :]   ,				start_edges=start_edges[divergence_idx + 1 :]   ,				end_edges=[True for _ in end_edges[divergence_idx + 1 :]]   ,				) )

	def lower() -> Tuple[Tuple[slice, ...], ...]:
		assert start_edges is not None
		assert end_edges is not None

		A__							:   Dict					       =		end[divergence_idx]
		return tuple(
		    path + (slice(UpperCamelCase__   ,				edi + 1 ),) + s
		    for s in _get_minimal_slice_set(
		        [0 for _ in start[divergence_idx + 1 :]]   ,				end[divergence_idx + 1 :]   ,				dims[divergence_idx + 1 :]   ,				start_edges=[True for _ in start_edges[divergence_idx + 1 :]]   ,				end_edges=end_edges[divergence_idx + 1 :]   ,				) )

	# If both start and end are at the edges of the subtree rooted at
	# divergence_idx, we can just select the whole subtree at once
	if start_edges[divergence_idx] and end_edges[divergence_idx]:
		slices.append(path + (slice(start[divergence_idx]   ,				end[divergence_idx] + 1 ),) )
	# If just start is at the edge, we can grab almost all of the subtree,
	# treating only the ragged bottom edge as an edge case
	elif start_edges[divergence_idx]:
		slices.append(path + (slice(start[divergence_idx]   ,				end[divergence_idx] ),) )
		slices.extend(lower() )
	# Analogous to the previous case, but the top is ragged this time
	elif end_edges[divergence_idx]:
		slices.extend(upper() )
		slices.append(path + (slice(start[divergence_idx] + 1   ,				end[divergence_idx] + 1 ),) )
	# If both sides of the range are ragged, we need to handle both sides
	# separately. If there's contiguous meat in between them, we can index it
	# in one big chunk
	else:
		slices.extend(upper() )
		A__							:   List[Any]					       =		end[divergence_idx] - start[divergence_idx]
		if middle_ground > 1:
			slices.append(path + (slice(start[divergence_idx] + 1   ,				end[divergence_idx] ),) )
		slices.extend(lower() )

	return slices

@torch.jit.ignore
def 						UpperCamelCase (lowercase_:			torch.Tensor   ,				lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			int )					->				Union[str, Any]:
	A__							:   Dict					       =		t.shape[:no_batch_dims]
	A__							:   Tuple					       =		list(_flat_idx_to_idx(UpperCamelCase__   ,				UpperCamelCase__ ) )
	# _get_minimal_slice_set is inclusive
	A__							:   Any					       =		list(_flat_idx_to_idx(flat_end - 1   ,				UpperCamelCase__ ) )

	# Get an ordered list of slices to perform
	A__							:   Union[str, Any]					       =		_get_minimal_slice_set(
	    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				)

	A__							:   Union[str, Any]					       =		[t[s] for s in slices]

	return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )

def 						UpperCamelCase (lowercase_:			Callable   ,				lowercase_:			Dict[str, Any]   ,				lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			bool = False   ,				lowercase_:			Any = None   ,				lowercase_:			bool = False   ,				)					->				str:
	if not (len(UpperCamelCase__ ) > 0):
		raise ValueError("""Must provide at least one input""" )

	A__							:   Optional[Any]					       =		[shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__ )]
	A__							:   Tuple					       =		tuple([max(UpperCamelCase__ ) for s in zip(*UpperCamelCase__ )] )

	def _prep_inputs(lowercase_:			torch.Tensor ) -> torch.Tensor:
		if not low_mem:
			if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
				A__							:   Dict					       =		t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
			A__							:   List[Any]					       =		t.reshape(-1   ,				*t.shape[no_batch_dims:] )
		else:
			A__							:   Tuple					       =		t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
		return t

	A__							:   Dict[str, Any]					       =		tensor_tree_map(_prep_inputs   ,				UpperCamelCase__ )
	A__							:   List[str]					       =		None
	if _out is not None:
		A__							:   str					       =		tensor_tree_map(lambda lowercase_ : t.view([-1] + list(t.shape[no_batch_dims:] ) )   ,				_out )

	A__							:   List[str]					       =		1
	for d in orig_batch_dims:
		flat_batch_dim *= d

	A__							:   Tuple					       =		flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

	def _select_chunk(lowercase_:			torch.Tensor ) -> torch.Tensor:
		return t[i : i + chunk_size] if t.shape[0] != 1 else t

	A__							:   List[str]					       =		0
	A__							:   int					       =		prepped_outputs
	for _ in range(UpperCamelCase__ ):
		# Chunk the input
		if not low_mem:
			A__							:   List[Any]					       =		_select_chunk
		else:
			A__							:   int					       =		partial(
			    _chunk_slice   ,				flat_start=UpperCamelCase__   ,				flat_end=min(UpperCamelCase__   ,				i + chunk_size )   ,				no_batch_dims=len(UpperCamelCase__ )   ,				)

		A__							:   Dict[str, Any]					       =		tensor_tree_map(UpperCamelCase__   ,				UpperCamelCase__ )

		# Run the layer on the chunk
		A__							:   Any					       =		layer(**UpperCamelCase__ )

		# Allocate space for the output
		if out is None:
			A__							:   Union[str, Any]					       =		tensor_tree_map(lambda lowercase_ : t.new_zeros((flat_batch_dim,) + t.shape[1:] )   ,				UpperCamelCase__ )

		# Put the chunk in its pre-allocated space
		if isinstance(UpperCamelCase__   ,				UpperCamelCase__ ):

			def assign(lowercase_:			dict   ,				lowercase_:			dict ) -> None:
				for k, v in da.items():
					if isinstance(UpperCamelCase__   ,				UpperCamelCase__ ):
						assign(UpperCamelCase__   ,				da[k] )
					else:
						if _add_into_out:
							v[i : i + chunk_size] += da[k]
						else:
							A__							:   Tuple					       =		da[k]

			assign(UpperCamelCase__   ,				UpperCamelCase__ )
		elif isinstance(UpperCamelCase__   ,				UpperCamelCase__ ):
			for xa, xa in zip(UpperCamelCase__   ,				UpperCamelCase__ ):
				if _add_into_out:
					xa[i : i + chunk_size] += xa
				else:
					A__							:   Dict					       =		xa
		elif isinstance(UpperCamelCase__   ,				torch.Tensor ):
			if _add_into_out:
				out[i : i + chunk_size] += output_chunk
			else:
				A__							:   Optional[int]					       =		output_chunk
		else:
			raise ValueError("""Not supported""" )

		i += chunk_size

	A__							:   List[Any]					       =		tensor_tree_map(lambda lowercase_ : t.view(orig_batch_dims + t.shape[1:] )   ,				UpperCamelCase__ )

	return out



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__ = 512   ,	):
						A__							:   Union[str, Any]					       =		max_chunk_size
						A__							:   Optional[int]					       =		None
						A__							:   Optional[tuple]					       =		None






					def   __A      (			self   ,	A__   ,	A__   ,	A__      ):
						logging.info("""Tuning chunk size..."""      )

						if min_chunk_size >= self.max_chunk_size:
							return min_chunk_size

						A__							:   List[int]					       =		[2**l for l in range(int(math.log(self.max_chunk_size   ,	2      )      ) + 1      )]
						A__							:   List[str]					       =		[c for c in candidates if c > min_chunk_size]
						A__							:   Union[str, Any]					       =		[min_chunk_size] + candidates
						candidates[-1] += 4

						def test_chunk_size(A__      ) -> bool:
							try:
								with torch.no_grad():
									fn(*A__   ,	chunk_size=A__      )
								return True
							except RuntimeError:
								return False

						A__							:   List[Any]					       =		0
						A__							:   Any					       =		len(A__      ) - 1
						while i > min_viable_chunk_size_index:
							A__							:   Optional[int]					       =		test_chunk_size(candidates[i]      )
							if not viable:
								A__							:   Tuple					       =		(min_viable_chunk_size_index + i) // 2
							else:
								A__							:   Dict					       =		i
								A__							:   List[str]					       =		(i + len(A__      ) - 1) // 2

						return candidates[min_viable_chunk_size_index]






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Dict					       =		True
						for aa, aa in zip(A__   ,	A__      ):
							assert type(A__      ) == type(A__      )
							if isinstance(A__   ,	(list, tuple)      ):
								consistent &= self._compare_arg_caches(A__   ,	A__      )
							elif isinstance(A__   ,	A__      ):
								A__							:   Dict					       =		[v for _, v in sorted(aa.items()   ,	key=lambda A__      : x[0]      )]
								A__							:   str					       =		[v for _, v in sorted(aa.items()   ,	key=lambda A__      : x[0]      )]
								consistent &= self._compare_arg_caches(A__   ,	A__      )
							else:
								consistent &= aa == aa

						return consistent






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Dict					       =		True
						A__							:   tuple					       =		tree_map(lambda A__      : a.shape if isinstance(A__   ,	torch.Tensor      ) else a   ,	A__   ,	A__      )
						if self.cached_arg_data is not None:
							# If args have changed shape/value, we need to re-tune
							assert len(self.cached_arg_data      ) == len(A__      )
							A__							:   str					       =		self._compare_arg_caches(self.cached_arg_data   ,	A__      )
						else:
							# Otherwise, we can reuse the precomputed value
							A__							:   Optional[Any]					       =		False

						if not consistent:
							A__							:   Dict					       =		self._determine_favorable_chunk_size(
							    A__   ,	A__   ,	A__   ,	)
							A__							:   Tuple					       =		arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	):
						A__							:   Any					       =		parent
						A__							:   Any					       =		13
						A__							:   Optional[Any]					       =		7
						A__							:   Union[str, Any]					       =		30
						A__							:   str					       =		self.seq_length + self.mem_len
						A__							:   Dict					       =		15
						A__							:   int					       =		True
						A__							:   Tuple					       =		True
						A__							:   Union[str, Any]					       =		99
						A__							:   Optional[Any]					       =		[10, 50, 80]
						A__							:   str					       =		32
						A__							:   Tuple					       =		32
						A__							:   Union[str, Any]					       =		4
						A__							:   Optional[Any]					       =		8
						A__							:   int					       =		128
						A__							:   List[Any]					       =		2
						A__							:   List[str]					       =		2
						A__							:   int					       =		None
						A__							:   List[str]					       =		1
						A__							:   Union[str, Any]					       =		0
						A__							:   List[str]					       =		3
						A__							:   int					       =		self.vocab_size - 1
						A__							:   Optional[Any]					       =		0.0_1






					def   __A      (			self      ):
						A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Optional[Any]					       =		None
						if self.use_labels:
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Any					       =		TransfoXLConfig(
						    vocab_size=self.vocab_size   ,	mem_len=self.mem_len   ,	clamp_len=self.clamp_len   ,	cutoffs=self.cutoffs   ,	d_model=self.hidden_size   ,	d_embed=self.d_embed   ,	n_head=self.num_attention_heads   ,	d_head=self.d_head   ,	d_inner=self.d_inner   ,	div_val=self.div_val   ,	n_layer=self.num_hidden_layers   ,	eos_token_id=self.eos_token_id   ,	pad_token_id=self.vocab_size - 1   ,	init_range=self.init_range   ,	num_labels=self.num_labels   ,	)

						return (config, input_ids_a, input_ids_a, lm_labels)






					def   __A      (			self      ):
						random.seed(self.seed      )
						tf.random.set_seed(self.seed      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Dict					       =		TFTransfoXLModel(A__      )

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						A__							:   List[str]					       =		{"""input_ids""": input_ids_a, """mems""": mems_a}

						A__       ,       A__							:   str					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		TFTransfoXLLMHeadModel(A__      )

						A__       ,       A__							:   int					       =		model(A__      ).to_tuple()

						A__							:   int					       =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
						A__       ,       A__							:   Optional[Any]					       =		model(A__      ).to_tuple()

						A__       ,       A__							:   Union[str, Any]					       =		model([input_ids_a, mems_a]      ).to_tuple()

						A__							:   Any					       =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Any					       =		TFTransfoXLForSequenceClassification(A__      )
						A__							:   Optional[Any]					       =		model(A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.prepare_config_and_inputs()
						((A__)       ,       (A__)       ,       (A__)       ,       (A__))							:   List[Any]					       =		config_and_inputs
						A__							:   int					       =		{"""input_ids""": input_ids_a}
						return config, inputs_dict



@require_tf
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					UpperCAmelCase__:       Optional[Any]  							=	() if is_tf_available() else ()
					UpperCAmelCase__:       int  							=	(
					    {
					        '''feature-extraction''': TFTransfoXLModel,
					        '''text-classification''': TFTransfoXLForSequenceClassification,
					        '''text-generation''': TFTransfoXLLMHeadModel,
					        '''zero-shot''': TFTransfoXLForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Tuple  							=	False
					UpperCAmelCase__:       List[str]  							=	False






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
							# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
							# tokenizer.
							return True

						return False






					def   __A      (			self      ):
						A__							:   Tuple					       =		TFTransfoXLModelTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	d_embed=37      )






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






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_model(*A__      )






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   Any					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Union[str, Any]					       =		[TFTransfoXLForSequenceClassification]

						for model_class in self.all_model_classes:
							A__							:   Any					       =		model_class(A__      )
							assert isinstance(model.get_input_embeddings()   ,	tf.keras.layers.Layer      )
							if model_class in list_other_models_with_output_ebd:
								A__							:   Optional[Any]					       =		model.get_output_embeddings()
								assert isinstance(A__   ,	tf.keras.layers.Layer      )
								A__							:   Tuple					       =		model.get_bias()
								assert name is None
							else:
								A__							:   Dict					       =		model.get_output_embeddings()
								assert x is None
								A__							:   int					       =		model.get_bias()
								assert name is None






					def   __A      (			self      ):
						# TODO JP: Make TransfoXL XLA compliant
						pass






					@slow
					def   __A      (			self      ):
						for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   List[Any]					       =		TFTransfoXLModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""      )
					def   __A      (			self      ):
						pass



@require_tf
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@unittest.skip("""Skip test until #12651 is resolved."""      )
					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""      )
						# fmt: off
						A__							:   Tuple					       =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,	dtype=tf.intaa      )  # noqa: E231
						# fmt: on
						#  In 1991 , the remains of Russian Tsar Nicholas II and his family
						#  ( except for Alexei and Maria ) are discovered .
						#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
						#  remainder of the story . 1883 Western Siberia ,
						#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
						#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
						#  father initially slaps him for making such an accusation , Rasputin watches as the
						#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
						#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
						#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

						# fmt: off
						A__							:   Dict					       =		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
						# fmt: on
						#  In 1991, the remains of Russian Tsar Nicholas II and his family (
						#  except for Alexei and Maria ) are discovered. The voice of young son,
						#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
						#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
						#  and a group of men to perform magic. Rasputin has a vision and
						#  denounces one of the men as a horse thief. Although his father initially
						#  slaps him for making such an accusation, Rasputin watches as the man
						#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
						#  of the Virgin Mary, prompting him to become a priest.
						#  Rasputin quickly becomes famous, with people, even a bishop, begging for
						#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
						# Nicholas II and his family were discovered. The voice of <unk> young son,
						# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

						A__							:   Any					       =		model.generate(A__   ,	max_length=200   ,	do_sample=A__      )
						self.assertListEqual(output_ids[0].numpy().tolist()   ,	A__      )



